Quick and Complete Convergence in the Law of Large Numbers with Applications to Statistics

Author:

Tartakovsky Alexander G.1ORCID

Affiliation:

1. AGT StatConsult, 71 Cypress Way, Rolling Hills Estates, CA 90274, USA

Abstract

In the first part of this article, we discuss and generalize the complete convergence introduced by Hsu and Robbins in 1947 to the r-complete convergence introduced by Tartakovsky in 1998. We also establish its relation to the r-quick convergence first introduced by Strassen in 1967 and extensively studied by Lai. Our work is motivated by various statistical problems, mostly in sequential analysis. As we show in the second part, generalizing and studying these convergence modes is important not only in probability theory but also to solve challenging statistical problems in hypothesis testing and changepoint detection for general stochastic non-i.i.d. models.

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference58 articles.

1. Complete convergence and the law of large numbers;Hsu;Proc. Natl. Acad. Sci. USA,1947

2. Convergence rates in the law of large numbers;Baum;Trans. Am. Math. Soc.,1965

3. Le Cam, L.M., and Neyman, J. (1967). Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, San Diego, CA, USA, 21 June–18 July 1965 and 27 December 1965–7 January 1966, University of California Press. Vol. 2: Contributions to Probability Theory. Part 1.

4. Asymptotic optimality of certain multihypothesis sequential tests: Non-i.i.d. case;Tartakovsky;Stat. Inference Stoch. Process.,1998

5. Tartakovsky, A.G. (2020). Sequential Change Detection and Hypothesis Testing: General Non-i.i.d. Stochastic Models and Asymptotically Optimal Rules, Chapman & Hall/CRC Press, Taylor & Francis Group. Monographs on Statistics and Applied Probability 165.

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