Conditional Distribution Approximations
Often inference for a subset of model parameters is desired, and the others are treated as nuisance parameters. Among the many methods for attacking this problem is conditional inference, in which sufficient statistics for nuisance parameters are conditioned on. Calculations involving these conditional distributions are often quite difficult. This chapter will develop methods for approximating densities and distribution functions for conditional distributions.
KeywordsConditional Distribution Nuisance Parameter Saddlepoint Approximation Conditional Inference Cumulant Generate Function
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