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相关论文: A New measure of income inequality

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In this paper we derive inferential results for a new index of inequality, specifically defined for capturing significant changes observed both in the left and in the right tail of the income distributions. The latter shifts are an apparent…

统计理论 · 数学 2017-06-20 Youri Davydov , Francesca Greselin

Heavy-tailed distributions, such as the Cauchy distribution, are acknowledged for providing more accurate models for financial returns, as the normal distribution is deemed insufficient for capturing the significant fluctuations observed in…

统计理论 · 数学 2025-07-31 Ganesh Vishnu Avhad , Ananya Lahiri , Sudheesh K. Kattumannil

Widely used income inequality measure, Gini index is extended to form a family of income inequality measures known as Single-Series Gini (S-Gini) indices. In this study, we develop empirical likelihood (EL) and jackknife empirical…

统计方法学 · 统计学 2024-05-29 Sreelakshmi N , Sudheesh K Kattumannil , Rituparna Sen

In the present article, we discuss jackknife empirical likelihood (JEL) and adjusted jackknife empirical likelihood (AJEL) based inference for finding confidence intervals for probability weighted moment (PWM). We obtain the asymptotic…

统计方法学 · 统计学 2018-07-13 Deepesh Bhati , Sudheesh K Kattumannil , N Sreelakshmi

Semivariance is a measure of the dispersion of all observations that fall above the mean or target value of a random variable and it plays an important role in life-length, actuarial and income studies. In this paper, we develop a new…

统计方法学 · 统计学 2024-02-29 Saparya Suresh , Sudheesh K. Kattumannil

Log symmetric distributions are useful in modeling data which show high skewness and have found applications in various fields. Using a recent characterization for log symmetric distributions, we propose a goodness of fit test for testing…

统计方法学 · 统计学 2024-10-08 Anjana S , Sudheesh Kattumannil

We develop a jackknife empirical likelihood (JEL) framework for inference on parameters defined through multivariate three-sample U-statistic. From three independent multivariate samples, we construct JEL ratio statistic based on suitable…

统计方法学 · 统计学 2025-12-03 Naresh Garg , Litty Mathew , Isha Dewan , Sudheesh Kumar Kattumannil

Jackknife empirical likelihood (JEL) is an effective modified version of empirical likelihood method (EL). Through the construction of the jackknife pseudo-values, JEL overcomes the computational difficulty of EL method when its constraints…

统计方法学 · 统计学 2016-03-15 Ying-Ju Chen , Wei Ning

The Sen index and Sen-Shorrocks-Thon (SST) index are widely used measures of poverty indices. Developing reliable inference for these measures enables us to compare these measures in different populations of interest in an effective way. It…

统计方法学 · 统计学 2026-03-19 Sreelakshmi N , Saparya Suresh , Sudheesh K. Kattumannil

In this paper we develop a novel inferential approach based on geometric records for estimating the tail index of heavy-tailed distributions. We construct a maximum likelihood estimator for the Pareto model and establish its strong…

统计理论 · 数学 2026-04-30 Martín Alcalde , Raúl Gouet , Miguel Lafuente , F. Javier López , Gerardo Sanz

In many applications, parameters of interest are estimated by solving some non-smooth estimating equations with $U$-statistic structure. Jackknife empirical likelihood (JEL) approach can solve this problem efficiently by reducing the…

统计方法学 · 统计学 2019-06-18 Yongli Sang , Xin Dang , Yichuan Zhao

Survival extropy, which quantifies the uncertainty associated with the remaining lifetime distribution, provides an information-theoretic perspective on survival behavior. We consider a divergence measure based on survival extropy and…

统计理论 · 数学 2025-12-03 Naresh Garg , Isha Dewan , Sudheesh Kumar Kattumannil

The categorical Gini correlation, $\rho_g$, was proposed by Dang et al. to measure the dependence between a categorical variable, $Y$ , and a numerical variable, $X$. It has been shown that $\rho_g$ has more appealing properties than…

统计方法学 · 统计学 2023-10-17 Sameera Hewage , Yongli Sang

In the present article, we propose jackknife empirical likelihood (JEL) ratio test for testing the independence of time to failure and cause of failure in competing risks data. We use U-statistic theory to derive the JEL ratio test. The…

统计方法学 · 统计学 2021-10-19 Sreelakshmy N. , Sreedevi E. P

The notion of expectiles, originally introduced in the context of testing for homoscedasticity and conditional symmetry of the error distribution in linear regression, induces a law-invariant, coherent and elicitable risk measure that has…

统计方法学 · 统计学 2020-07-20 Simone A. Padoan , Gilles Stupfler

"The rich are getting richer" implies that the population income distributions are getting more right skewed and heavily tailed. For such distributions, the mean is not the best measure of the center, but the classical indices of income…

统计方法学 · 统计学 2023-08-08 Vytaras Brazauskas , Francesca Greselin , Ricardas Zitikis

In this paper, we obtain a new characterization result for symmetric distributions based on the entropy measure. Using the characterization, we propose a nonparametric test to test the symmetry of a distribution. We also develop the…

统计理论 · 数学 2025-05-14 Ganesh Vishnu Avhad , Ananya Lahiri , Sudheesh K. Kattumannil

We propose the so-called jackknife empirical likelihood approach for the survey data of general unequal probability sampling designs, and analyze parameters defined according to U-statistics. We prove theoretically that jackknife…

统计方法学 · 统计学 2023-03-28 Mengdong Shang , Xia Chen

We introduce a novel approach called the Bayesian Jackknife empirical likelihood method for analyzing survey data obtained from various unequal probability sampling designs. This method is particularly applicable to parameters described by…

统计方法学 · 统计学 2023-09-14 Mengdong Shang , Xia Chen

Bivariate extreme-value distributions have been used in modeling extremes in environmental sciences and risk management. An important issue is estimating the dependence function, such as the Pickands dependence function. Some estimators for…

统计理论 · 数学 2013-03-21 Liang Peng , Linyi Qian , Jingping Yang
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