QA362 : Complete Convergence for the Estimator of Nonparametric Regression Model baxsed on Negatively Superadditive-dependent Errors
Thesis > Central Library of Shahrood University > Mathematical Sciences > MSc > 2016
Authors:
Abstarct: Independence and dependence properties of random variables play a basic role in statistics and probability. In most situations, the random variables are not independent. Hence, we should study and use the different measures of dependence between random variables. In this thesis, particularly, we focus on negatively superadditive-dependence (NSD) structure and its applications. Moment inequalities could be used in many applications of probability, e.g. finding lower or upper bounds for moments which their exact values are not known. Two useful moment inequalities for NSD random variables are Rosenthal-type maximal and Kolmogorov-type exponential inequalities. In this thesis, by using the mentioned inequalities, we re-introduce the complete convergence of arrays of row-wise NSD random variables and the complete consistency for the estimator of nonparametric regression model baxsed on NSD errors. Then, we examine the performance of theoretical results by a simulation study.
Keywords:
#Negatively superadditive-dependent random variables #Rosenthal-type maximal inequality #Kolmogorov-type exponential inequality #complete convergence #complete consistency Link
Keeping place: Central Library of Shahrood University
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Keeping place: Central Library of Shahrood University
Visitor: