[
Beach, C.M. and R. Davidson. 1983. “Distribution-free statistical inference with Lorenz curves and income shares.” The Review of Economic Studies 50(4): 723–735. DOI: https://doi.org/10.2307/2297772.10.2307/2297772
]Search in Google Scholar
[
Berger, Y.G. 2008. “A note on the asymptotic equivalence of jackknife and linearization variance estimation for the Gini coefficient.” Journal of Official Statistics 24-4: 541 – 555. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/a-note-on-the-asymptotic-equivalence-of-jackknife-and-linearization-variance-estimation-for-the-gini-coefficient.pdf (accessed April 2020).
]Search in Google Scholar
[
Berger, Y.G. 2018a. “An empirical likelihood approach under cluster sampling with missing observations.” Annals of the Institute of Statistical Mathematics. DOI: https://doi.org/10.1007/s10463-018-0681-x.10.1007/s10463-018-0681-x
]Search in Google Scholar
[
Berger, Y.G. 2018b. “Empirical likelihood approaches in survey sampling.” The Survey Statistician 78: 22–31. Available at: http://isi-iass.org/home/wp-content/uploads/N78-2018-07-ISSN.pdf (accessed April 2020).
]Search in Google Scholar
[
Berger, Y.G. and O.D.L.R. Torres. 2012. “A unified theory of empirical likelihood ratio confidence intervals for survey data with unequal probabilities.” Proceedings of the Survey Research Method Section of the American Statistical Association, Joint Statistical Meetings, San Diego: 15. Available at: http://www.asasrms.org/Proceedings (accessed November 2019).
]Search in Google Scholar
[
Berger, Y.G. and O.D.L.R. Torres. 2014. “Empirical likelihood confidence intervals: an application to the EU-SILC household surveys.” In Contribution to Sampling Statistics, Contribution to Statistics, edited by F. Mecatti, P.L. Conti, and M.G. Ranalli, 65–84. Springer.10.1007/978-3-319-05320-2_5
]Search in Google Scholar
[
Berger, Y.G. and O.D.L.R. Torres. 2016. “An empirical likelihood approach for inference under complex sampling design.” Journal of the Royal Statistical Society Series B 78(2): 319–341. DOI: https://doi.org/10.1111/rssb.12115.10.1111/rssb.12115
]Search in Google Scholar
[
Bhattacharya, D. 2007. “Inference on inequality from household survey data.” Journal of Econometrics 137(2): 674–707. DOI: https://doi.org/10.1016/j.jeconom.2005.09.003.10.1016/j.jeconom.2005.09.003
]Search in Google Scholar
[
Binder, D.A. and M.S. Kovaćević. 1995. “Estimating some measure of income inequality from survey data: an application of the estimating equation approach.” Survey Methodology 21(2): 137–145. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X199500214396 (accessed April 2020).
]Search in Google Scholar
[
Bishop, J., J.P. Formby, and B. Zheng. 1997. “Statistical inference and the Sen index of poverty.” International Economic Review 381–387. DOI: https://doi.org/10.2307/2527379.10.2307/2527379
]Search in Google Scholar
[
Brent, R.P. 1973. Algorithms for Minimization without Derivatives. New-Jersey: Prentice-Hall. ISBN 0-13-022335-2.
]Search in Google Scholar
[
Davidson, R. 2009. “Reliable inference for the Gini index.” Journal of Econometrics 150(1): 30–40. DOI: https://doi.org/10.1016/j.jeconom.2008.11.004.10.1016/j.jeconom.2008.11.004
]Search in Google Scholar
[
Dekker, T.J. 1969. “Finding a zero by means of successive linear interpolation.” In Constructive Aspects of the Fundamental Theorem of Algebra, edited by B. Dejon and P. Henrici, 37–489. Handbook of Statistics, London: Wiley-Interscience.
]Search in Google Scholar
[
Gajdos, T. and J.A. Weymark. 2005. “Multidimensional generalized Gini indices.” Economic Theory 26(3): 471–496. DOI: https://doi.org/10.1007/s00199-004-0529-x.10.1007/s00199-004-0529-x
]Search in Google Scholar
[
Gastwirth, J.L. and M. Gail. 1985. “Simple asymptotically distribution-free methods for comparing Lorenz curves and Gini indices obtained from complete data.” Advances in Econometrics 4: 229–243. Available at: https://scholar.google.com/scholar_lookup?title=Simple%20Asymptotically%20Distribution-free%20Methods%20for%20Comparing%20Lorenz%20Curves%20and%20Gini%20Indices%20Obtained%20from%20Complete%20Data&author=JL.%20Gastwirth&author=MH.%20Gail&pages=229-243&publication_year=1985 (accessed April 2020).
]Search in Google Scholar
[
Giles, D. 2004. “Calculating a standard error for the Gini coefficient: some further results.” Oxford Bulletin of Economics and Statistics 66(3): 425–433. DOI: https://doi.org/10.1111/j.1468-0084.2004.00086.x.10.1111/j.1468-0084.2004.00086.x
]Search in Google Scholar
[
Gini, C. 1914. “Sulla Misura Della Concentrazione e Cella Variabilitá dei Caratteri.” Atti del Reale Istituto veneto di scienze, lettere ed arti, 73: 1203–1248. Available at: https://EconPapers.repec.org/RePEc:mtn:ancoec:0501 (accessed April 2020).
]Search in Google Scholar
[
Giorgi, G.M. and C. Gigliarano. 2017. “The Gini concentration index: a review of the inference literature.” Journal of Economic Surveys 31(4): 1130–1148. DOI: https://doi.org/10.1111/joes.12185.10.1111/joes.12185
]Search in Google Scholar
[
Glasser, G.J. 1962. “Variance formulas for the mean difference and coefficient of concentration.” Journal of the American Statistical Association 57(299): 648–654. DOI: https://doi.org/10.1080/01621459.1962.10500553.10.1080/01621459.1962.10500553
]Search in Google Scholar
[
Hájek, J. 1971. “Comment on a paper by Basu, D.” In Foundations of Statistical Inference, edited by V.P. Godambe and D.A. Sprott, 236. Toronto: Holt, Rinehart & Winston.10.2307/2334645
]Search in Google Scholar
[
Hartley, H.O. and J.N.K. Rao. 1962. “Sampling with unequal probabilities without replacement.” The Annals of Mathematical Statistics 33: 350–374. DOI: https://doi.org/10.1214/aoms/1177704564.10.1214/aoms/1177704564
]Search in Google Scholar
[
Hoeffding, W. 1948. “A non-parametric test of independence.” The annals of Mathematical Statistics, 546–557. DOI: https://doi.org/10.1214/aoms/1177730150.10.1214/aoms/1177730150
]Search in Google Scholar
[
Karagiannis, E. and M. Kovaćević. 2000. “A method to calculate the Jack-250 knife variance estimator for the Gini coefficient.” Oxford Bulletin of Economics and Statistics 62(1): 119–122. DOI: https://doi.org/10.1111/1468-0084.00163.10.1111/1468-0084.00163
]Search in Google Scholar
[
Koshevoy, G. and K. Mosler. 1997. “Multivariate Gini indices.” Journal of Multivariate Analysis 60(2): 252–276. DOI: https://doi.org/10.1006/jmva.1996.1655.10.1006/jmva.1996.1655
]Search in Google Scholar
[
Langel, M. and Y. Tillé. 2013. “Variance estimation of the Gini index: revisiting a result several times published.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 176(2): 521–540. DOI: https://doi.org/10.1111/j.1467-985X.2012.01048.x.10.1111/j.1467-985X.2012.01048.x
]Search in Google Scholar
[
Lorenz, M.O. 1905. “Methods of measuring the concentration of wealth.” Publications of the American Statistical Association 9(70): 209–219. DOI: https://doi.org/10.2307/2276207.10.2307/2276207
]Search in Google Scholar
[
Mills, J.A. and S. Zandvakili. 1997. “Statistical inference via bootstrapping for measures of inequality.” Journal of Applied Econometrics 12(2): 133 – 150. DOI: https://doi.org/10.1002/(SICI)1099-1255(199703)12:2133:AID-JAE4333.0.CO;2-H.
]Search in Google Scholar
[
Modarres, R. and J.L. Gastwirth. 2006. “A cautionary note on estimating the standard error of the Gini index of inequality.” Oxford Bulletin of Economics and Statistics 68(3): 385–390. DOI: https://doi.org/10.1111/j.1468-0084.2006.00167.x.10.1111/j.1468-0084.2006.00167.x
]Search in Google Scholar
[
Nair, U.S. 1936. “The standard error of Gini’s mean difference.” Biometrika 28(3/4): 428–436. DOI: https://doi.org/10.1093/biomet/28.3-4.428.10.1093/biomet/28.3-4.428
]Search in Google Scholar
[
Neyman, J. 1938. “On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection.” Journal of the Royal Statistical Society 97(4): 558–625. DOI: https://doi.org/10.2307/2342192.10.2307/2342192
]Search in Google Scholar
[
Nygård, F. and A. Sandström. 1989. “Income inequality measures based on sample surveys.” Journal of Econometrics 42(1): 81–95. DOI: https://doi.org/10.1016/0304-4076(89)90077-8.10.1016/0304-4076(89)90077-8
]Search in Google Scholar
[
Ogwang, T. 2000. “A convenient method of computing the Gini index and its standard error.” Oxford Bulletin of Economics and Statistics 62(1): 123 – 129. DOI: https://doi.org/10.1111/1468-0084.00164.10.1111/1468-0084.00164
]Search in Google Scholar
[
Owen, A.B. 1988. “Empirical Likelihood Ratio Confidence Intervals for a Single Functional.” Biometrika 75(2): 237 – 249. DOI: https://doi.org/10.1093/biomet/75.2.237.10.1093/biomet/75.2.237
]Search in Google Scholar
[
Peng, L. 2011. “Empirical likelihood methods for the Gini index.” Australian & New Zealand Journal of Statistics 53(2): 131–139. DOI: https://doi.org/10.1111/j.1467-842X.2011.00614.x.10.1111/j.1467-842X.2011.00614.x
]Search in Google Scholar
[
Qin, Y., J.N.K. Rao, and C. Wu. 2010. “Empirical likelihood confidence intervals for the Gini measure of income inequality.” Economic Modelling 27: 1429–1435. DOI: https://doi.org/10.1016/j.econmod.2010.07.015.10.1016/j.econmod.2010.07.015
]Search in Google Scholar
[
Rao, J.N.K., C.F.J. Wu, and K. Yue. 1992. “Some recent work on resampling methods for complex surveys.” Survey Methodology 18: 209–217. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X199200214486 (accessed April 2020).
]Search in Google Scholar
[
Rust, K.F. and J.N.K. Rao. 1996. “Variance estimation for complex surveys using replication techniques.” Biometrika 5(3): 281310. DOI: https://doi.org/10.1177/096228029600500305.10.1177/0962280296005003058931197
]Search in Google Scholar
[
Sandström, A., B. Waldén, and J.H. Wretman. 1985. Variance estimators of the Gini coefficient: simple random sampling. Sweden. Available at: https://www.scb.se/contentassets/7004f1a7effe4e8690548165695b1864/pm-p-1985-17-green.pdf (accessed April 2020).
]Search in Google Scholar
[
Sandström, A., J.H. Wretman, and B. Waldén. 1988. “Variance estimators of the Gini coefficient – probability sampling.” Journal of Business & Economic Statistics 6(1): 113–119. DOI: https://doi.org/10.1080/07350015.1988.10509643.10.1080/07350015.1988.10509643
]Search in Google Scholar
[
Schezhtman, E. and S. Yitzhaki. 1987. “A Measure of Association Based on Gini’s Mean Difference.” Communications in Statistics-Theory and Methods 16(1): 207–231. DOI: https://doi.org/10.1080/03610928708829359.10.1080/03610928708829359
]Search in Google Scholar
[
Sendler, W. 1979. “On statistical inference in concentration measurement.” Metrika 26(1): 109–122. DOI: https://doi.org/10.1007/BF01893478.10.1007/BF01893478
]Search in Google Scholar
[
Shao, J. 1994. “L-Statistics in Complex Survey Problems.” The Annals of Statistics 22(2): 946–967. DOI: https://doi.org/10.1214/aos/1176325505.10.1214/aos/1176325505
]Search in Google Scholar
[
Wu, C. and J.N.K. Rao. 2006. “Pseudo-empirical likelihood ratio confidence intervals for complex surveys.” Canadian Journal of Statistics 34(3): 359–375. DOI: https://doi.org/10.1002/cjs.5550340301.10.1002/cjs.5550340301
]Search in Google Scholar
[
Yitzhaki, S. 1991. “Calculating jackknife variance estimators for parameters of the Gini method.” Journal of Business & Economic Statistics 9(2): 235 – 239. DOI: https://doi.org/10.1080/07350015.1991.10509849.10.1080/07350015.1991.10509849
]Search in Google Scholar
[
Yitzhaki, S. 1998. “More than a dozen alternative ways of spelling Gini.” Research on Economic Inequality 8: 13–30. DOI: https://doi.org/10.1.1.365.4196.
]Search in Google Scholar
