MSRI Workshop on Nonlinear Estimation and Classification
CD Table of Contents

March 19 - 29, 2001

Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research has been undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that is liberally interpreted to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. This workshop brought together eminent experts from these fields in order to exchange ideas and forge directions for the future.

Table of Contents - Disk 1

Table of Contents - Disk 2

Robert Gray Gauss mixture vector quantization: Clustering Gauss mixtures with the minimum discrimination information distortion for modeling, compression, and classification Tom Dietterich Some experiments with ensemble methods for classification and conditional density estimation Edward I. George Dilution priors for model uncertainty Maria DeIorio, Peter Mueller, Gary Rosner & Steven MacEachern An ANOVA model for dependent random measures with an application to population models Ernest Fokoue Aspects of stochastic simulation based inference for some mixtures of latent structures Yuhong Yang Combining regression estimators under a fixed design David Scott Clustering by partial mixture estimation Angelika van der Linde Model complexity and model priors Grace Wahba Optimal properties and adaptive tuning of support vector machines (SVM's) Robert Burbidge Adaptive kernels for SV classification Mario A. T. Figueiredo Unsupervised sparse regression Steve Ellis Instability in nonlinear estimation and classification Charles Stone Extended linear modeling with splines Linda Zhao Bayesian approaches in nonparametric estimation problems Charles Kooperberg & Charles Stone Logspline density estimation with free-knot splines Jennifer Pittman Adaptive splines and genetic algorithms with an application to classification Heping Zhang Mixed-effects multivariate adaptive splines models: An automated procedure for fitting longitudinal data and growth curves Sally Wood Bayesian mixture splines for spatially adaptive nonparametric regression Michael Jordan Variational inference for clustering and classification Bill Cleveland Modeling internet traffic John Rice A simple model for a complex system: Predicting travel times on freeways Andrew Nobel Denoising deterministic time series Barbara Bailey A statistical approach to quantifying the predictability of noisy nonlinear systems Claudia Tebaldi Looking for nonlinearities in the large scale dynamics of the atmosphere Nando DeFreitas Non-stationary, nonlinear classification and model selection Ashis Gangopadhyay & Kin Cheung Semiparametric estimation of the long-memory parameter in farima models Terry Speed Nonlinear estimation and classification: Challenges from genomics Gilles Blanchard Bounding generalization error of aggregate classifiers through empirical margin distributions Servane Gey & Elodie Nedelec Model selection for CART regression trees Adam Krzyzak Nonlinear function learning and classification using optimal radial basis function networks Ludger Ruschendorf On adaptive estimation by neural nets type estimators Alexandre Tsybakov Fast rates and adaptation in nonparametric classification

Richard Baraniuk Besov vs. Plato: Multiscale image modeling Martin Vetterli Wavelets, Approximation Compression and Sampling: Review and New Results Ronald DeVore Harmonic Analysis of BV Minh Do and Martin Vetterli Texture characterization and retrieval using steerable hidden Markov models Maarten Jansen Thresholding second generation wavelet coefficients for noisy, non-equispaced data Eric Kolaczyk and Robert Nowak A multiresolution analysis for statistical likelihoods: Theory and methods Donald Geman Coarse-to-fine classification and visual indexing Tomaso Poggio From bits to information: Theory and applications of learning machines David Denison Bayesian prediction using adaptive ridge estimators Jorg Rahnenfuhrer Data compression and statistical inference Juan Lin Local curved gaussian models Amy Braverman Compressing and analyzing massive geophysical data sets by Monte Carlo extended ECVQ Rahul Shukla, Minh Do and Martin Vetterli Best adaptive tiling in a rate-distortion sense Enrico Capobianco Multi-resolution properties of semi-parametric volatility models Harri Kiiveri Environmental monitoring using a time series of satellite images and other spatial data sets Adrian Raftery Bayesian multidimensional scaling and choice of dimension Katherine Pollard and Mark van der Laan Computationally intensive statistical methods for microarray based drug discovery Saira Mian Analysis of Transcription Profiling Data Using Generative and Discriminative Methods Alexander Loguinov, Rus Yukhananov, Christopher Vulpe and Saira Mian Using robust and resistant regression analysys (MM-estimator) to find differentially expressed genes in microarray data Ingo Ruczinski, Charles Kooperberg and Michael L. LeBlanc Logic Regression Jerome Friedman Predictive data mining with multiple additive regression trees Rob Schapire Logistic regression, AdaBoost and Bregman distances Margaret H. Wright Where does mainstream optimization fit? Steve Marron High dimension - low sample size data analysis Joshua Tenenbaum A global geometric framework for nonlinear dimensionality reduction Jacqueline J. Meulman Multivariate optimal scaling techniques for the analysis of large and complicated data sets Nelson Butuk Construction of low dimensional DME reaction state space trajectories

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