Results 1 to 10 of about 8,819 (162)

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

Asymptotic Normality of M-Estimator in Linear Regression Model with Asymptotically Almost Negatively Associated Errors

Mathematics, 2023
This paper studies a linear regression model in which the errors are asymptotically almost negatively associated (AANA, in short) random variables. Firstly, the central limit theorem for AANA sequences of random variables is established. Then, we use the
Yu Zhang
doaj   +1 more source

Asymptotic Normality in Linear Regression with Approximately Sparse Structure

Mathematics, 2022
In this paper, we study the asymptotic normality in high-dimensional linear regression. We focus on the case where the covariance matrix of the regression variables has a KMS structure, in asymptotic settings where the number of predictors, p, is ...
Saulius Jokubaitis, Remigijus Leipus
doaj   +1 more source

Asymptotic normality of recursive algorithms via martingale difference arrays [PDF]

Discrete Mathematics & Theoretical Computer Science, 2001
We propose martingale central limit theorems as an tool to prove asymptotic normality of the costs of certain recursive algorithms which are subjected to random input data.
Werner Schachinger
doaj   +2 more sources

Problems for combinatorial numbers satisfying a class of triangular arrays

Lietuvos Matematikos Rinkinys, 2023
Numbers satisfying a class of triangular arrays, defined by a bivariate first-order linear difference equation with linear coefficients, include a wide range of combinatorial numbers: binomial coefficients, Morgan numbers, Stirling numbers of the first ...
Igoris Belovas
doaj   +3 more sources