Nonparametric function estimation modeling and simulation - James R. Thompson

James R. Thompson

ISBN 10: 0898712610 / ISBN 13: 9780898712612

Published by 1987, 1987

About this Item

Occasion - Etat Correct - Ancien livre de Bibliothèque, Pliure(s) - Nonparametric function estimation modeling and simulation (1987) - Grand Format. Seller Inventory # 3254593

Bibliographic Details

Title: Nonparametric function estimation modeling ...

Publisher: 1987

Publication Date: 1987

Binding: Soft cover

Condition: Used: Acceptable

About this title

Synopsis:

Topics emphasized in this book 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.

Review:

'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, Journal of the American Statistical Association

'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