Statistical Inference From Fuzzy Data

Bootstrap Statistical Inference for the Variance Based on Fuzzy Data

Abstract: The bootstrap is a simple and straightforward method for calculating approximated biases, standard deviations, confidence intervals, testing statistical hypotheses, and so forth, in almost any nonparametric estimation problem. In this paper we describe a bootstrap method for variance that is designed directly for hypothesis testing in case of fuzzy data based on Yao-Wu signed distance.

Statistical Inference Through Data Compression

Data Compression and Statistical Inference

Data driven methods for clustering and vector quantization can be used to construct nonparametric tests for multivariate testing problems. We combine the old concept of permutation tests with recent clustering algorithms to obtain multivariate inferential methods which in the univariate case are very similar to rank tests. At hand of computer experiments we illustrate some typical properties of...

Procrustean statistical inference of deformations

A two step method has been devised for the statistical inference of deformation changes. In the first step of this method and based on Procrustes analysis of deformation tensors, the significance of the change in a time or space series of deformation tensors is statistically analyzed. In the second step significant change(s) in deformations are localized. In other words, they are assigned to ce...

Scalable Statistical Methods for Ancestral Inference from Genomic Variation Data

Scalable Statistical Methods for Ancestral Inference from Genomic Variation Data by Andrew Hans Chan Doctor of Philosophy in Computer Science University of California, Berkeley Professor Yun S. Song, Chair Developments in DNA sequencing technology over the last few years have yielded unprecedented volumes of genetic data. The resulting datasets are indispensable for a variety of purposes, from ...