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Asymptotic Normality for Plug-In Estimators of Generalized Shannon’s Entropy [PDF]

Entropy, 2022
Shannon’s entropy is one of the building blocks of information theory and an essential aspect of Machine Learning (ML) methods (e.g., Random Forests).
Jialin Zhang, Jingyi Shi
doaj   +2 more sources

Asymptotic Normality of Quadratic Estimators. [PDF]

Stoch Process Their Appl, 2016
We prove conditional asymptotic normality of a class of quadratic U-statistics that are dominated by their degenerate second order part and have kernels that change with the number of observations. These statistics arise in the construction of estimators in high-dimensional semi- and non-parametric models, and in the construction of nonparametric ...
Robins J, Li L, Tchetgen E, van der Vaart A.   +3 more
europepmc   +5 more sources

The asymptotic normality of internal estimator for nonparametric regression [PDF]

Journal of Inequalities and Applications, 2018
In this paper, we aim to study the asymptotic properties of internal estimator of nonparametric regression with independent and dependent data. Under some weak conditions, we present some results on asymptotic normality of the estimator.
Penghua Li, Xiaoqin Li, Liping Chen
doaj   +2 more sources

Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples [PDF]

Journal of Inequalities and Applications, 2018
In the present article, by utilizing some inequalities for linearly negative quadrant dependent random variables, we discuss the uniformly asymptotic normality of sample quantiles for linearly negative quadrant dependent samples under mild conditions ...
Xueping Hu, Rong Jiang, Keming Yu, Tong Zhang   +3 more
doaj   +2 more sources

On Posterior Asymptotic Normality and Asymptotic Normality of Estimators for the Galton-Watson Process

Journal of the Royal Statistical Society Series B: Statistical Methodology, 1987
SUMMARY For a Galton-Watson process with offspring distribution pθ, where θ is an unknown parameter, asymptotic posterior normality is established for θ and for the mean of the offspring distribution. A form of asymptotic normality for the mean of the offspring distribution is also obtained, without restriction on whether the process is ...
exaly   +3 more sources

Strategies for Asymptotic Normalization

CoRR, 2022
FSCD ...
Claudia Faggian, Giulio Guerrieri
openaire   +5 more sources

Asymptotic Behavior of a Nonparametric Estimator of the Renewal Function for Random Fields

Mathematics, 2023
In this paper, we study the asymptotic normality of a nonparametric estimator of the renewal function associated with a sequence of absolutely continuous nonnegative two-dimensional random fields.
Livasoa Andriamampionona, Victor Harison, Michel Harel   +2 more
doaj   +1 more source

Estimation of quantile regression model without longitudinal data and with auxiliary information

Xi'an Gongcheng Daxue xuebao, 2021
In order to study the estimation of the quantile regression model with missing longitudinal data and auxiliary information, the parameter estimation and asymptotic normality of linear quantile regression model are given by using inverse probability ...
Yuting ZHANG, Shuanghua LUO, Chengyi ZHANG   +2 more
doaj   +1 more source

Local asymptotic normality of statistical models of discrete martingales

Lietuvos Matematikos Rinkinys, 2023
We establish general conditions assuring the local asymptotic normality of statistical experiments of discrete or purely discontinuous local martingales obtained models of point processes of all types were found out.
Vaidotas Kanišauskas
doaj   +3 more sources

Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design

Entropy, 2022
The optimal subsampling is an statistical methodology for generalized linear models (GLMs) to make inference quickly about parameter estimation in massive data regression. Existing literature only considers bounded covariates.
Guangqiang Teng, Boping Tian, Yuanyuan Zhang, Sheng Fu   +3 more
doaj   +1 more source