'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

• Chapter 1: Historical BackgroundChapter 2: Some Approaches to Nonparametric Density EstimationChapter 3: Maximum Likelihood Density EstimationChapter 4: Maximum Penalized Likelihood Density EstimationChapter 5: Discrete Maximum Penalized Likelihood EstimationChapter 6: Nonparametric Density of Estimation in Higher DimensionsChapter 7: Nonparametric Regression and Intensity Function EstimationChapter 8: Model Building and Speculative Data AnalysisAppendix I: An Introduction to Mathematical Optimization TheoryAppendix II: Numerical Solution of Constrained Optimization ProblemsAppendix III: Optimization Algorithms for Noisy ProblemsAppendix IV: A Brief Primer in SimulationIndex.

'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

• Chapter 1: Historical BackgroundChapter 2: Some Approaches to Nonparametric Density EstimationChapter 3: Maximum Likelihood Density EstimationChapter 4: Maximum Penalized Likelihood Density EstimationChapter 5: Discrete Maximum Penalized Likelihood EstimationChapter 6: Nonparametric Density of Estimation in Higher DimensionsChapter 7: Nonparametric Regression and Intensity Function EstimationChapter 8: Model Building and Speculative Data AnalysisAppendix I: An Introduction to Mathematical Optimization TheoryAppendix II: Numerical Solution of Constrained Optimization ProblemsAppendix III: Optimization Algorithms for Noisy ProblemsAppendix IV: A Brief Primer in SimulationIndex.