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Pedro Delicado

Personal Details

First Name:Pedro
Middle Name:
Last Name:Delicado
Suffix:
RePEc Short-ID:pde192
[This author has chosen not to make the email address public]
http://www-eio.upc.es/~delicado/

Affiliation

Universitat Politècnica de Catalunya

http://www.upc.edu
Spain, Barcelona

Research output

as
Jump to: Working papers Articles

Working papers

  1. Eva Boj & Pedro Delicado & Josep Fortiana & Anna Esteve & Adria Caballe, 2012. "Local Distance-Based Generalized Linear Models using the dbstats package for R," Working Papers XREAP2012-11, Xarxa de Referència en Economia Aplicada (XREAP), revised May 2012.
  2. Frederic Udina & Pedro Delicado, 2001. "Estimating parliamentary composition through electoral polls," Economics Working Papers 562, Department of Economics and Business, Universitat Pompeu Fabra.
  3. Estanislao Arana & Pedro Delicado & Luis Martí, 1999. "Validation procedures in radiological diagnostic models. Neural network and logistic regression," Economics Working Papers 414, Department of Economics and Business, Universitat Pompeu Fabra.
  4. Pedro Delicado & Manuel del Río, 1999. "A generalization of histogram type estimators," Economics Working Papers 422, Department of Economics and Business, Universitat Pompeu Fabra.
  5. Pedro Delicado, 1998. "Principal curves and principal oriented points," Economics Working Papers 309, Department of Economics and Business, Universitat Pompeu Fabra.
  6. Pedro Delicado & Juan Romo, 1998. "Constant coefficient tests for random coefficient regression," Economics Working Papers 329, Department of Economics and Business, Universitat Pompeu Fabra.
  7. Pedro Delicado, 1998. "Statistics in archaeology: New directions," Economics Working Papers 310, Department of Economics and Business, Universitat Pompeu Fabra.
  8. Pedro Delicado & Ana Justel, 1997. "Forecasting with missing data: Application to a real case," Economics Working Papers 213, Department of Economics and Business, Universitat Pompeu Fabra.
  9. Pedro Delicado & Iolanda Placencia, 1997. "Comparing and validating hypothesis test procedures: Graphical and numerical tools," Economics Working Papers 210, Department of Economics and Business, Universitat Pompeu Fabra.
  10. Pedro Delicado & Manuel del Rio, 1996. "Weighted Kernel regression," Economics Working Papers 164, Department of Economics and Business, Universitat Pompeu Fabra, revised Apr 1997.
  11. Delicado, Pedro, 1995. "Random coefficient regressions: parametric goodness of fit tests," DES - Working Papers. Statistics and Econometrics. WS 4199, Universidad Carlos III de Madrid. Departamento de Estadística.
  12. Delicado, Pedro, 1994. "Goodness of fit tests in random coefficient regression models," DES - Working Papers. Statistics and Econometrics. WS 3962, Universidad Carlos III de Madrid. Departamento de Estadística.
  13. Delicado, Pedro & Río, Manuel del, 1993. "Bootstraping the general linear hypothesis test," DES - Working Papers. Statistics and Econometrics. WS 3702, Universidad Carlos III de Madrid. Departamento de Estadística.

Articles

  1. Feliu Serra-Burriel & Pedro Delicado & Fernando M. Cucchietti, 2021. "Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis," Mathematics, MDPI, vol. 9(11), pages 1-22, June.
  2. Eduardo Doval & Pedro Delicado, 2020. "Identifying and Classifying Aberrant Response Patterns Through Functional Data Analysis," Journal of Educational and Behavioral Statistics, , vol. 45(6), pages 719-749, December.
  3. Pedro Delicado, 2019. "Comments on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 334-337, June.
  4. Pedro Delicado & Philippe Vieu, 2017. "Choosing the most relevant level sets for depicting a sample of densities," Computational Statistics, Springer, vol. 32(3), pages 1083-1113, September.
  5. Kehui Chen & Pedro Delicado & Hans-Georg Müller, 2017. "Modelling function-valued stochastic processes, with applications to fertility dynamics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 177-196, January.
  6. Eva Boj & Adrià Caballé & Pedro Delicado & Anna Esteve & Josep Fortiana, 2016. "Global and local distance-based generalized linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 170-195, March.
  7. Pedro Delicado, 2015. "Discussion of “Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan” by Piercesare Secchi, Simone Vantini and Valeria Vitelli," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 329-333, July.
  8. Delicado, Pedro & Vieu, Philippe, 2015. "Optimal level sets for bivariate density representation," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 1-18.
  9. R. Giraldo & P. Delicado & J. Mateu, 2012. "Hierarchical clustering of spatially correlated functional data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(4), pages 403-421, November.
  10. C. Comas & P. Delicado & J. Mateu, 2011. "A second order approach to analyse spatial point patterns with functional marks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 503-523, November.
  11. Delicado, P., 2011. "Dimensionality reduction when data are density functions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 401-420, January.
  12. Gonzalez, Juan R. & Peña, Edsel A. & Delicado, Pedro, 2010. "Confidence intervals for median survival time with recurrent event data," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 78-89, January.
  13. Boj, Eva & Delicado, Pedro & Fortiana, Josep, 2010. "Distance-based local linear regression for functional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 429-437, February.
  14. Delicado, P. & Goria, M.N., 2008. "A small sample comparison of maximum likelihood, moments and L-moments methods for the asymmetric exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1661-1673, January.
  15. Pedro Delicado, 2007. "Functional k-sample problem when data are density functions," Computational Statistics, Springer, vol. 22(3), pages 391-410, September.
  16. Pedro Delicado, 2006. "Local likelihood density estimation based on smooth truncation," Biometrika, Biometrika Trust, vol. 93(2), pages 472-480, June.
  17. Frederic Udina & Pedro Delicado, 2005. "Estimating Parliamentary composition through electoral polls," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 387-399, March.
  18. Pedro Delicado & Mario Huerta, 2003. "Principal Curves of Oriented Points: theoretical and computational improvements," Computational Statistics, Springer, vol. 18(2), pages 293-315, July.
  19. Castro-Rodriguez, Fidel & Da-Rocha, Jose Maria & Delicado, Pedro, 2002. "Desperately Seeking Theta's: Estimating the Distribution of Consumers under Increasing Block Rates," Journal of Regulatory Economics, Springer, vol. 22(1), pages 29-58, July.
  20. Delicado, Pedro, 2001. "Another Look at Principal Curves and Surfaces," Journal of Multivariate Analysis, Elsevier, vol. 77(1), pages 84-116, April.
  21. Pedro Delicado & Juan Romo, 1999. "Goodness of Fit Tests in Random Coefficient Regression Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(1), pages 125-148, March.
  22. Delicado, Pedro & del Rio, Manuel, 1994. "Bootstrapping the general linear hypothesis test," Computational Statistics & Data Analysis, Elsevier, vol. 18(3), pages 305-316, October.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Eva Boj & Pedro Delicado & Josep Fortiana & Anna Esteve & Adria Caballe, 2012. "Local Distance-Based Generalized Linear Models using the dbstats package for R," Working Papers XREAP2012-11, Xarxa de Referència en Economia Aplicada (XREAP), revised May 2012.

    Cited by:

    1. Catalina Bolancé & Zuhair Bahraoui & Ramon Alemany, 2015. "Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches," Working Papers XREAP2015-01, Xarxa de Referència en Economia Aplicada (XREAP), revised Jan 2015.
    2. Anna Castañer & Mª Mercè Claramunt, 2014. "Optimal stop-loss reinsurance: a dependence analysis," Working Papers XREAP2014-04, Xarxa de Referència en Economia Aplicada (XREAP), revised Apr 2014.
    3. Eva Boj & Teresa Costa & Josep Fortiana & Anna Esteve, 2015. "Assessing the Importance of Risk Factors in Distance-Based Generalized Linear Models," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 951-962, December.
    4. Esther Vayá & José Ramón García & Joaquim Murillo & Javier Romaní & Jordi Suriñach, 2016. "“Economic Impact of Cruise Activity: The Port of Barcelona”," AQR Working Papers 201609, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2016.
    5. Mercedes Ayuso & Montserrat Guillén & Jens Perch Nielsen, 2016. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Working Papers XREAP2016-08, Xarxa de Referència en Economia Aplicada (XREAP), revised Dec 2016.
    6. Antonio Manresa & Ferran Sancho, 2012. "Leontief versus Ghosh: two faces of the same coin," Working Papers XREAP2012-18, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2012.
    7. Anna Castañer & Mª Mercè Claramunt & Alba Tadeo & Javier Varea, 2016. "Modelización de la dependencia del número de siniestros. Aplicación a Solvencia II," Working Papers XREAP2016-01, Xarxa de Referència en Economia Aplicada (XREAP), revised Sep 2016.

  2. Frederic Udina & Pedro Delicado, 2001. "Estimating parliamentary composition through electoral polls," Economics Working Papers 562, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. José Garcia Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian forecasting of electoral outcomes with new parties' competition," Economics Working Papers 1624, Department of Economics and Business, Universitat Pompeu Fabra.
    2. José García-Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian Forecasting of Electoral Outcomes with new Parties' Competition," Working Papers 1065, Barcelona School of Economics.
    3. Jarosław Flis & Wojciech Słomczyński & Dariusz Stolicki, 2020. "Pot and ladle: a formula for estimating the distribution of seats under the Jefferson–D’Hondt method," Public Choice, Springer, vol. 182(1), pages 201-227, January.
    4. Montalvo, José G. & Papaspiliopoulos, Omiros & Stumpf-Fétizon, Timothée, 2019. "Bayesian forecasting of electoral outcomes with new parties’ competition," European Journal of Political Economy, Elsevier, vol. 59(C), pages 52-70.

  3. Estanislao Arana & Pedro Delicado & Luis Martí, 1999. "Validation procedures in radiological diagnostic models. Neural network and logistic regression," Economics Working Papers 414, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. Bergtold, Jason S. & Taylor, Daniel B. & Bosch, Darrell J., 2003. "Networking Your Way to a Better Prediction: Effectively Modeling Contingent Valuation Survey Data," 2003 Annual meeting, July 27-30, Montreal, Canada 22152, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

  4. Pedro Delicado & Manuel del Río, 1999. "A generalization of histogram type estimators," Economics Working Papers 422, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. Tomas Ruzgas & Mantas Lukauskas & Gedmantas Čepkauskas, 2021. "Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model," Mathematics, MDPI, vol. 9(21), pages 1-22, October.

  5. Pedro Delicado & Iolanda Placencia, 1997. "Comparing and validating hypothesis test procedures: Graphical and numerical tools," Economics Working Papers 210, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. Domowitz, I. El-Gamal, M., 1997. "Financial Market Structure and the Ergocicity of Prices," Working papers 9719, Wisconsin Madison - Social Systems.

  6. Delicado, Pedro, 1995. "Random coefficient regressions: parametric goodness of fit tests," DES - Working Papers. Statistics and Econometrics. WS 4199, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Pedro Delicado & Juan Romo, 1998. "Constant coefficient tests for random coefficient regression," Economics Working Papers 329, Department of Economics and Business, Universitat Pompeu Fabra.

  7. Delicado, Pedro, 1994. "Goodness of fit tests in random coefficient regression models," DES - Working Papers. Statistics and Econometrics. WS 3962, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Pedro Delicado & Juan Romo, 1998. "Constant coefficient tests for random coefficient regression," Economics Working Papers 329, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Tianshun Yan & Changlin Mei, 2017. "A test for a parametric form of the volatility in second-order diffusion models," Computational Statistics, Springer, vol. 32(4), pages 1583-1596, December.
    3. Zhang, Chun-Xia & Mei, Chang-Lin & Zhang, Jiang-She, 2007. "An empirical study of a test for polynomial relationships in randomly right censored regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6543-6556, August.
    4. Delicado, Pedro, 1995. "Random coefficient regressions: parametric goodness of fit tests," DES - Working Papers. Statistics and Econometrics. WS 4199, Universidad Carlos III de Madrid. Departamento de Estadística.

  8. Delicado, Pedro & Río, Manuel del, 1993. "Bootstraping the general linear hypothesis test," DES - Working Papers. Statistics and Econometrics. WS 3702, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Pedro Delicado, 1998. "Statistics in archaeology: New directions," Economics Working Papers 310, Department of Economics and Business, Universitat Pompeu Fabra.

Articles

  1. Pedro Delicado & Philippe Vieu, 2017. "Choosing the most relevant level sets for depicting a sample of densities," Computational Statistics, Springer, vol. 32(3), pages 1083-1113, September.

    Cited by:

    1. S. Barahona & P. Centella & X. Gual-Arnau & M. V. Ibáñez & A. Simó, 2020. "Supervised classification of geometrical objects by integrating currents and functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 637-660, September.

  2. Kehui Chen & Pedro Delicado & Hans-Georg Müller, 2017. "Modelling function-valued stochastic processes, with applications to fertility dynamics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 177-196, January.

    Cited by:

    1. Boland, Joanna & Telesca, Donatello & Sugar, Catherine & Jeste, Shafali & Goldbeck, Cameron & Senturk, Damla, 2022. "A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    2. Cody Carroll & Hans‐Georg Müller, 2023. "Latent deformation models for multivariate functional data and time‐warping separability," Biometrics, The International Biometric Society, vol. 79(4), pages 3345-3358, December.
    3. Aguilera-Morillo, M. Carmen & Aguilera, Ana M. & Jiménez-Molinos, Francisco & Roldán, Juan B., 2019. "Stochastic modeling of Random Access Memories reset transitions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 197-209.
    4. Marco Stefanucci & Stefano Mazzuco, 2022. "Analysing cause‐specific mortality trends using compositional functional data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 61-83, January.
    5. Alexander S. Long & Brian J. Reich & Ana‐Maria Staicu & John Meitzen, 2023. "A nonparametric test of group distributional differences for hierarchically clustered functional data," Biometrics, The International Biometric Society, vol. 79(4), pages 3778-3791, December.

  3. Eva Boj & Adrià Caballé & Pedro Delicado & Anna Esteve & Josep Fortiana, 2016. "Global and local distance-based generalized linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 170-195, March.

    Cited by:

    1. Beibei Yuan & Willem Heiser & Mark Rooij, 2019. "The δ-Machine: Classification Based on Distances Towards Prototypes," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 442-470, October.
    2. S. Barahona & P. Centella & X. Gual-Arnau & M. V. Ibáñez & A. Simó, 2020. "Supervised classification of geometrical objects by integrating currents and functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 637-660, September.
    3. Amparo Baíllo & Aurea Grané, 2021. "Subsampling and Aggregation: A Solution to the Scalability Problem in Distance-Based Prediction for Mixed-Type Data," Mathematics, MDPI, vol. 9(18), pages 1-17, September.

  4. Delicado, Pedro & Vieu, Philippe, 2015. "Optimal level sets for bivariate density representation," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 1-18.

    Cited by:

    1. Bongiorno, Enea G. & Goia, Aldo, 2019. "Describing the concentration of income populations by functional principal component analysis on Lorenz curves," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 10-24.
    2. Pedro Delicado & Philippe Vieu, 2017. "Choosing the most relevant level sets for depicting a sample of densities," Computational Statistics, Springer, vol. 32(3), pages 1083-1113, September.
    3. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.
    4. Aneiros, Germán & Horová, Ivana & Hušková, Marie & Vieu, Philippe, 2022. "On functional data analysis and related topics," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

  5. R. Giraldo & P. Delicado & J. Mateu, 2012. "Hierarchical clustering of spatially correlated functional data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(4), pages 403-421, November.

    Cited by:

    1. Tapia, Mariela & Heinemann, Detlev & Ballari, Daniela & Zondervan, Edwin, 2022. "Spatio-temporal characterization of long-term solar resource using spatial functional data analysis: Understanding the variability and complementarity of global horizontal irradiance in Ecuador," Renewable Energy, Elsevier, vol. 189(C), pages 1176-1193.
    2. Giraldo, Ramón & Dabo-Niang, Sophie & Martínez, Sergio, 2018. "Statistical modeling of spatial big data: An approach from a functional data analysis perspective," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 126-129.
    3. Menafoglio, Alessandra & Secchi, Piercesare, 2017. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics," European Journal of Operational Research, Elsevier, vol. 258(2), pages 401-410.
    4. Kim, Joonpyo & Oh, Hee-Seok, 2020. "Pseudo-quantile functional data clustering," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    5. Sultan Mahmud & Ferdausi Mahojabin Sumana & Md Mohsin & Md. Hasinur Rahaman Khan, 2022. "Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1863-1884, March.
    6. Mapitsi Rangata & Sonali Das & Montaz Ali, 2020. "Analysing Maximum Monthly Temperatures in South Africa for 45 years Using Functional Data Analysis," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(3), pages 1-27, September.
    7. Victor Muthama Musau & Carlo Gaetan & Paolo Girardi, 2022. "Clustering of bivariate satellite time series: A quantile approach," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
    8. Carlos Barrera-Causil & Juan Carlos Correa & Andrew Zamecnik & Francisco Torres-Avilés & Fernando Marmolejo-Ramos, 2021. "An FDA-Based Approach for Clustering Elicited Expert Knowledge," Stats, MDPI, vol. 4(1), pages 1-21, March.
    9. Elvira Romano & Jorge Mateu & Ramon Giraldo, 2015. "On the performance of two clustering methods for spatial functional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 467-492, October.
    10. Ramón Giraldo & William Caballero & Jesús Camacho-Tamayo, 2018. "Mantel test for spatial functional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 21-39, January.
    11. Rafael Meléndez & Ramón Giraldo & Víctor Leiva, 2020. "Sign, Wilcoxon and Mann-Whitney Tests for Functional Data: An Approach Based on Random Projections," Mathematics, MDPI, vol. 9(1), pages 1-11, December.

  6. C. Comas & P. Delicado & J. Mateu, 2011. "A second order approach to analyse spatial point patterns with functional marks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 503-523, November.

    Cited by:

    1. Bianca Maria Colosimo & Luca Pagani & Marco Grasso, 2024. "Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 429-447, January.
    2. Mohammad Ghorbani & Ottmar Cronie & Jorge Mateu & Jun Yu, 2021. "Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 529-568, September.
    3. Matthias Eckardt & Mehdi Moradi, 2024. "Marked Spatial Point Processes: Current State and Extensions to Point Processes on Linear Networks," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(2), pages 346-378, June.
    4. Ramón Giraldo & William Caballero & Jesús Camacho-Tamayo, 2018. "Mantel test for spatial functional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 21-39, January.

  7. Delicado, P., 2011. "Dimensionality reduction when data are density functions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 401-420, January.

    Cited by:

    1. Berrendero, José R. & Cuevas, Antonio & Pateiro-López, Beatriz, 2016. "Shape classification based on interpoint distance distributions," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 237-247.
    2. Hron, K. & Menafoglio, A. & Templ, M. & Hrůzová, K. & Filzmoser, P., 2016. "Simplicial principal component analysis for density functions in Bayes spaces," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 330-350.
    3. Won-Ki Seo, 2020. "Functional Principal Component Analysis for Cointegrated Functional Time Series," Papers 2011.12781, arXiv.org, revised Apr 2023.
    4. Calò, Daniela G. & Montanari, Angela & Viroli, Cinzia, 2014. "A hierarchical modeling approach for clustering probability density functions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 79-91.
    5. ARATA Yoshiyuki, 2017. "A Functional Linear Regression Model in the Space of Probability Density Functions," Discussion papers 17015, Research Institute of Economy, Trade and Industry (RIETI).
    6. Tadao Hoshino, 2024. "Functional Spatial Autoregressive Models," Papers 2402.14763, arXiv.org, revised Oct 2024.
    7. Bongiorno, Enea G. & Goia, Aldo, 2019. "Describing the concentration of income populations by functional principal component analysis on Lorenz curves," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 10-24.
    8. S. Barahona & P. Centella & X. Gual-Arnau & M. V. Ibáñez & A. Simó, 2020. "Supervised classification of geometrical objects by integrating currents and functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 637-660, September.
    9. Karel Hron & Jitka Machalová & Alessandra Menafoglio, 2023. "Bivariate densities in Bayes spaces: orthogonal decomposition and spline representation," Statistical Papers, Springer, vol. 64(5), pages 1629-1667, October.
    10. J. Machalová & K. Hron & G.S. Monti, 2016. "Preprocessing of centred logratio transformed density functions using smoothing splines," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1419-1435, June.
    11. Petersen, Alexander & Zhang, Chao & Kokoszka, Piotr, 2022. "Modeling Probability Density Functions as Data Objects," Econometrics and Statistics, Elsevier, vol. 21(C), pages 159-178.
    12. Martínez-Camblor, Pablo & Corral, Norberto, 2011. "Repeated measures analysis for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3244-3256, December.
    13. Berrendero, J.R. & Justel, A. & Svarc, M., 2011. "Principal components for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2619-2634, September.
    14. Genest, Christian & Hron, Karel & Nešlehová, Johanna G., 2023. "Orthogonal decomposition of multivariate densities in Bayes spaces and relation with their copula-based representation," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    15. Delicado, Pedro & Vieu, Philippe, 2015. "Optimal level sets for bivariate density representation," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 1-18.
    16. Jitka Machalová & Renáta Talská & Karel Hron & Aleš Gába, 2021. "Compositional splines for representation of density functions," Computational Statistics, Springer, vol. 36(2), pages 1031-1064, June.
    17. Seo, Won-Ki & Beare, Brendan K., 2019. "Cointegrated linear processes in Bayes Hilbert space," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 90-95.
    18. Talská, R. & Menafoglio, A. & Machalová, J. & Hron, K. & Fišerová, E., 2018. "Compositional regression with functional response," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 66-85.
    19. Kokoszka, Piotr & Miao, Hong & Petersen, Alexander & Shang, Han Lin, 2019. "Forecasting of density functions with an application to cross-sectional and intraday returns," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1304-1317.
    20. Menafoglio, Alessandra & Petris, Giovanni, 2016. "Kriging for Hilbert-space valued random fields: The operatorial point of view," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 84-94.
    21. Shang, Han Lin & Haberman, Steven & Xu, Ruofan, 2022. "Multi-population modelling and forecasting life-table death counts," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 239-253.
    22. Zhang, Zhen & Müller, Hans-Georg, 2011. "Functional density synchronization," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2234-2249, July.
    23. Hsin‐wen Chang & Ian W. McKeague, 2022. "Empirical likelihood‐based inference for functional means with application to wearable device data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1947-1968, November.
    24. Angela Montanari & Daniela Calò, 2013. "Model-based clustering of probability density functions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 301-319, September.
    25. Epifanio, Irene & Ventura-Campos, Noelia, 2011. "Functional data analysis in shape analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2758-2773, September.

  8. Gonzalez, Juan R. & Peña, Edsel A. & Delicado, Pedro, 2010. "Confidence intervals for median survival time with recurrent event data," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 78-89, January.

    Cited by:

    1. de Peretti, Christian & Siani, Carole, 2010. "Graphical methods for investigating the finite-sample properties of confidence regions," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 262-271, February.

  9. Boj, Eva & Delicado, Pedro & Fortiana, Josep, 2010. "Distance-based local linear regression for functional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 429-437, February.

    Cited by:

    1. Zhiyong Zhou & Zhengyan Lin, 2016. "Asymptotic normality of locally modelled regression estimator for functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 116-131, March.
    2. Chagny, Gaëlle & Roche, Angelina, 2016. "Adaptive estimation in the functional nonparametric regression model," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 105-118.
    3. Chouaf Abdelhak & Laksaci Ali, 2012. "On the functional local linear estimate for spatial regression," Statistics & Risk Modeling, De Gruyter, vol. 29(3), pages 189-214, August.
    4. Fatiha Messaci & Nahima Nemouchi & Idir Ouassou & Mustapha Rachdi, 2015. "Local polynomial modelling of the conditional quantile for functional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 597-622, November.
    5. Han Lin Shang, 2014. "Bayesian bandwidth estimation for a functional nonparametric regression model with mixed types of regressors and unknown error density," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(3), pages 599-615, September.
    6. Bouabsa Wahiba, 2023. "The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 27(1), pages 17-32, March.
    7. Oussama Bouanani & Saâdia Rahmani & Ali Laksaci & Mustapha Rachdi, 2020. "Asymptotic normality of conditional mode estimation for functional dependent data," Indian Journal of Pure and Applied Mathematics, Springer, vol. 51(2), pages 465-481, June.
    8. Abderrahmane Belguerna & Hamza Daoudi & Khadidja Abdelhak & Boubaker Mechab & Zouaoui Chikr Elmezouar & Fatimah Alshahrani, 2024. "A Comprehensive Analysis of MSE in Estimating Conditional Hazard Functions: A Local Linear, Single Index Approach for MAR Scenarios," Mathematics, MDPI, vol. 12(3), pages 1-20, February.
    9. Aurea Grané & Alpha A. Sow-Barry, 2021. "Visualizing Profiles of Large Datasets of Weighted and Mixed Data," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    10. Oscar Melo & Carlos Melo & Jorge Mateu, 2015. "Distance-based beta regression for prediction of mutual funds," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 83-106, January.
    11. Han Shang, 2014. "Bayesian bandwidth estimation for a semi-functional partial linear regression model with unknown error density," Computational Statistics, Springer, vol. 29(3), pages 829-848, June.
    12. Shang, Han Lin, 2013. "Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 185-198.
    13. Raffaella Piccarreta, 2012. "Graphical and Smoothing Techniques for Sequence Analysis," Sociological Methods & Research, , vol. 41(2), pages 362-380, May.
    14. Philip T. Reiss & Jeff Goldsmith & Han Lin Shang & R. Todd Ogden, 2017. "Methods for Scalar-on-Function Regression," International Statistical Review, International Statistical Institute, vol. 85(2), pages 228-249, August.

  10. Delicado, P. & Goria, M.N., 2008. "A small sample comparison of maximum likelihood, moments and L-moments methods for the asymmetric exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1661-1673, January.

    Cited by:

    1. R. K. Jaiswal & T. R. Nayak & A. K. Lohani & R. V. Galkate, 2022. "Regional flood frequency modeling for a large basin in India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1845-1861, March.
    2. Barriga, Gladys D.C. & Louzada-Neto, Franscisco & Cancho, Vicente G., 2011. "The complementary exponential power lifetime model," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1250-1259, March.
    3. Asquith, William H., 2014. "Parameter estimation for the 4-parameter Asymmetric Exponential Power distribution by the method of L-moments using R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 955-970.
    4. Di Nardo, E. & Guarino, G. & Senato, D., 2008. "Symbolic computation of moments of sampling distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4909-4922, July.
    5. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    6. Camilo Lillo & Víctor Leiva & Orietta Nicolis & Robert G. Aykroyd, 2018. "L-moments of the Birnbaum–Saunders distribution and its extreme value version: estimation, goodness of fit and application to earthquake data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(2), pages 187-209, January.

  11. Pedro Delicado, 2007. "Functional k-sample problem when data are density functions," Computational Statistics, Springer, vol. 22(3), pages 391-410, September.

    Cited by:

    1. Martínez-Camblor, Pablo, 2010. "Nonparametric k-sample test based on kernel density estimator for paired design," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 2035-2045, August.
    2. Hron, K. & Menafoglio, A. & Templ, M. & Hrůzová, K. & Filzmoser, P., 2016. "Simplicial principal component analysis for density functions in Bayes spaces," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 330-350.
    3. Łukasz Smaga, 2020. "A note on repeated measures analysis for functional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 117-139, March.
    4. Matthias Studer & Gilbert Ritschard & Alexis Gabadinho & Nicolas S. Müller, 2011. "Discrepancy Analysis of State Sequences," Sociological Methods & Research, , vol. 40(3), pages 471-510, August.
    5. István Berkes & Robertas Gabrys & Lajos Horváth & Piotr Kokoszka, 2009. "Detecting changes in the mean of functional observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 927-946, November.
    6. Bongiorno, Enea G. & Goia, Aldo, 2019. "Describing the concentration of income populations by functional principal component analysis on Lorenz curves," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 10-24.
    7. S. Barahona & P. Centella & X. Gual-Arnau & M. V. Ibáñez & A. Simó, 2020. "Supervised classification of geometrical objects by integrating currents and functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 637-660, September.
    8. Anthony Hayter, 2014. "Identifying common normal distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 135-152, March.
    9. Łukasz Smaga & Jin‐Ting Zhang, 2020. "Linear hypothesis testing for weighted functional data with applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 493-515, June.
    10. Martínez-Camblor, Pablo & Corral, Norberto, 2011. "Repeated measures analysis for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3244-3256, December.
    11. Vsevolozhskaya, O.A. & Greenwood, M.C. & Bellante, G.J. & Powell, S.L. & Lawrence, R.L. & Repasky, K.S., 2013. "Combining functions and the closure principle for performing follow-up tests in functional analysis of variance," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 175-184.
    12. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.
    13. Dalia Valencia & Rosa E. Lillo & Juan Romo, 2019. "A Kendall correlation coefficient between functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1083-1103, December.
    14. Christian Acal & Ana M. Aguilera, 2023. "Basis expansion approaches for functional analysis of variance with repeated measures," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 291-321, June.
    15. Menafoglio, Alessandra & Petris, Giovanni, 2016. "Kriging for Hilbert-space valued random fields: The operatorial point of view," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 84-94.
    16. Zhang, Zhen & Müller, Hans-Georg, 2011. "Functional density synchronization," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2234-2249, July.
    17. Huaihou Chen & Philip T. Reiss & Thaddeus Tarpey, 2014. "Optimally weighted L-super-2 distance for functional data," Biometrics, The International Biometric Society, vol. 70(3), pages 516-525, September.

  12. Pedro Delicado, 2006. "Local likelihood density estimation based on smooth truncation," Biometrika, Biometrika Trust, vol. 93(2), pages 472-480, June.

    Cited by:

    1. Marco Marzio & Stefania Fensore & Agnese Panzera & Charles C. Taylor, 2018. "Circular local likelihood," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 921-945, December.

  13. Frederic Udina & Pedro Delicado, 2005. "Estimating Parliamentary composition through electoral polls," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 387-399, March.
    See citations under working paper version above.
  14. Pedro Delicado & Mario Huerta, 2003. "Principal Curves of Oriented Points: theoretical and computational improvements," Computational Statistics, Springer, vol. 18(2), pages 293-315, July.

    Cited by:

    1. Pulkkinen, Seppo, 2015. "Ridge-based method for finding curvilinear structures from noisy data," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 89-109.
    2. Delicado, P., 2011. "Dimensionality reduction when data are density functions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 401-420, January.
    3. Haonan Wang & Hari Iyer, 2007. "Application of local linear embedding to nonlinear exploratory latent structure analysis," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 199-225, June.

  15. Castro-Rodriguez, Fidel & Da-Rocha, Jose Maria & Delicado, Pedro, 2002. "Desperately Seeking Theta's: Estimating the Distribution of Consumers under Increasing Block Rates," Journal of Regulatory Economics, Springer, vol. 22(1), pages 29-58, July.

    Cited by:

    1. Arbues, Fernando & Garcia-Valinas, Maria Angeles & Martinez-Espineira, Roberto, 2003. "Estimation of residential water demand: a state-of-the-art review," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 32(1), pages 81-102, March.
    2. María A. García-Valiñas, 2018. "Spain," Post-Print hal-03191536, HAL.
    3. Lehmann, Paul, 2011. "Making water affordable to all: A typology and evaluation of options for urban water pricing," UFZ Discussion Papers 10/2011, Helmholtz Centre for Environmental Research (UFZ), Division of Social Sciences (ÖKUS).
    4. Rita Martins & Carlota Quintal & Eduardo Barata & Luís Cruz, 2010. "Water Pricing and Social Equity in Portuguese Municipalities," GEMF Working Papers 2010-17, GEMF, Faculty of Economics, University of Coimbra.
    5. Pinto, Francisco Silva & Marques, Rui Cuhna, 2015. "Tariff recommendations: A Panacea for the Portuguese water sector?," Utilities Policy, Elsevier, vol. 34(C), pages 36-44.
    6. Ming-Feng Hung & Bin-Tzong Chie & Huei-Chu Liao, 2020. "A Comparison of Electricity-Pricing Programs: Economic Efficiency, Cost Recovery, and Income Distribution," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(1), pages 143-163, February.
    7. Maria A. García‐Valiñas & Roberto Martínez‐Espiñeira & Francisco González‐Gómez, 2010. "Economics of Water Reform in the Murray-Darling Basin," Centre for Water Economics, Environment and Policy Papers 1005, Centre for Water Economics, Environment and Policy, Crawford School of Public Policy, The Australian National University.
    8. María Angeles García Valiñas, 2005. "Promotion and remuneration of university professors: from the LRU to the COU," Hacienda Pública Española / Review of Public Economics, IEF, vol. 172(1), pages 119-143, June.
    9. María Angeles García Valiñas, 2004. "Eficiencia y equidad en el diseño de precios óptimos para bienes y servicios públicos," Hacienda Pública Española / Review of Public Economics, IEF, vol. 168(1), pages 95-119, march.
    10. Maria A. García-Valiñas & Roberto Martínez-Francisco & González-Gómez, 2010. "Water affordability: alternativem measurement and explanatory Factors in Andalusia," Centre for Water Economics, Environment and Policy Papers 1014, Centre for Water Economics, Environment and Policy, Crawford School of Public Policy, The Australian National University.
    11. Rita Martins & Luis Cruz & Eduardo Barata, 2013. "Water Price Regulation: A Review of Portuguese Tariff Recommendations," Public Organization Review, Springer, vol. 13(2), pages 197-205, June.

  16. Delicado, Pedro, 2001. "Another Look at Principal Curves and Surfaces," Journal of Multivariate Analysis, Elsevier, vol. 77(1), pages 84-116, April.

    Cited by:

    1. Serge Iovleff, 2015. "Probabilistic auto-associative models and semi-linear PCA," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(3), pages 267-286, September.
    2. Salinelli, Ernesto, 2009. "Nonlinear principal components, II: Characterization of normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 652-660, April.
    3. Pulkkinen, Seppo, 2015. "Ridge-based method for finding curvilinear structures from noisy data," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 89-109.
    4. Berrendero, J.R. & Justel, A. & Svarc, M., 2011. "Principal components for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2619-2634, September.
    5. Pedro Delicado, 1998. "Statistics in archaeology: New directions," Economics Working Papers 310, Department of Economics and Business, Universitat Pompeu Fabra.
    6. Cholaquidis, Alejandro & Fraiman, Ricardo & Moreno, Leonardo, 2022. "Level set and density estimation on manifolds," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    7. Girard, Stéphane & Iovleff, Serge, 2005. "Auto-associative models and generalized principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 21-39, March.
    8. Pedro Delicado & Mario Huerta, 2003. "Principal Curves of Oriented Points: theoretical and computational improvements," Computational Statistics, Springer, vol. 18(2), pages 293-315, July.
    9. Youness Aliyari Ghassabeh & Frank Rudzicz, 2021. "Modified Subspace Constrained Mean Shift Algorithm," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 27-43, April.

  17. Pedro Delicado & Juan Romo, 1999. "Goodness of Fit Tests in Random Coefficient Regression Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(1), pages 125-148, March.
    See citations under working paper version above.
  18. Delicado, Pedro & del Rio, Manuel, 1994. "Bootstrapping the general linear hypothesis test," Computational Statistics & Data Analysis, Elsevier, vol. 18(3), pages 305-316, October.
    See citations under working paper version above.

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NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (2) 1998-11-23 2012-05-29
  2. NEP-FOR: Forecasting (1) 2012-05-29

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