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Accurate confidence intervals in regression analyses of non-normal data

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Abstract

A linear model in which random errors are distributed independently and identically according to an arbitrary continuous distribution is assumed. Second- and third-order accurate confidence intervals for regression parameters are constructed from Charlier differential series expansions of approximately pivotal quantities around Student’s t distribution. Simulation verifies that small sample performance of the intervals surpasses that of conventional asymptotic intervals and equals or surpasses that of bootstrap percentile-t and bootstrap percentile-|t| intervals under mild to marked departure from normality.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Montana State University, Bozeman, MT, 59717-2400, USA

    Robert J. Boik

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  1. Robert J. Boik
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Boik, R.J. Accurate confidence intervals in regression analyses of non-normal data. AISM 60, 61–83 (2008). https://doi.org/10.1007/s10463-006-0085-1

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  • DOI: https://doi.org/10.1007/s10463-006-0085-1

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