Multiple Comparison Procedures, Trimmed Means And Transformed Statistics

Multiple Comparison Procedures, Trimmed Means And Transformed Statistics

Classiier Combining through Trimmed Means and Order Statistics

| Combining the outputs of multiple neural networks has led to substantial improvements in several dii-cult pattern recognition problems. In this article, we introduce and investigate robust combiners, a family of classiiers based on order statistics. We focus our study to the analysis of the decision boundaries, and how these boundaries are aaected by order statistics combiners. In particular,...

On Weighted Randomly Trimmed Means

A class of robust location estimators called weighted randomly trimmed means are introduced and not only their consistency and asymptotic normality are proved, but their influence functions, asymptotic variances and breakdown points are also derived. They possess the same breakdown points as the median, and some of them own higher asymptotic relative efficiencies at the heavy-tailed distributio...

L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals.

The L-statistics form an important class of estimators in nonparametric statistics. Its members include trimmed means and sample quantiles and functions thereof. This article is devoted to theory and applications of L-statistics for repeated measurements data, wherein the measurements on the same subject are dependent and the measurements from different subjects are independent. This article ha...

Multiple Comparison Procedures

1. Introduction Analysis of variance is used to test for the real treatment differences. When the null hypothesis that all the treatment means are equal is not rejected, it may seem that no further questions need to be asked. However, in some experimental situations, it may be an oversimplification of the problem. For example, consider an experiment in rice weed control with 15 treatments viz. ...