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Nonparametric Function Estimation, Modeling, and Simulation

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by James R. Thompson and Richard A. Tapia


1990 / xvi + 304 pages / Softcover / ISBN: 978-0-898712-61-2 / List Price $76.0 / SIAM Member Price $53.20 / Order Code OT21

"...For those who do not have a copy of Tapia and Thompson (1978), the new book is definitely worth buying. The original Tapia and Thompson material in Chapters 1-5 is something everyone interested in density estimation should read, and the new chapters are a nice bonus....The newer material by Thompson contains thought-provoking treatments of some important issues that hopefully will provide a similar stimulus for research in its subject matter areas." -- Randall L. Eubank, Texas A&M University, Journal of the American Statistical Association, Volume 88, June 1993.

"The authors share their experience with those who wish to use exploratory devices, such as nonparametric density estimation, towards a better understanding of real world processes, which require several characterizing parameters and have multidimensional data outputs. The book is reasonably successful in its attempt to be a "road map" to investigators trying to make sense of the multiparametic models and multidimensional data....The book makes for interesting reading and should help the reader avoid some of the false trails in analyzing multidimensional data, and perhaps spare the reader from repeating mistakes of the authors and others." -- E.F. Schuster, Mathematical Reviews, Issue 92b.

Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions.

Professionals in statistics, computer science, and operations research will find this book useful, as will graduate students in the areas of statistics, engineering, data analysis and modeling, and density estimation. Engineers in all fields--particularly biomedicine--will be interested in this book and should find its contents quite applicable.

Chapter 1: Historical Background; Chapter 2: Some Approaches to Nonparametric Density Estimation; Chapter 3: Maximum Likelihood Density Estimation; Chapter 4: Maximum Penalized Likelihood Density Estimation; Chapter 5: Discrete Maximum Penalized Likelihood Estimation; Chapter 6: Nonparametric Density of Estimation in Higher Dimensions; Chapter 7: Nonparametric Regression and Intensity Function Estimation; Chapter 8: Model Building and Speculative Data Analysis; Appendix I: An Introduction to Mathematical Optimization Theory; Appendix II: Numerical Solution of Constrained Optimization Problems; Appendix III: Optimization Algorithms for Noisy Problems; Appendix IV: A Brief Primer in Simulation; Index.


ISBN: 9780898712612

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