Approximate multivariate conditional inference using the adjusted profile likelihood

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.