Further remarks on asymptotic normality of likelihood and conditional analyses
D. A. S. Fraser, Philip McDunnough · Canadian Journal of Statistics · 1984
Abstract Under weak conditions the normalized likelihood with or without weight function almost surely converges to a normal density function: for a real parameter or a vector parameter; with or without the assumption of independent identical distributions. Applications arise for confidence intervals, confidence distributions, structural distributions. and conditional analyses with transformation and structural models.