Approximate multivariate conditional inference using the adjusted profile likelihood

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3 Scopus citations

Abstract

The author proposes saddlepoint approximation methods that are adapted to multivariate conditional inference in canonical exponential families. Several approaches to approximating conditional discrete distributions involve dividing an approximation to the full joint mass function, summed over tail regions of interest, by an approximate marginal density. The author first approximates this conditional likelihood by the adjusted profile likelihood, and then applies a multivariate saddlepoint approximation. He also presents formulas to aid in performing simultaneously the profiling and maximizing steps.

Original languageEnglish (US)
Pages (from-to)5-14
Number of pages10
JournalCanadian Journal of Statistics
Volume32
Issue number1
DOIs
StatePublished - Mar 2004

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Adjusted profile likelihood
  • Multivariate approximation
  • Sequential saddlepoint approximation

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