Function Estim Density.. (2nd Ed)
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Book Overview Nonparametric kernel estimators apply to the statistical analysis of independent or dependent sequences of random variables and for sampl...
Nonparametric kernel estimators apply to the statistical analysis of independent or dependent sequences of random variables and for samples of continuous or discrete processes. The optimization of these procedures is based on the choice of a bandwidth that minimizes an estimation error and the weak convergence of the estimators is proved. This book introduces new mathematical results on statistical methods for the density and regression functions presented in the mathematical literature and for functions defining more complex models such as the models for the intensity of point processes, for the drift and variance of auto-regressive diffusions and the single-index regression models.
This second edition presents minimax properties with Lp risks, for a real p larger than one, and optimal convergence results for new kernel estimators of function defining processes: models for multidimensional variables, periodic intensities, estimators of the distribution functions of censored and truncated variables, estimation in frailty models, estimators for time dependent diffusions, for spatial diffusions and for diffusions with stochastic volatility.
Book Details Format: Hardcover | Pages: 244 | Language: English | Publisher: WORLD SCIENTIFIC PUB CO INC | ISBN: 9811272832
FormatHardcover
Pages244
LanguageEnglish
ISBN9811272832
EAN9789811272837
PublisherWORLD SCIENTIFIC PUB CO INC
Publication Date1970-01-01
Edition2
AccessoriesNo Accessory
ConditionNew
Product TypeHARD COVER BOOKS
Weight1.14 Pounds
Length9.0 Inches
Width6.0 Inches
Height0.63 Inches
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