Edgeworth expansions for errors-in-variables models

Gutti Jogesh Babu; Z. D. Bai

doi:10.1016/0047-259X(92)90045-H

Edgeworth expansions for errors-in-variables models

Gutti Jogesh Babu, Z. D. Bai

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

5 Scopus citations

Abstract

Edgeworth expansions for sums of independent but not identically distributed multivariate random vectors are established. The results are applied to get valid Edgeworth expansions for estimates of regression parameters in linear errors-in-variable models. The expansions for studentized versions are also developed. Further, Edgeworth expansions for the corresponding bootstrapped statistics are obtained. Using these expansions, the bootstrap distribution is shown to approximate the sampling distribution of the studentized estimators, better than the classical normal approximation.

Original language	English (US)
Pages (from-to)	226-244
Number of pages	19
Journal	Journal of Multivariate Analysis
Volume	42
Issue number	2
DOIs	https://doi.org/10.1016/0047-259X(92)90045-H
State	Published - Aug 1992

All Science Journal Classification (ASJC) codes

Statistics and Probability
Numerical Analysis
Statistics, Probability and Uncertainty

Access to Document

10.1016/0047-259X(92)90045-H

Cite this

@article{513083e9fad34439b88142ca6459f9b3,

title = "Edgeworth expansions for errors-in-variables models",

abstract = "Edgeworth expansions for sums of independent but not identically distributed multivariate random vectors are established. The results are applied to get valid Edgeworth expansions for estimates of regression parameters in linear errors-in-variable models. The expansions for studentized versions are also developed. Further, Edgeworth expansions for the corresponding bootstrapped statistics are obtained. Using these expansions, the bootstrap distribution is shown to approximate the sampling distribution of the studentized estimators, better than the classical normal approximation.",

author = "Babu, {Gutti Jogesh} and Bai, {Z. D.}",

note = "Funding Information: Received September 11, 1990; revised January 10, 1992. AMS 1980 subject classifications: 6OF05, 62E20, 62505. Key words and phrases: bootstrap, Edgeworth expansions, errors-in-variable models, skewness. * Research supported in part by NSA Grant MDA904-90-H-1001 and by NSF Grant DMS-9007717. t Research supported by the Air Force Offtce of Scientific Research under Grant AFOSR-89.0279.",

year = "1992",

month = aug,

doi = "10.1016/0047-259X(92)90045-H",

language = "English (US)",

volume = "42",

pages = "226--244",

journal = "Journal of Multivariate Analysis",

issn = "0047-259X",

publisher = "Academic Press Inc.",

number = "2",

}

TY - JOUR

T1 - Edgeworth expansions for errors-in-variables models

AU - Babu, Gutti Jogesh

AU - Bai, Z. D.

N1 - Funding Information: Received September 11, 1990; revised January 10, 1992. AMS 1980 subject classifications: 6OF05, 62E20, 62505. Key words and phrases: bootstrap, Edgeworth expansions, errors-in-variable models, skewness. * Research supported in part by NSA Grant MDA904-90-H-1001 and by NSF Grant DMS-9007717. t Research supported by the Air Force Offtce of Scientific Research under Grant AFOSR-89.0279.

PY - 1992/8

Y1 - 1992/8

N2 - Edgeworth expansions for sums of independent but not identically distributed multivariate random vectors are established. The results are applied to get valid Edgeworth expansions for estimates of regression parameters in linear errors-in-variable models. The expansions for studentized versions are also developed. Further, Edgeworth expansions for the corresponding bootstrapped statistics are obtained. Using these expansions, the bootstrap distribution is shown to approximate the sampling distribution of the studentized estimators, better than the classical normal approximation.

AB - Edgeworth expansions for sums of independent but not identically distributed multivariate random vectors are established. The results are applied to get valid Edgeworth expansions for estimates of regression parameters in linear errors-in-variable models. The expansions for studentized versions are also developed. Further, Edgeworth expansions for the corresponding bootstrapped statistics are obtained. Using these expansions, the bootstrap distribution is shown to approximate the sampling distribution of the studentized estimators, better than the classical normal approximation.

UR - http://www.scopus.com/inward/record.url?scp=38249009029&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38249009029&partnerID=8YFLogxK

U2 - 10.1016/0047-259X(92)90045-H

DO - 10.1016/0047-259X(92)90045-H

M3 - Article

AN - SCOPUS:38249009029

SN - 0047-259X

VL - 42

SP - 226

EP - 244

JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

IS - 2

ER -

Edgeworth expansions for errors-in-variables models

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this