According to the American Cancer Society's forecasts for 2019, there will be about 268,600
new cases in the United States with invasive breast cancer in women, about 62,930 new …

Parameter studies are everywhere in computational science. Complex engineering
simulations must run several times with different inputs to effectively study the relationships …

Sufficient dimension reduction is a rapidly developing research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

The regression estimation problem has a long history. Already in 1632 Galileo Galilei used a
procedure which can be interpreted as? tting a linear relationship to contaminated observed …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

We propose a novel method of dimensionality reduction for supervised learning problems.
Given a regression or classification problem in which we wish to predict a response variable …

We propose “supervised principal component analysis (supervised PCA)”, a generalization
of PCA that is uniquely effective for regression and classification problems with high …

This book is intended to introduce graduate students and practicing professionals to some of
the main ideas and methods of semiparametric and nonparametric estimation in …

We present a new methodology for sufficient dimension reduction (SDR). Our methodology
derives directly from the formulation of SDR in terms of the conditional independence of the …

The local statistical approach for fault detection and isolation is applied to fuzzy models
validation. The method detects the inconsistencies between a fuzzy rule base and the …