Download Introduction to Robust and Quasi-Robust Statistical Methods by Dr. William J. J. Rey (auth.) PDF

By Dr. William J. J. Rey (auth.)

Show description

Read or Download Introduction to Robust and Quasi-Robust Statistical Methods PDF

Similar probability & statistics books

Directions in Robust Statistics and Diagnostics: Part II

This IMA quantity in arithmetic and its purposes instructions IN strong information AND DIAGNOSTICS relies at the lawsuits of the 1st 4 weeks of the six week IMA 1989 summer time application "Robustness, Diagnostics, Computing and pix in Statistics". a massive goal of the organizers was once to attract a large set of statisticians operating in robustness or diagnostics into collaboration at the hard difficulties in those parts, really at the interface among them.

Bayesian Networks: An Introduction

Bayesian Networks: An creation offers a self-contained advent to the idea and functions of Bayesian networks, a subject matter of curiosity and significance for statisticians, computing device scientists and people fascinated by modelling advanced information units. the fabric has been widely demonstrated in school room educating and assumes a simple wisdom of chance, records and arithmetic.

Missing data analysis in practice

Lacking facts research in perform presents sensible tools for reading lacking facts besides the heuristic reasoning for realizing the theoretical underpinnings. Drawing on his 25 years of expertise learning, educating, and consulting in quantitative parts, the writer provides either frequentist and Bayesian views.

Statistical Shape Analysis

A completely revised and up-to-date version of this advent to fashionable statistical tools for form research form research is a vital device within the many disciplines the place items are in comparison utilizing geometrical gains.  Examples contain evaluating mind form in schizophrenia; investigating protein molecules in bioinformatics; and describing development of organisms in biology.

Additional resources for Introduction to Robust and Quasi-Robust Statistical Methods

Example text

To ease the understanding of its analytical aspects, we now consider the three points just mentioned. The Prokhorov metric permits comparison of a discrete empirical distribution with a continuous one through the association of each observation of the former with a subset of the sample space; the comparison is then performed with the help of the probability of the latter distribution over this subset. ccordingly, the triangular inequality holds true. - Although we have required the possibility of considering simultaneously both types of distributions, we must say that several papers avoid this constraint and hence also avoid the Prokhorov metric.

2 I [n (n-1 ) ] + ... Obviously, the same mathematical trick can be applied to the second leading term of the expansion, and so on. To avoid a loss of efficiency in the estimation, he suggests the following definition of an average estimator =n a- [ (n-l ) In ] L: a,; For reasons that were not obvious at that time, the jackknife estimate 1) frequently exhibits fairly good statistical properties. Tukey who will soon appear on the stage has also named a,;, the pseudo-estimates, and 1),;, the jackknife pseUdo-values.

1. Introduction The so-called jackknife method has been introduced by Quenouille to reduce a bias in estimation and then progressively extended to obtain an estimate of variances. It is interesting in that it yields an improved estimator and enables the estimator to be assessed in an inexpensive way, that is with regard to the methodology although the computation, if required, is not necessarily inexpensive. By all standards the results thus obtained are impressive in most cases; but in a few, not very well defined, circumstances the results are either poor or ridiculous.

Download PDF sample

Rated 4.89 of 5 – based on 35 votes