Edgeworth expansions for errors-in-variables models

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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 languageEnglish (US)
Pages (from-to)226-244
Number of pages19
JournalJournal of Multivariate Analysis
Volume42
Issue number2
DOIs
StatePublished - Aug 1992

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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