Quantile regression is gradually emerging as a unified statistical methodology for estimating
models of conditional quantile functions. By complementing the exclusive focus of classical …

Modern statistical methods use complex, sophisticated models that can lead to intractable
computations. Saddlepoint approximations can be the answer. Written from the user's point …

One-step efficient GMM estimation has been developed in the recent papers of Back and
Brown (1990), Imbens (1993) and Qin and Lawless (1994). These papers emphasized …

In this paper I discuss alternatives to the GMM estimators proposed by Hansen (1982) and
others. These estimators are shown to have a number of advantages. First of all, there is no …

Recent developments in higher‐order asymptotic theory for statistical inference have
emphasized the fundamental role of the likelihood function in providing accurate …

Many statisticians resort to using the asymptotic normal approximation method to carry out
statistical inference for many statistical tests, especially nonparametric ones. In this article …

In this paper, we investigate the use of the empirical distribution function in place of the
underlying distribution function F to construct an empirical saddlepoint approximation to the …

Saddlepoint approximations of marginal densities and tail probabilities of general nonlinear
statistics are derived. These are based on the expansion of the statistic up to the second …

In this paper we consider regression problems subject to noise in the operator or design
matrix. This characterization appropriately models many physical phenomena with …

Classical statistical inference relies mostly on parametric models and on optimal procedures
which are mostly justified by their asymptotic properties when the data generating process …