On Order Statistics for GS-Distributions

In this article, a class of distributions is used to establish several recurrence relations satisfied by single and product moments of order statistics and progressive Type-II right censoring. The recurrence relations for moments of some specific distributions including uniform (a;b); exponential (λ); generalized exponential (α;λ;ν); beta (1;b); beta (b;1); logistic (α;β) and other distributions from order statistics and progressive Type-II right censoring can be obtained as special cases. A short explanation of GS-distribution can be found in reference [27]. As an example, means, variances and covariances for standard exponential distribution of progressive Type-II right censored order statistics are computed. Various characterizations of the recently introduced GS-distributions are presented. These characterizations are based on a simple relationship between two truncated moments ; on hazard function ; and on functions of order statistics. A characterization of the GS-distributions based on conditional moment of order statistics is extended to truncated moment of order statistics

A Bernstein-von Mises theorem in the nonparametric right-censoring model

In the recent Bayesian nonparametric literature, many examples have been reported in which Bayesian estimators and posterior distributions do not achieve the optimal convergence rate, indicating that the Bernstein-von Mises theorem does not hold. In this article, we give a positive result in this direction by showing that the Bernstein-von Mises theorem holds in survival models for a large class of prior processes neutral to the right. We also show that, for an arbitrarily given convergence rate n^{-\alpha} with 0<\alpha \leq 1/2, a prior process neutral to the right can be chosen so that its posterior distribution achieves the convergence rate n^{-\alpha}.Comment: Published by the Institute of Mathematical Statistics (http://www.imstat.org) in the Annals of Statistics (http://www.imstat.org/aos/) at http://dx.doi.org/10.1214/00905360400000052

Measuring wage inequality under right censoring

A conditional tail index estimator is introduced which explicitly allows for right-tail censoring (top-coding). We show that the factor values used to adjust top-coded wages have changed over time and depend on individuals' characteristics, occupations and industries, and propose suitable values. Specifically, contrasting the results of our approach with those of a conservative fixed adjustment factor of 1.5 (used in the literature), suggests that wage inequality in 2017 measured with the Gini coefficient is larger than that suggested by the fixed adjustment factor.info:eu-repo/semantics/publishedVersio

Measuring wage inequality under right censoring

In this paper we investigate potential changes which may have occurred over the last two decades in the probability mass of the right tail of the wage distribution, through the analysis of the corresponding tail index. In specific, a conditional tail index estimator is introduced which explicitly allows for right tail censoring (top-coding), which is a feature of the widely used current population survey (CPS), as well as of other surveys. Ignoring the top-coding may lead to inconsistent estimates of the tail index and to under or over statements of inequality and of its evolution over time. Thus, having a tail index estimator that explicitly accounts for this sample characteristic is of importance to better understand and compute the tail index dynamics in the censored right tail of the wage distribution. The contribution of this paper is threefold: i) we introduce a conditional tail index estimator that explicitly handles the top-coding problem, and evaluate its finite sample performance and compare it with competing methods; ii) we highlight that the factor values used to adjust the top-coded wage have changed over time and depend on the characteristics of individuals, occupations and industries, and propose suitable values; and iii) we provide an in-depth empirical analysis of the dynamics of the US wage distribution's right tail using the public-use CPS database from 1992 to 2017

Bootstrapping the Conditional Moment Test for Parametric Duration Models

This letter evaluates the performance of auxiliary regression-based specification tests for parametric duration models estimated with censored data. The test using asymptotic critical values has poor size. Bootstrapping corrects the size problem but results in a biased power curve.conditional moment test, test size, right censoring, type I censoring, duration analysis, exponential distribution, Weibull distribution, specification test, power curve, bootstrap bias