IDEAS home Printed from https://ideas.repec.org/r/jss/jstsof/v054i15.html
   My bibliography  Save this item

Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


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. 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).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.