Andreas Alfons

Personal Details

First Name:	Andreas
Middle Name:
Last Name:	Alfons
Suffix:
RePEc Short-ID:	pal900
	[This author has chosen not to make the email address public]
	https://personal.eur.nl/alfons/

Affiliation

Econometrisch Instituut
Faculteit der Economische Wetenschappen
Erasmus Universiteit Rotterdam

Rotterdam, Netherlands
http://www.econometric-institute.org/
RePEc:edi:eieurnl (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.

Articles

1. Andreas Alfons & Christophe Croux & Peter Filzmoser, 2017. "Robust Maximum Association Estimators," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 436-445, January.

   Cited by:

   1. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
   2. Nathan Uyttendaele, 2018. "On the estimation of nested Archimedean copulas: a theoretical and an experimental comparison," Computational Statistics, Springer, vol. 33(2), pages 1047-1070, June.
   3. Langworthy, Benjamin W. & Stephens, Rebecca L. & Gilmore, John H. & Fine, Jason P., 2021. "Canonical correlation analysis for elliptical copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
   4. Jorge G. Adrover & Stella M. Donato, 2023. "Aspects of robust canonical correlation analysis, principal components and association," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 623-650, June.
   5. Stephen P. Groff, 2022. "A contemporary social contract: An exploration of enabling factors influencing climate policy intractability in developed nations," Global Policy, London School of Economics and Political Science, vol. 13(5), pages 721-735, November.
   6. Alfio Marazzi & Marina Valdora & Victor Yohai & Michael Amiguet, 2019. "A robust conditional maximum likelihood estimator for generalized linear models with a dispersion parameter," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 223-241, March.
   7. Liebscher, Eckhard, 2021. "Kendall regression coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

2. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.

   Cited by:

   1. Zhaoxia Xu & Xiaoping Zhou & Qihu Qian, 2021. "The global sensitivity analysis of slope stability based on the least angle regression," 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. 105(3), pages 2361-2379, February.
   2. Smucler, Ezequiel & Yohai, Victor J., 2017. "Robust and sparse estimators for linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 116-130.

3. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).

   Cited by:

   1. Robert Vlacuha & Boris Frankovic, 2015. "The Calibration of Weights by Calif Tool in the Practice of the Statistical Office of the Slovak Republic," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 153-164, June.
   2. Kreutzmann, Ann-Kristin & Pannier, Sören & Rojas-Perilla, Natalia & Schmid, Timo & Templ, Matthias & Tzavidis, Nikos, 2017. "The R package emdi for estimating and mapping regionally disaggregated indicators," Discussion Papers 2017/15, Free University Berlin, School of Business & Economics.
   3. Anna Murawska & Bartosz Mickiewicz & Małgorzata Zajdel & Małgorzata Michalcewicz-Kaniowska, 2020. "Multidimensional Analysis of the Relationship between Sustainable Living Conditions and Long and Good Health in the European Union Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 716-735.
   4. Dawber James & Smith Paul A. & Tzavidis Nikos & Würz Nora & Flower Tanya & Thomas Heledd & Schmid Timo, 2022. "Experimental UK Regional Consumer Price Inflation with Model-Based Expenditure Weights," Journal of Official Statistics, Sciendo, vol. 38(1), pages 213-237, March.
   5. Domma, Filippo & Condino, Francesca & Giordano, Sabrina, 2018. "A new formulation of the Dagum distribution in terms of income inequality and poverty measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 104-126.
   6. Chakraborty, Robin & Waltl, Sofie R., 2018. "Missing the wealthy in the HFCS: micro problems with macro implications," Working Paper Series 2163, European Central Bank.
      • Sofie R. Waltl & Robin Chakraborty, 2022. "Missing the wealthy in the HFCS: micro problems with macro implications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 169-203, March.
   7. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
   8. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
   9. Abhik Ghosh, 2017. "Divergence based robust estimation of the tail index through an exponential regression model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(2), pages 181-213, June.
   10. Marchetti Stefano & Tzavidis Nikos, 2021. "Robust Estimation of the Theil Index and the Gini Coeffient for Small Areas," Journal of Official Statistics, Sciendo, vol. 37(4), pages 955-979, December.
   11. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
   12. Nora Würz & Timo Schmid & Nikos Tzavidis, 2022. "Estimating regional income indicators under transformations and access to limited population auxiliary information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1679-1706, October.
   13. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
   14. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
   15. Maria Marino & Benedetto Rocchi & Simone Severini, 2021. "Conditional Income Disparity between Farm and Non‐farm Households in the European Union: A Longitudinal Analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(2), pages 589-606, June.
   16. Templ, Matthias & Kowarik, Alexander & Meindl, Bernhard, 2015. "Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i04).
   17. Ann-Kristin Kreutzmann, 2018. "Estimation of sample quantiles: challenges and issues in the context of income and wealth distributions [Die Schätzung von Quantilen: Herausforderungen und Probleme im Kontext von Einkommens- und V," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 12(3), pages 245-270, December.
   18. Walter, Paul & Weimer, Katja, 2018. "Estimating poverty and inequality indicators using interval censored income data from the German microcensus," Discussion Papers 2018/10, Free University Berlin, School of Business & Economics.
   19. Enrico Fabrizi & Maria Rosaria Ferrante & Carlo Trivisano, 2020. "A functional approach to small area estimation of the relative median poverty gap," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1273-1291, June.
   20. Rocchi, B & Marino, M & Severini, S, 2018. "Does a farm household income problem still exist in the European Union?," 2018 Seventh AIEAA Conference, June 14-15, Conegliano, Italy 275653, Italian Association of Agricultural and Applied Economics (AIEAA).
   21. 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.
   22. Tzavidis, Nikos & Zhang, Li-Chun & Luna Hernandez, Angela & Schmid, Timo & Rojas-Perilla, Natalia, 2016. "From start to finish: A framework for the production of small area official statistics," Discussion Papers 2016/13, Free University Berlin, School of Business & Economics.
   23. Benedetto Rocchi & Maria Marino & Simone Severini, 2021. "Does an Income Gap between Farm and Nonfarm Households Still Exist? The Case of the European Union," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(4), pages 1672-1697, December.
   24. Gregorio Izquierdo Llanes & Antonio Salcedo Galiano, 2023. "Why does equivalization matter? An application to the monetary poverty in the sustainable development goals framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2575-2589, June.
   25. Ilaria Benedetti & Gianni Betti & Federico Crescenzi, 2020. "Measuring Child Poverty and Its Uncertainty: A Case Study of 33 European Countries," Sustainability, MDPI, vol. 12(19), pages 1-12, October.
   26. Templ Matthias, 2015. "Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey," Journal of Official Statistics, Sciendo, vol. 31(4), pages 737-761, December.
   27. Ermanno Catullo & Antonio Palestrini & Ruggero Grilli & Mauro Gallegati, 2018. "Early warning indicators and macro-prudential policies: a credit network agent based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 81-115, April.
   28. Juris Breidaks, 2015. "Variance Estimation Using Package vardpoor in R," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 24-38, June.
   29. Matthias Templ & Alexander Kowarik & Bernhard Meindl, 2014. "Development and Current Practice in Using R at Statistics Austria," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 173-184, June.
   30. Templ, Matthias & Meindl, Bernhard & Kowarik, Alexander & Dupriez, Olivier, 2017. "Simulation of Synthetic Complex Data: The R Package simPop," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i10).

4. Andreas Alfons & Matthias Templ & Peter Filzmoser, 2013. "Robust estimation of economic indicators from survey samples based on Pareto tail modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 271-286, March.

   Cited by:

   1. Arthur Charpentier & Emmanuel Flachaire, 2022. "Pareto models for top incomes and wealth," Post-Print hal-03649428, HAL.
      • Arthur Charpentier & Emmanuel Flachaire, 2022. "Pareto models for top incomes and wealth," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 1-25, March.
   2. Winkelried, Diego & Escobar, Bruno, 2020. "Declining inequality in Latin America? Robustness checks for Peru," MPRA Paper 106566, University Library of Munich, Germany.
      • Diego Winkelried & Bruno Escobar, 2022. "Declining inequality in Latin America? Robustness checks for Peru," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 223-243, March.
   3. Kris Boudt & Valentin Todorov & Wenjing Wang, 2020. "Robust Distribution-Based Winsorization in Composite Indicators Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(2), pages 375-397, June.
   4. Bluhm, Richard & Krause, Melanie, 2018. "Top Lights: Bright cities and their contribution to economic development," MERIT Working Papers 2018-041, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
      • Bluhm, Richard & Krause, Melanie, 2022. "Top lights: Bright cities and their contribution to economic development," Journal of Development Economics, Elsevier, vol. 157(C).
      • Richard Bluhm & Melanie Krause, 2020. "Top Lights: Bright cities and their contribution to economic development," SoDa Laboratories Working Paper Series 2020-08, Monash University, SoDa Laboratories.
      • Krause, Melanie & Bluhm, Richard, 2016. "Top Lights - Bright Spots and their Contribution to Economic Development," VfS Annual Conference 2016 (Augsburg): Demographic Change 145773, Verein für Socialpolitik / German Economic Association.
      • Richard Bluhm & Melanie Krause, 2018. "Top Lights - Bright Cities and their Contribution to Economic Development," CESifo Working Paper Series 7411, CESifo.
   5. Xavier Jara & Nicolás Oliva, 2018. "Top income adjustments and tax reforms in Ecuador," WIDER Working Paper Series wp-2018-165, World Institute for Development Economic Research (UNU-WIDER).
   6. Burkhauser, Richard V. & Herault, Nicolas & Jenkins, Stephen P. & Wilkins, Roger, 2017. "Survey Under-Coverage of Top Incomes and Estimation of Inequality: What Is the Role of the UK's SPI Adjustment?," IZA Discussion Papers 10868, Institute of Labor Economics (IZA).
      • Richard V. Burkhauser & Nicolas Hérault & Stephen P. Jenkins & Roger Wilkins, 2018. "Survey Under‐Coverage of Top Incomes and Estimation of Inequality: What is the Role of the UK's SPI Adjustment?," Fiscal Studies, John Wiley & Sons, vol. 39(2), pages 213-240, June.
      • Burkhauser, Richard V. & Hérault, Nicolas & Jenkins, Stephen P. & Wilkins, Roger, 2018. "Survey under-coverage of top incomes and estimation of inequality: what is the role of the UK’s SPI adjustment?," LSE Research Online Documents on Economics 84038, London School of Economics and Political Science, LSE Library.
      • Richard V. Burkhauser & Nicolas Hérault & Stephen P. Jenkins & Roger Wilkins, 2017. "Survey Under-Coverage of Top Incomes and Estimation of Inequality: What is the Role of the UK’s SPI Adjustment?," NBER Working Papers 23539, National Bureau of Economic Research, Inc.
   7. Brzeziński, Michał & Myck, Michal & Najsztub, Mateusz, 2019. "Reevaluating Distributional Consequences of the Transition to Market Economy in Poland: New Results from Combined Household Survey and Tax Return Data," IZA Discussion Papers 12734, Institute of Labor Economics (IZA).
      • Michał Brzeziński & Michał Myck & Mateusz Najsztub, 2019. "Reevaluating distributional consequences of the transition to market economy in Poland: new results from combined household survey and tax return data," Working Papers 2019-18, Faculty of Economic Sciences, University of Warsaw.
   8. P. Jenkins, Stephen & Hérault, Nicolas & V. Burkhauser, Richard & Wilkins, Roger, 2017. "Survey under-coverage of top incomes and estimation of inequality: what is the role of the UK’s SPI adjustment?," ISER Working Paper Series 2017-08, Institute for Social and Economic Research.
      • Richard V. Burkhauser & Nicolas Hérault & Stephen P. Jenkins & Roger Wilkins, 2017. "Survey Under-Coverage of Top Incomes and Estimation of Inequality: What Is the Role of the UK’s SPI Adjustment?," Melbourne Institute Working Paper Series wp2017n16, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
   9. Li, Chengyou & Yu, Yangcheng & Li, Qinghai, 2021. "Top-income data and income inequality correction in China," Economic Modelling, Elsevier, vol. 97(C), pages 210-219.
   10. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
       • Vladimir Hlasny, 2020. "Parametric Representation of the Top of Income Distributions: Options, Historical Evidence and Model Selection," Commitment to Equity (CEQ) Working Paper Series 90, Tulane University, Department of Economics.
       • Vladimir Hlasny, 2020. "Parametric Representation of the Top of Income Distributions: Options, Historical Evidence and Model Selection," Working Papers 547, ECINEQ, Society for the Study of Economic Inequality.
   11. Nora Lustig, 2018. "Measuring the Distribution of Household Income, Consumption and Wealth: State of Play and Measurement Challenges," Working Papers 1801, Tulane University, Department of Economics.
   12. Tobias Schoch & André Müller, 2020. "Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 267-304, December.
   13. SOLOGON Denisa & VAN KERM Philippe, 2014. "Earnings dynamics, foreign workers and the stability of inequality trends in Luxembourg 1988-2009," LISER Working Paper Series 2014-03, Luxembourg Institute of Socio-Economic Research (LISER).
   14. Chakraborty, Robin & Waltl, Sofie R., 2018. "Missing the wealthy in the HFCS: micro problems with macro implications," Working Paper Series 2163, European Central Bank.
       • Sofie R. Waltl & Robin Chakraborty, 2022. "Missing the wealthy in the HFCS: micro problems with macro implications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 169-203, March.
   15. Jenkins, Stephen P., 2017. "Pareto models, top incomes, and recent trends in UK income inequality," LSE Research Online Documents on Economics 67667, London School of Economics and Political Science, LSE Library.
       • P. Jenkins, Stephen, 2016. "Pareto models, top incomes, and recent trends in UK income inequality," ISER Working Paper Series 2016-07, Institute for Social and Economic Research.
       • Jenkins, Stephen P., 2016. "Pareto Models, Top Incomes, and Recent Trends in UK Income Inequality," IZA Discussion Papers 10124, Institute of Labor Economics (IZA).
       • Stephen P Jenkins, 2016. "Pareto models, top incomes, and recent trends in UK income inequality," STICERD - Public Economics Programme Discussion Papers 30, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
       • Stephen P. Jenkins, 2017. "Pareto Models, Top Incomes and Recent Trends in UK Income Inequality," Economica, London School of Economics and Political Science, vol. 84(334), pages 261-289, April.
   16. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman, 2018. "A robust semi-parametric approach for measuring income inequality in Malaysia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1-13.
   17. Frank A. Cowell & Philippe Kerm, 2015. "Wealth Inequality: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(4), pages 671-710, September.
   18. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
   19. Maria Marino & Benedetto Rocchi & Simone Severini, 2021. "Conditional Income Disparity between Farm and Non‐farm Households in the European Union: A Longitudinal Analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(2), pages 589-606, June.
   20. Nora Lustig, 2020. "The ``missing rich'' in household surveys: causes and correction approaches," Working Papers 520, ECINEQ, Society for the Study of Economic Inequality.
   21. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2021. "Measuring income inequality: A robust semi-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
   22. Silvia De Nicol`o & Maria Rosaria Ferrante & Silvia Pacei, 2021. "Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples," Papers 2107.08950, arXiv.org, revised May 2023.
   23. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2019. "A robust and efficient estimator for the tail index of inverse Pareto distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 431-439.
   24. Jan Pablo Burgard & Joscha Krause & Dennis Kreber & Domingo Morales, 2021. "The generalized equivalence of regularization and min–max robustification in linear mixed models," Statistical Papers, Springer, vol. 62(6), pages 2857-2883, December.
   25. , Stone Center & Lustig, Nora, 2020. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," SocArXiv j23pn, Center for Open Science.
       • Nora Lustig, 2019. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," Commitment to Equity (CEQ) Working Paper Series 75, Tulane University, Department of Economics.
   26. Benedetto Rocchi & Maria Marino & Simone Severini, 2021. "Does an Income Gap between Farm and Nonfarm Households Still Exist? The Case of the European Union," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(4), pages 1672-1697, December.
   27. Templ Matthias, 2015. "Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey," Journal of Official Statistics, Sciendo, vol. 31(4), pages 737-761, December.
   28. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman, 2018. "Optimal threshold for Pareto tail modelling in the presence of outliers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 169-180.
   29. Michał Brzeziński, 2013. "Robust estimation of the Pareto index: A Monte Carlo Analysis," Working Papers 2013-32, Faculty of Economic Sciences, University of Warsaw.
   30. Matthias Templ & Alexander Kowarik & Bernhard Meindl, 2014. "Development and Current Practice in Using R at Statistics Austria," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 173-184, June.

5. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," 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. 6(1), pages 29-47, April.

   Cited by:

   1. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
   2. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
   3. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
   4. Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.
   5. Elena Catanese, 2016. "Data Editing for Complex Surveys in Presence Of Administrative Data: An Application to Fss 2013 Livestock Survey Data Based on The Joint Sequential Use Of Different R Packages," Romanian Statistical Review, Romanian Statistical Review, vol. 64(2), pages 101-117, June.
   6. Matthias Templ & Alexander Kowarik & Bernhard Meindl, 2014. "Development and Current Practice in Using R at Statistics Austria," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 173-184, June.

6. Andreas Alfons & Stefan Kraft & Matthias Templ & Peter Filzmoser, 2011. "Simulation of close-to-reality population data for household surveys with application to EU-SILC," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 383-407, August.

   Cited by:

   1. Beat Hulliger & Tobias Schoch, 2014. "Robust, distribution-free inference for income share ratios under complex sampling," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(1), pages 63-85, January.
   2. Dana R. Thomson & Lieke Kools & Warren C. Jochem, 2018. "Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia," Data, MDPI, vol. 3(3), pages 1-19, August.
   3. Burgard Jan Pablo & Dieckmann Hanna & Krause Joscha & Merkle Hariolf & Münnich Ralf & Neufang Kristina M. & Schmaus Simon, 2020. "A generic business process model for conducting microsimulation studies," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 191-211, August.
   4. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
   5. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
   6. Templ, Matthias & Kowarik, Alexander & Meindl, Bernhard, 2015. "Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i04).
   7. Alfons, Andreas & Templ, Matthias & Filzmoser, Peter, 2010. "An Object-Oriented Framework for Statistical Simulation: The R Package simFrame," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i03).
   8. ANTONI Jean-Philippe & VUIDEL Gilles & KLEIN Olivier, 2017. "Generating a located synthetic population of individuals, households, and dwellings," LISER Working Paper Series 2017-07, Luxembourg Institute of Socio-Economic Research (LISER).
   9. Templ Matthias, 2015. "Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey," Journal of Official Statistics, Sciendo, vol. 31(4), pages 737-761, December.
   10. Amanda M. Y. Chu & Benson S. Y. Lam & Agnes Tiwari & Mike K. P. So, 2019. "An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research," IJERPH, MDPI, vol. 16(22), pages 1-17, November.
   11. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).
   12. Templ, Matthias & Meindl, Bernhard & Kowarik, Alexander & Dupriez, Olivier, 2017. "Simulation of Synthetic Complex Data: The R Package simPop," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i10).

7. Andreas Alfons & Wolfgang Baaske & Peter Filzmoser & Wolfgang Mader & Roland Wieser, 2011. "Robust variable selection with application to quality of life research," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 65-82, March.

   Cited by:

   1. Cerioli, Andrea & Farcomeni, Alessio & Riani, Marco, 2013. "Robust distances for outlier-free goodness-of-fit testing," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 29-45.
   2. Julie Poláčková & Andrea Jindrová, 2011. "Assessment of subjective aspects of the quality of life in the various regions of the Czech Republic," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(7), pages 267-274.
   3. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.

8. Alfons, Andreas & Templ, Matthias & Filzmoser, Peter, 2010. "An Object-Oriented Framework for Statistical Simulation: The R Package simFrame," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i03).

   Cited by:

   1. Kreutzmann, Ann-Kristin & Pannier, Sören & Rojas-Perilla, Natalia & Schmid, Timo & Templ, Matthias & Tzavidis, Nikos, 2017. "The R package emdi for estimating and mapping regionally disaggregated indicators," Discussion Papers 2017/15, Free University Berlin, School of Business & Economics.
   2. Hofert, Marius & Mächler, Martin, 2016. "Parallel and Other Simulations in R Made Easy: An End-to-End Study," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i04).
   3. Andrey Ziyatdinov & Alexandre Perera-Lluna, 2014. "Data Simulation in Machine Olfaction with the R Package Chemosensors," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-19, February.
   4. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
   5. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
   6. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
   7. Silvia De Nicol`o & Maria Rosaria Ferrante & Silvia Pacei, 2021. "Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples," Papers 2107.08950, arXiv.org, revised May 2023.
   8. Antonio Arcos & Maria del Mar Rueda & Sara Pasadas-del-Amo, 2020. "Treating Nonresponse in Probability-Based Online Panels through Calibration: Empirical Evidence from a Survey of Political Decision-Making Procedures," Mathematics, MDPI, vol. 8(3), pages 1-16, March.
   9. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.
   10. Zimmermann Thomas & Münnich Ralf Thomas, 2018. "Small Area Estimation with a Lognormal Mixed Model under Informative Sampling," Journal of Official Statistics, Sciendo, vol. 34(2), pages 523-542, June.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Andreas Alfons should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.