On the Baum–Katz theorem for randomly weighted sums of negatively associated random variables with general normalizing sequences and applications in some random design regression models

In this paper, we develop Jajte’s technique, which is used in the proof of strong laws of large numbers, to prove complete convergence for randomly weighted sums of negatively associated random variables. Based on a general normalizing function that satisfies some specific conditions, we give some g...

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Bibliographic Details
Published in:	Statistical papers (Berlin, Germany) Vol. 65; no. 3; pp. 1869 - 1900
Main Authors:	Ta Cong, Son, Tran Manh, Cuong, Bui Khanh, Hang, Le Van, Dung
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024 Springer Nature B.V
Subjects:	Convergence Economic Theory/Quantitative Economics/Mathematical Methods Economics Estimators Finance Insurance Management Mathematics and Statistics Operations Research/Decision Theory Probability Theory and Stochastic Processes Random variables Regression analysis Regression models Regular Article Statistics Statistics for Business Sums Theorems Weighting functions 62G05 Complete convergence Simple linear regression model with random design Non parametric regression model with random design Randomly weighted sum 62G20 60F15
ISSN:	0932-5026, 1613-9798
Online Access:	Get full text
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