James E. Gentle: Computational statistics (Statistics and Computing Series)

Causal Inference in Statistics: A Gentle Introduction

This paper provides a conceptual introduction to causal inference, aimed to assist researchers bene t from recent advances in this area. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in ...

Computational rank-based statistics

This review discusses two algorithms which can be used to compute rank-based regression estimates. For completeness, a brief overview of rank-based inference procedures in the context of a linear model is presented. The discussion includes geometry, estimation, inference, and diagnostics. In regard to computing the rank-based estimates, we discuss two approaches. The first approach is based on ...

Computational Statistics Spring 2008

Experimental Mathematics and Computational Statistics

The field of statistics has long been noted for techniques to detect patterns and regularities in numerical data. In this article we explore connections between statistics and the emerging field of “experimental mathematics.” These includes both applications of experimental mathematics in statistics, as well as statistical methods applied to computational mathematics.

Computing Multivariate Statistics

Many multivariate statistics are expressed as functions of the hypergeometric function of a matrix argument, or more generally, as series of Jack functions. This work is a collection of formulas, identities, and algorithms useful for the computations of these statistics in practice. Numerical examples are presented. 1. Definitions 1.1. Partitions and hook lengths. For an integer k ≥ 0 we say th...