Improved confidence regions for a mean vector under general conditions

Jin Xu; Arjun K. Gupta

doi:10.1016/j.csda.2005.10.011

Improved confidence regions for a mean vector under general conditions

Jin Xu, Arjun K. Gupta

Bowling Green State University

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

In this paper, we propose two different confidence regions for a mean vector under general conditions when the population of interest is nonnormal and the sample size is moderate. It is shown that both confidence regions will improve the accuracy of the approximation in the sense that the coverage error is of order o fenced(n^{- 1}). Monte Carlo examples are demonstrated to show the performance with comparison to the classical methods.

Original language	English
Pages (from-to)	1051-1062
Number of pages	12
Journal	Computational Statistics and Data Analysis
Volume	51
Issue number	2
DOIs	https://doi.org/10.1016/j.csda.2005.10.011
State	Published - 15 Nov 2006
Externally published	Yes

Keywords

Confidence region
Coverage probability
Edgeworth expansion
Normalization transformation

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10.1016/j.csda.2005.10.011

Cite this

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abstract = "In this paper, we propose two different confidence regions for a mean vector under general conditions when the population of interest is nonnormal and the sample size is moderate. It is shown that both confidence regions will improve the accuracy of the approximation in the sense that the coverage error is of order o fenced(n- 1). Monte Carlo examples are demonstrated to show the performance with comparison to the classical methods.",

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AU - Gupta, Arjun K.

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AB - In this paper, we propose two different confidence regions for a mean vector under general conditions when the population of interest is nonnormal and the sample size is moderate. It is shown that both confidence regions will improve the accuracy of the approximation in the sense that the coverage error is of order o fenced(n- 1). Monte Carlo examples are demonstrated to show the performance with comparison to the classical methods.

KW - Confidence region

KW - Coverage probability

KW - Edgeworth expansion

KW - Normalization transformation

UR - https://www.scopus.com/pages/publications/33750336289

U2 - 10.1016/j.csda.2005.10.011

DO - 10.1016/j.csda.2005.10.011

M3 - 文章

AN - SCOPUS:33750336289

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VL - 51

SP - 1051

EP - 1062

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 2

ER -

Improved confidence regions for a mean vector under general conditions

Abstract

Keywords

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