Nonlinear Estimation and Classification

1. Front Matter

   Pages N2-vii

   PDF

2. Introduction

   1. Introduction

      • David D. Denison, Mark H. Hansen, Christopher C. Holmes, Bani Mallick, Bin Yu

      Pages 1-5

3. Longer Papers

   1. Front Matter

      Pages 7-7

      PDF

   2. Wavelet Statistical Models and Besov Spaces

      • Hyeokho Choi, Richard G. Baraniuk

      Pages 9-29

   3. Coarse-to-Fine Classification and Scene Labeling

      • Donald Geman

      Pages 31-48

   4. Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Sets

      • Harri Kiiveri, Peter Caccetta, Norm Campbell, Fiona Evans, Suzanne Furby, Jeremy Wallace

      Pages 49-62

   5. Traffic Flow on a Freeway Network

      • Peter Bickel, Chao Chen, Jaimyoung Kwon, John Rice, Pravin Varaiya, Erik van Zwet

      Pages 63-81

   6. Internet Traffic Tends Toward Poisson and Independent as the Load Increases

      • Jin Cao, William S. Cleveland, Dong Lin, Don X. Sun

      Pages 83-109

   7. Regression and Classification with Regularization

      • Sayan Mukherjee, Ryan Rifkin, Tomaso Poggio

      Pages 111-128

   8. Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines

      • Grace Wahba, Yi Lin, Yoonkyung Lee, Hao Zhang

      Pages 129-147

   9. The Boosting Approach to Machine Learning: An Overview

      • Robert E. Schapire

      Pages 149-171

   10. Improved Class Probability Estimates from Decision Tree Models

       • Dragos D. Margineantu, Thomas G. Dietterich

       Pages 173-188

   11. Gauss Mixture Quantization: Clustering Gauss Mixtures

       • Robert M. Gray

       Pages 189-212

   12. Extended Linear Modeling with Splines

       • Jianhua Z. Huang, Charles J. Stone

       Pages 213-233

4. Shorter Papers

   1. Front Matter

      Pages 235-235

      PDF

   2. Adaptive Sparse Regression

      • Mário A. T. Figueiredo

      Pages 237-247

   3. Multiscale Statistical Models

      • Eric D. Kolaczyk, Robert D. Nowak

      Pages 249-259

   4. Wavelet Thresholding on Non-Equispaced Data

      • Maarten Jansen

      Pages 261-271

   5. Multi-Resolution Properties of Semi-Parametric Volatility Models

      • Enrico Capobianco

      Pages 273-284

   6. Confidence Intervals for Logspline Density Estimation

      • Charles Kooperberg, Charles J. Stone

      Pages 285-295

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 is being 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 we liberally interpret 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. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.

• ANOVA
• Estimator
• data analysis
• probability
• statistical inference
• statistics
• time series

• Department of Mathematics, Imperial College, London, UK

  David D. Denison, Christopher C. Holmes

• Room 2C283 Bell Laboratories, Lucent Technologies, Murray Hill, USA

  Mark H. Hansen

• Statistical Department, Texas A&M University, College Station, USA

  Bani Mallick

• Department of Statistics, University of California, Berkeley, Berkeley, USA

  Bin Yu

Policies and ethics