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September, 1986 The Use of Subseries Values for Estimating the Variance of a General Statistic from a Stationary Sequence
Edward Carlstein
Ann. Statist. 14(3): 1171-1179 (September, 1986). DOI: 10.1214/aos/1176350057


Let $Z_i:-\infty<i <+\infty}$ be a strictly stationary $\infty$ -mixing sequence. Without specifying the dependence model giving rise to ${Z_i}$ and without specifying the marginal distribution of $Z_i$ , we address the question of variance estimation for a general statistic $t_n=t_n (Z_1,...,Z_n)$. For estimating $Var{t_n}$ from just the available data $(Z_1,...,Z_n)$ we propose computing subseries values: $t_m(Z_{i+1},Z_{i+2}, Z_{i+m})$, $0\leq i<i+m\leq n$. These subseries values are used as replicates to model the sampling variability of $t_n$. In particular, we use adjacent nonoverlapping subseries of length $m = m_n$, with $m_\rightarrow\ infty$ and $m_n/n\rigtharrow 0$. Our variance estimator is just the usual sample variance computed amongst these subseries values (after appropriate standardization). This estimator is shown to be consistent under mild integrability conditions. We present optimal (i.e.,minimum m.s.e.) choices of $m_n$ for the special case where $t_n=\overset{-}{Z}_n$ and ${Z_i}$ is a normal AR(1) sequence. A simulation study is conducted, showing that those same choices of $m_n$ are effective when $t_n$ is a robust estimator of location and ${Z_i}$ is subject to contamination.


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Edward Carlstein. "The Use of Subseries Values for Estimating the Variance of a General Statistic from a Stationary Sequence." Ann. Statist. 14 (3) 1171 - 1179, September, 1986.


Published: September, 1986
First available in Project Euclid: 12 April 2007

zbMATH: 0602.62029
MathSciNet: MR856813
Digital Object Identifier: 10.1214/aos/1176350057

Primary: 62G05
Secondary: 60G10

Keywords: $\infty$-mixing , Dependence , nonparametric , stationary , subseries , variance estimation

Rights: Copyright © 1986 Institute of Mathematical Statistics


Vol.14 • No. 3 • September, 1986
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