Income inequality estimators are biased in small samples, leading generally to an
underestimation. This aspect deserves particular attention when estimating inequality in …

We have used the generalized linearization technique based on the concept of influence
function, as Osier has done (Osier 2009), to estimate the variance of complex statistics such …

Ratios of quantiles are often computed for income distributions as rough measures of
inequality, and inference for such ratios has recently become available. The special case …

The problem of missing data is a common feature in any study, and a single imputation
method is often applied to deal with this problem. The first contribution of this paper is to …

Although distributional inequality and concentration are important statistical concepts in
many research fields (including economics, political and social science, information theory …

Over the past 50 years, crown asymmetry of forest trees has been evaluated through several
indices constructed from the perspective of projected crown shape or displacement but often …

In grouped data, the estimation of the Lorenz curve without taking into account the within-
class variability leads to an overestimation of the curve and an underestimation of the Gini …

Many parameters that describe poverty, social exclusion and inequality can take values in
the (0, 1) interval. This chapter discusses area level models for the estimation of these …

This paper proposes a deep generalisation for income inequality indices. A generalised
income inequality index that depends on two parameters and that involves a large set of …

Результаты углублённого анализа научных трудов позволили выявить проблемы
(ограничения, недостатки и противоречия), на базе которых сформулировано …