Open Access
2014 A general approach to the joint asymptotic analysis of statistics from sub-samples
Stanislav Volgushev, Xiaofeng Shao
Electron. J. Statist. 8(1): 390-431 (2014). DOI: 10.1214/14-EJS888


In time series analysis, statistics based on collections of estimators computed from subsamples play a crucial role in an increasing variety of important applications. Proving results about the joint asymptotic distribution of such statistics is challenging, since it typically involves a nontrivial verification of technical conditions and tedious case-by-case asymptotic analysis. In this paper, we provide a novel technique that allows to circumvent those problems in a general setting. Our approach consists of two major steps: a probabilistic part which is mainly concerned with weak convergence of sequential empirical processes, and an analytic part providing general ways to extend this weak convergence to functionals of the sequential empirical process. Our theory provides a unified treatment of asymptotic distributions for a large class of statistics, including recently proposed self-normalized statistics and sub-sampling based p-values. In addition, we comment on the consistency of bootstrap procedures and obtain general results on compact differentiability of certain mappings that are of independent interest.


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Stanislav Volgushev. Xiaofeng Shao. "A general approach to the joint asymptotic analysis of statistics from sub-samples." Electron. J. Statist. 8 (1) 390 - 431, 2014.


Published: 2014
First available in Project Euclid: 18 April 2014

zbMATH: 1294.62023
MathSciNet: MR3195121
Digital Object Identifier: 10.1214/14-EJS888

Primary: 62E20 , 62G09 , 62G15 , 62M10
Secondary: 62G20

Keywords: change point , compact differentiability , Empirical processes , self-normalization , sub-sampling , time series , weak convergence

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.8 • No. 1 • 2014
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