Advances in computation and the fast and cheap computational facilities now available to
statisticians have had a significant impact upon statistical research, and especially the …

The existence of high speed, inexpensive computing has made it easy to look at data in
ways that were once impossible. Where once a data analyst was forced to make restrictive …

A minimum divergence estimation method is developed for robust parameter estimation. The
proposed approach uses new density-based divergences which, unlike existing methods of …

This book gives a comprehensive account of density-based minimum distance methods and
their use in statistical inference. It covers statistical distances, density-based minimum …

It is shown how and why the influence curve poorly measures the robustness properties of
minimum Hellinger distance estimation. Rather, for this and related forms of estimation, there …

Categorical data arise often in many fields, including biometrics, economics, management,
manufacturing, marketing, psychology, and sociology. This book provides an introduction to …

A general class of minimum distance estimators for continuous models called minimum
disparity estimators are introduced. The conventional technique is to minimize a distance …

We discuss a method of weighting likelihood equations with the aim of obtaining fully
efficient and robust estimators. We discuss the case of continuous probability models using …

Statistics for stochastic processes is rapidly developing. It forms a branch of mathematical
sciences, spreading over theoretical statistics, probability theory, software development and …

The ϕ-divergence association models for two-way contingency tables is a family of models
that includes the association and correlation models as special cases. We present this family …