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The bias of an estimator is defined as the difference of its expected value from the parameter to be estimated, where the expectation is with respect to the model. Loosely speaking, small bias reflects the desire that if an experiment is…

Methodology · Statistics 2018-02-16 Ioannis Kosmidis

We consider the famous Rasch model, which is applied to psychometric surveys when n persons under test answer m questions. The score is given by a realization of a random binary (n,m)-matrix. Its (j,k)th component indicates whether or not…

Statistics Theory · Mathematics 2016-12-22 Friedrich Liese , Alexander Meister , Johanna Kappus

Mutual information is a measure of the dependence between random variables that has been used successfully in myriad applications in many fields. Generalized mutual information measures that go beyond classical Shannon mutual information…

Information Theory · Computer Science 2021-07-30 Kevin R. Moon , Kumar Sricharan , Alfred O. Hero

Population control is an essential component of any projector Monte Carlo algorithm. This control mechanism usually introduces a bias in the sampled quantities that is inversely proportional to the population size. In this paper, we…

Computational Physics · Physics 2021-04-28 Khaldoon Ghanem , Niklas Liebermann , Ali Alavi

This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used estimators such as the restricted maximum likelihood estimator.…

Methodology · Statistics 2021-05-18 Tatsuya Kubokawa , Shonosuke Sugasawa , Hiromasa Tamae , Sanjay Chaudhuri

The Gini score is a popular tool in statistical modeling and machine learning for model validation and model selection. It is a purely rank based score that allows one to assess risk rankings. The Gini score for statistical modeling has…

Machine Learning · Statistics 2025-11-20 Alexej Brauer , Mario V. Wüthrich

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits, and some variants are shown to be associated with multiple complex traits. Genetic covariance between two traits is defined…

Methodology · Statistics 2023-10-06 Jianqiao Wang , Sai Li , Hongzhe Li

We consider the problem of estimating the error variance in a general linear model when the error distribution is assumed to be spherically symmetric, but not necessary Gaussian. In particular we study the case of a scale mixture of…

Statistics Theory · Mathematics 2013-03-18 Yuzo Maruyama , William E. Strawderman

This paper proposes a class of estimators for population correlation coefficient when information about the population mean and population variance of one of the variables is not available but information about these parameters of another…

General Mathematics · Mathematics 2007-05-23 M. Khoshnevisan , F. Kaymarm , H. P. Singh , Rajesh Singh , Florentin Smarandache

The aim of survey statistics is to produce estimates with a minimal bias and a corresponding acceptable variance given a specific budget, preferable with a minor response burden for the participants. In recent years, considerable efforts…

Methodology · Statistics 2026-04-02 Martin Hyllienmark , Gustaf Strandell

We consider robust covariance estimation with group symmetry constraints. Non-Gaussian covariance estimation, e.g., Tyler scatter estimator and Multivariate Generalized Gaussian distribution methods, usually involve non-convex minimization…

Machine Learning · Statistics 2013-06-19 Ilya Soloveychik , Ami Wiesel

The aim of this paper is to establish the asymptotic behavior of the mutual influence of the Gini index and the poverty measures by using the Gaussian fields described in Mergane and Lo(2013). The results are given as representation…

Methodology · Statistics 2017-05-29 Gane Samb Lo , Pape Djiby Mergane , Tchilabalo Abozou Kpanzou

In this paper we propose a bimodal gamma distribution using a quadratic transformation based on the alpha-skew-normal model. We discuss several properties of this distribution such as mean, variance, moments, hazard rate and entropy…

Methodology · Statistics 2020-05-08 R. Vila , L. Ferreira , H. Saulo , F. Prataviera , E. M. M. Ortega

We derive a Bayesian framework for incorporating selection effects into population analyses. We allow for both measurement uncertainty in individual measurements and, crucially, for selection biases on the population of measurements, and…

Data Analysis, Statistics and Probability · Physics 2019-04-10 Ilya Mandel , Will M. Farr , Jonathan R. Gair

In this paper, we study the behavior of a kernel estimator of the regression function in the right censored model with $\alpha$-mixing data . The uniform strong consistency over a real compact set of the estimate is established along with a…

Statistics Theory · Mathematics 2008-02-21 Zohra Guessoum , Elias Ould-Saïd

In this paper we deal with the estimation of population variance of the study variable y using auxiliary information on variable x. A family of ratio and product-type estimators are proposed using suitable transformation on both random…

Statistics Theory · Mathematics 2013-09-16 Viplav K. Singh , Rajesh Singh

The key indicators of model stability are the population stability index (PSI), which uses the difference in population distribution, and the Kolmogorov-Smirnov statistic (KS) between two distributions. When deriving a binary choice model,…

Methodology · Statistics 2025-07-08 M. Pomazanov

In this paper we will show that the Gini coefficient and the introduced measure of angular inequality are special cases of a wider indexed family of measurements. We will discuss the properties of the defined class based, inter alia, on a…

Methodology · Statistics 2015-06-02 Piotr Dniestrzanski

There has been a growing interest in statistical inference from data satisfying the so-called manifold hypothesis, assuming data points in the high-dimensional ambient space to lie in close vicinity of a submanifold of much lower dimension.…

Methodology · Statistics 2023-01-04 Rong Tang , Yun Yang

When using incorrect or inaccurate signal models to perform parameter estimation on a gravitational wave signal, biased parameter estimates will in general be obtained. For a single event this bias may be consistent with the posterior, but…

General Relativity and Quantum Cosmology · Physics 2015-06-01 Jonathan R. Gair , Christopher J. Moore
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