Smoothing Techniques for Curve Estimation: Proceedings of a Workshop held in Heidelberg, April 2–4, 1979

Th. Gasser, M. Rosenblatt (auth.), Th. Gasser, M. Rosenblatt (eds.)9780387097060, 0-387-09706-6

This work introduces new developments in the construction, analysis, and implementation of parallel computing algorithms. This book presents 23 self-contained chapters, including surveys, written by distinguished researchers in the field of parallel computing. Each chapter is devoted to some aspects of the subject: parallel algorithms for matrix computations, parallel optimization, management of parallel programming models and data, with the largest focus on parallel scientific computing in industrial applications.

Key features include: construction and analysis of parallel algorithms for linear algebra and optimization problems; different aspects of parallel architectures, including distributed memory computers with multicore processors; a wide range of industrial applications: parallel simulation of flows through oil filters as well as in porous and gas media, jet aerodynamics, heat conduction in electrical cables, nonlinear optics processes in tapered lasers, and molecular and cell dynamics.

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Table of contents :
Nonparametric curve estimation….Pages 1-4
A tree-structured approach to nonparametric multiple regression….Pages 5-22
Kernel estimation of regression functions….Pages 23-68
Total least squares….Pages 69-76
Some theoretical results on Tukey’s 3R smoother….Pages 77-90
Bias- and efficiency-robustness of general M-estimators for regression with random carriers….Pages 91-116
Approximate conditional-mean type smoothers and interpolators….Pages 117-143
Optimal convergence properties of kernel estimates of derivatives of a density function….Pages 144-154
Density quantile estimation approach to statistical data modelling….Pages 155-180
Global measures of deviation for kernel and nearest neighbor density estimates….Pages 181-190
Some comments on the asymptotic behavior of robust smoothers….Pages 191-195
Cross-validation techniques for smoothing spline functions in one or two dimensions….Pages 196-232
Convergence rates of “thin plate” smoothing splines wihen the data are noisy….Pages 233-245