Density Estimation for Statistics and Data Analysis
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A01=Bernard. W. Silverman
adaptive
Adaptive Kernel
Adaptive Kernel Estimate
advanced density estimation applications
Augmented Data Set
Author_Bernard. W. Silverman
B.W. Silverman
Bivariate Normal Mixture
Category=GPH
Category=PBT
Category=PBW
cluster analysis methods
cross-validation
Cumulative Distribution Function
Data Set
Density Estimation
Epanechnikov Kernel
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
estimate
graphical data analysis
hazard rate estimation
kernel
Kernel Estimate
Kernel Estimator
likelihood
Likelihood Cross-validation
Maximum Penalized Likelihood Estimator
Maximum Penalized Likelihood Method
method
Multivariate Histograms
Multivariate Kernel Density Estimator
Naive Estimator
nonparametric statistics
Optimal Window Width
Orthogonal Series Estimators
parameter
Penalized Likelihood Approach
Penalized Log Likelihood
Pilot Estimate
projection pursuit
Roughness Penalty
Scatter Plot
simulation techniques
smoothing
Smoothing Parameter
width
window
Window Width
Product details
- ISBN 9780412246203
- Weight: 490g
- Dimensions: 152 x 229mm
- Publication Date: 01 Apr 1986
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique's practical value. Furthermore, the subject has been rather inaccessible to the general statistician.
The account presented in this book places emphasis on topics of methodological importance, in the hope that this will facilitate broader practical application of density estimation and also encourage research into relevant theoretical work. The book also provides an introduction to the subject for those with general interests in statistics. The important role of density estimation as a graphical technique is reflected by the inclusion of more than 50 graphs and figures throughout the text.
Several contexts in which density estimation can be used are discussed, including the exploration and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates and other quantities that depend on the density. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects. Attention is also given to adaptive methods, which smooth to a greater degree in the tails of the distribution, and to methods based on the idea of penalized likelihood.
