Pedro Duarte Silva

Personal Details

First Name:	Pedro
Middle Name:	Duarte
Last Name:	Silva
Suffix:
RePEc Short-ID:	psi537
	[This author has chosen not to make the email address public]

Affiliation

Católica Porto Business School
Universidade Católica Portuguesa

Porto, Portugal
http://www.catolicabs.porto.ucp.pt/
RePEc:edi:feucppt (more details at EDIRC)

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. A. Pedro Duarte Silva, 2000. "DISCARDING VARIABLES in PRINCIPAL COMPONENT ANALYSIS : ALGORITHMS for ALL-SUBSETS COMPARISONS," Working Papers de Economia (Economics Working Papers) 02, Católica Porto Business School, Universidade Católica Portuguesa.

   Cited by:

   1. Michael Brusco & Renu Singh & Douglas Steinley, 2009. "Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 705-726, December.
   2. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
   3. Ester Gutiérrez & Sebastián Lozano, 2014. "A DEA Approach to Performance-Based Budgeting of Formula One Constructors," Journal of Sports Economics, , vol. 15(2), pages 180-200, April.

Articles

1. A. Pedro Duarte Silva & Peter Filzmoser & Paula Brito, 2018. "Outlier detection in interval 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. 12(3), pages 785-822, September.

   Cited by:

   1. M. Rosário Oliveira & Margarida Azeitona & António Pacheco & Rui Valadas, 2022. "Association measures for interval variables," 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. 16(3), pages 491-520, September.

2. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.

   Cited by:

   1. Blanquero, Rafael & Carrizosa, Emilio & Molero-Río, Cristina & Romero Morales, Dolores, 2020. "Sparsity in optimal randomized classification trees," European Journal of Operational Research, Elsevier, vol. 284(1), pages 255-272.
   2. Laura Palagi, 2019. "Global optimization issues in deep network regression: an overview," Journal of Global Optimization, Springer, vol. 73(2), pages 239-277, February.
   3. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
   4. Astorino, Annabella & Avolio, Matteo & Fuduli, Antonio, 2022. "A maximum-margin multisphere approach for binary Multiple Instance Learning," European Journal of Operational Research, Elsevier, vol. 299(2), pages 642-652.
   5. Baldomero-Naranjo, Marta & Martínez-Merino, Luisa I. & Rodríguez-Chía, Antonio M., 2020. "Tightening big Ms in integer programming formulations for support vector machines with ramp loss," European Journal of Operational Research, Elsevier, vol. 286(1), pages 84-100.
   6. Baumann, P. & Hochbaum, D.S. & Yang, Y.T., 2019. "A comparative study of the leading machine learning techniques and two new optimization algorithms," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1041-1057.
   7. Sandra Benítez-Peña & Rafael Blanquero & Emilio Carrizosa & Pepa Ramírez-Cobo, 2019. "On support vector machines under a multiple-cost scenario," 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(3), pages 663-682, September.

3. A. Silva & Paula Brito, 2015. "Discriminant Analysis of Interval Data: An Assessment of Parametric and Distance-Based Approaches," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 516-541, October.

   Cited by:

   1. Dias, Sónia & Brito, Paula & Amaral, Paula, 2021. "Discriminant analysis of distributional data via fractional programming," European Journal of Operational Research, Elsevier, vol. 294(1), pages 206-218.
   2. M. Rosário Oliveira & Margarida Azeitona & António Pacheco & Rui Valadas, 2022. "Association measures for interval variables," 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. 16(3), pages 491-520, September.
   3. A. Pedro Duarte Silva & Peter Filzmoser & Paula Brito, 2018. "Outlier detection in interval 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. 12(3), pages 785-822, September.
   4. Boris Beranger & Huan Lin & Scott Sisson, 2023. "New models for symbolic data analysis," 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(3), pages 659-699, September.
   5. Rong Guan & Huiwen Wang & Haitao Zheng, 2020. "Improving accuracy of financial distress prediction by considering volatility: an interval-data-based discriminant model," Computational Statistics, Springer, vol. 35(2), pages 491-514, June.
   6. Pierpaolo D’Urso & Riccardo Massari & Livia De Giovanni & Carmela Cappelli, 2017. "Exponential distance-based fuzzy clustering for interval-valued data," Fuzzy Optimization and Decision Making, Springer, vol. 16(1), pages 51-70, March.

4. Paula Brito & A. Pedro Duarte Silva, 2012. "Modelling interval data with Normal and Skew-Normal distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 3-20, March.

   Cited by:

   1. Eufr�sio de A. Lima Neto & Ulisses U. dos Anjos, 2015. "Regression model for interval-valued variables based on copulas," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(9), pages 2010-2029, September.
   2. Sinova, Beatriz & Van Aelst, Stefan, 2015. "On the consistency of a spatial-type interval-valued median for random intervals," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 130-136.
   3. Sun, Yuying & Zhang, Xinyu & Wan, Alan T.K. & Wang, Shouyang, 2022. "Model averaging for interval-valued data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 772-784.
   4. Drago, Carlo, 2016. "Exploring the Community Structure of Complex Networks," ETA: Economic Theory and Applications 244529, Fondazione Eni Enrico Mattei (FEEM).
      • Drago, Carlo, 2015. "Exploring the Community Structure of Complex Networks," MPRA Paper 81024, University Library of Munich, Germany.
      • Carlo Drago, 2016. "Exploring the Community Structure of Complex Networks," Working Papers 2016.57, Fondazione Eni Enrico Mattei.
   5. M. Rosário Oliveira & Margarida Azeitona & António Pacheco & Rui Valadas, 2022. "Association measures for interval variables," 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. 16(3), pages 491-520, September.
   6. Qing Zhao & Huiwen Wang & Shanshan Wang, 2023. "Robust regression for interval-valued data based on midpoints and log-ranges," 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(3), pages 583-621, September.
   7. Karel Hron & Paula Brito & Peter Filzmoser, 2017. "Exploratory data analysis for interval compositional 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. 11(2), pages 223-241, June.
   8. A. Pedro Duarte Silva & Peter Filzmoser & Paula Brito, 2018. "Outlier detection in interval 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. 12(3), pages 785-822, September.
   9. Boris Beranger & Huan Lin & Scott Sisson, 2023. "New models for symbolic data analysis," 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(3), pages 659-699, September.
   10. A. Silva & Paula Brito, 2015. "Discriminant Analysis of Interval Data: An Assessment of Parametric and Distance-Based Approaches," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 516-541, October.
   11. Dias, Sónia & Brito, Paula, 2017. "Off the beaten track: A new linear model for interval data," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1118-1130.
   12. Samadi, S. Yaser & Billard, Lynne, 2021. "Analysis of dependent data aggregated into intervals," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
   13. Liang-Ching Lin & Hsiang-Lin Chien & Sangyeol Lee, 2021. "Symbolic interval-valued data analysis for time series based on auto-interval-regressive models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 295-315, March.

5. Pedro Duarte Silva, A., 2011. "Two-group classification with high-dimensional correlated data: A factor model approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2975-2990, November.

   Cited by:

   1. Olivier Ledoit & Michael Wolf, 2013. "Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions," ECON - Working Papers 105, Department of Economics - University of Zurich, revised Jul 2013.
      • Ledoit, Olivier & Wolf, Michael, 2015. "Spectrum estimation: A unified framework for covariance matrix estimation and PCA in large dimensions," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 360-384.

6. António Silva & Paula Brito, 2006. "Linear discriminant analysis for interval data," Computational Statistics, Springer, vol. 21(2), pages 289-308, June.

   Cited by:

   1. Angela Blanco-Fernández & Peter Winker, 2016. "Data generation processes and statistical management of interval data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(4), pages 475-494, October.
   2. Dias, Sónia & Brito, Paula & Amaral, Paula, 2021. "Discriminant analysis of distributional data via fractional programming," European Journal of Operational Research, Elsevier, vol. 294(1), pages 206-218.
   3. Rong Guan & Huiwen Wang & Haitao Zheng, 2020. "Improving accuracy of financial distress prediction by considering volatility: an interval-data-based discriminant model," Computational Statistics, Springer, vol. 35(2), pages 491-514, June.
   4. A. Silva & Paula Brito, 2015. "Discriminant Analysis of Interval Data: An Assessment of Parametric and Distance-Based Approaches," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 516-541, October.

7. Stam, Antonie & Duarte Silva, A. Pedro, 2003. "On multiplicative priority rating methods for the AHP," European Journal of Operational Research, Elsevier, vol. 145(1), pages 92-108, February.

   Cited by:

   1. Rajesh Kr. Singh & Angappa Gunasekaran & Pravin Kumar, 2018. "Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach," Annals of Operations Research, Springer, vol. 267(1), pages 531-553, August.
   2. Gomez-Limon, J.A. & Atance, I., 2004. "Identification of public objectives related to agricultural sector support," Journal of Policy Modeling, Elsevier, vol. 26(8-9), pages 1045-1071, December.
   3. P Leskinen & J Kangas, 2005. "Rank reversals in multi-criteria decision analysis with statistical modelling of ratio-scale pairwise comparisons," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(7), pages 855-861, July.
   4. Francisco J. André & Laura Riesgo, 2006. "A Duality Procedure to Elicit Nonlinear Multiattribute Utility Functions," Working Papers 06.02, Universidad Pablo de Olavide, Department of Economics.
   5. Alessio Ishizaka & Dieter Balkenborg & Todd Kaplan, 2005. "Influence of aggregation and measurement scale on ranking a compromise alternative in AHP," Discussion Papers 0506, University of Exeter, Department of Economics.
      • A Ishizaka & D Balkenborg & T Kaplan, 2011. "Influence of aggregation and measurement scale on ranking a compromise alternative in AHP," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 700-710, April.
      • Ishizaka, Alessio & Balkenborg, Dieter & Kaplan, Todd, 2009. "Influence of aggregation and measurement scale on ranking a compromise alternative in AHP," MPRA Paper 19563, University Library of Munich, Germany.
   6. José A. Gómez-Limón & Ignacio Atance, 2004. "Identification of Public Objectives Related to Agricultural Sector Support," Economic Working Papers at Centro de Estudios Andaluces E2004/57, Centro de Estudios Andaluces.
   7. Andre, Francisco J. & Riesgo, Laura, 2007. "A non-interactive elicitation method for non-linear multiattribute utility functions: Theory and application to agricultural economics," European Journal of Operational Research, Elsevier, vol. 181(2), pages 793-807, September.
   8. Ardalan Bafahm & Minghe Sun, 2019. "Some Conflicting Results in the Analytic Hierarchy Process," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 465-486, March.
   9. Liu, Xianliang & Ma, Yonghao, 2021. "A method to analyze the rank reversal problem in the ELECTRE II method," Omega, Elsevier, vol. 102(C).
   10. Jordi Gallego-Ayala & Dinis Juízo, 2014. "Integrating Stakeholders’ Preferences into Water Resources Management Planning in the Incomati River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 527-540, January.
   11. Rodríguez Sousa, A.A. & Parra-López, C. & Sayadi-Gmada, S. & Barandica, J.M. & Rescia, A.J., 2020. "A multifunctional assessment of integrated and ecological farming in olive agroecosystems in southwestern Spain using the Analytic Hierarchy Process," Ecological Economics, Elsevier, vol. 173(C).
   12. Hahn, Eugene D., 2006. "Link function selection in stochastic multicriteria decision making models," European Journal of Operational Research, Elsevier, vol. 172(1), pages 86-100, July.
   13. Pranith K. Roy & Krishnendu Shaw, 2023. "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 372-391, January.

8. Duarte Silva, António Pedro, 2001. "Efficient Variable Screening for Multivariate Analysis," Journal of Multivariate Analysis, Elsevier, vol. 76(1), pages 35-62, January.

   Cited by:

   1. Brusco, Michael J. & Steinley, Douglas, 2011. "Exact and approximate algorithms for variable selection in linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 123-131, January.
   2. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
      • du Jardin, Philippe & Severin, Eric, 2011. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," MPRA Paper 39935, University Library of Munich, Germany, revised 03 Apr 2012.
   3. Michael Brusco & Renu Singh & Douglas Steinley, 2009. "Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 705-726, December.
   4. Pedro Duarte Silva, A., 2011. "Two-group classification with high-dimensional correlated data: A factor model approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2975-2990, November.
   5. António Pedro Duarte Silva, 2002. "Discarding Variables in a Principal Component Analysis: Algorithms for All-Subsets Comparisons," Computational Statistics, Springer, vol. 17(2), pages 251-271, July.
   6. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
   7. Ester Gutiérrez & Sebastián Lozano, 2014. "A DEA Approach to Performance-Based Budgeting of Formula One Constructors," Journal of Sports Economics, , vol. 15(2), pages 180-200, April.
   8. Cadima, Jorge & Cerdeira, J. Orestes & Minhoto, Manuel, 2004. "Computational aspects of algorithms for variable selection in the context of principal components," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 225-236, September.
   9. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
   10. Nkiet, Guy Martial, 2012. "Direct variable selection for discrimination among several groups," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 151-163.
   11. A. Pedro Duarte Silva, 2009. "Exact and heuristic algorithms for variable selection: Extended Leaps and Bounds," Working Papers de Economia (Economics Working Papers) 01, Católica Porto Business School, Universidade Católica Portuguesa.

9. Silva, Antonio Pedro Duarte & Stam, Antonie, 1994. "Second order mathematical programming formulations for discriminant analysis," European Journal of Operational Research, Elsevier, vol. 72(1), pages 4-22, January.

   Cited by:

   1. Sueyoshi, Toshiyuki, 2001. "Extended DEA-Discriminant Analysis," European Journal of Operational Research, Elsevier, vol. 131(2), pages 324-351, June.
   2. Mingue Sun, 2009. "Liquidity Risk and Financial Competition: A Mixed Integer Programming Model for Multiple-Class Discriminant Analysis," Working Papers 0102, College of Business, University of Texas at San Antonio.
   3. J J Glen, 2005. "Mathematical programming models for piecewise-linear discriminant analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(3), pages 331-341, March.
   4. Wilson, J. M., 1996. "Integer programming formulations of statistical classification problems," Omega, Elsevier, vol. 24(6), pages 681-688, December.
   5. A. Pedro Duarte Silva & Antonie Stam & John Neter, 1997. "Two-Group Classification in Business: an Evaluation of Paramatric and Non-Parametric Approaches," Working Papers de Economia (Economics Working Papers) 03, Católica Porto Business School, Universidade Católica Portuguesa.
   6. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
   7. Sueyoshi, Toshiyuki, 2004. "Mixed integer programming approach of extended DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 152(1), pages 45-55, January.
   8. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry," European Journal of Operational Research, Elsevier, vol. 199(2), pages 576-594, December.
   9. Lam, Kim Fung & Moy, Jane W., 2002. "Combining discriminant methods in solving classification problems in two-group discriminant analysis," European Journal of Operational Research, Elsevier, vol. 138(2), pages 294-301, April.
   10. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
   11. Stam, Antonie & Ungar, David R., 1995. "RAGNU: A microcomputer package for two-group mathematical programming-based nonparametric classification," European Journal of Operational Research, Elsevier, vol. 86(2), pages 374-388, October.
   12. J J Glen, 2008. "An additive utility mixed integer programming model for nonlinear discriminant analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1492-1505, November.
   13. Wanarat, Pradit & Pavur, Robert, 1996. "Examining the effect of second-order terms in mathematical programming approaches to the classification problem," European Journal of Operational Research, Elsevier, vol. 93(3), pages 582-601, September.
   14. Glen, J.J., 2006. "A comparison of standard and two-stage mathematical programming discriminant analysis methods," European Journal of Operational Research, Elsevier, vol. 171(2), pages 496-515, June.
   15. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.
   16. Ostermark, Ralf & Hoglund, Rune, 1998. "Addressing the multigroup discriminant problem using multivariate statistics and mathematical programming," European Journal of Operational Research, Elsevier, vol. 108(1), pages 224-237, July.
   17. Sueyoshi, Toshiyuki, 1999. "DEA-discriminant analysis in the view of goal programming," European Journal of Operational Research, Elsevier, vol. 115(3), pages 564-582, June.

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Co-authorship network on CollEc

NEP Fields

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-CMP: Computational Economics (1) 2009-01-17
2. NEP-ECM: Econometrics (1) 2009-05-16

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