Lp-norm Consistencies of Nonparametric Estimates of Regression, Heteroskedasticity and Variance of Regression Estimate when Distribution of Regressor is Known PDF Download
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Author: Radhey S. Singh
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 30
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Book Description
Author: Radhey S. Singh
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 30
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Book Description
Author: John P. Small
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 32
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Book Description
Author:
Publisher:
ISBN:
Category : Classification
Languages : en
Pages : 976
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Author:
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ISBN:
Category : Economics
Languages : en
Pages : 984
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Book Description
Author: Michael Carter
Publisher:
ISBN:
Category : Cartels
Languages : en
Pages : 22
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Book Description
Author: Rene Gonin
Publisher: Routledge
ISBN: 1351428179
Category : Mathematics
Languages : en
Pages : 318
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Book Description
Complete with valuable FORTRAN programs that help solve nondifferentiable nonlinear LtandLo.-norm estimation problems, this important reference/text extensively delineates ahistory of Lp-norm estimation. It examines the nonlinear Lp-norm estimation problem that isa viable alternative to least squares estimation problems where the underlying errordistribution is nonnormal, i.e., non-Gaussian.Nonlinear LrNorm Estimation addresses both computational and statistical aspects ofLp-norm estimation problems to bridge the gap between these two fields . . . contains 70useful illustrations ... discusses linear Lp-norm as well as nonlinear Lt, Lo., and Lp-normestimation problems . . . provides all appropriate computational algorithms and FORTRANlistings for nonlinear Lt- and Lo.-norm estimation problems . . . guides readers with clear endof-chapter notes on related topics and outstanding research publications . . . contains numericalexamples plus several practical problems .. . and shows how the data can prescribe variousapplications of Lp-norm alternatives.Nonlinear Lp-Norm Estimation is an indispensable reference for statisticians,operations researchers, numerical analysts, applied mathematicians, biometricians, andcomputer scientists, as well as a text for graduate students in statistics or computer science.
Author: László Györfi
Publisher: Springer Science & Business Media
ISBN: 0387224424
Category : Mathematics
Languages : en
Pages : 662
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Book Description
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.
Author: Randall L. Eubank
Publisher: CRC Press
ISBN: 9780824793371
Category : Mathematics
Languages : en
Pages : 368
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Book Description
Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.
Author: Justin Chown
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Oliver B. Linton
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
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Book Description
We study the efficient estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heteroskedasticity. The effect of weighting on nonparametric regressions is examined, and cases when efficiency gain can be achieved via weighting is investigated. We show that in many popular nonparametric regression models our method has lower asymptotic variance than the usual unweighted procedures. A Monte Carlo investigation is conducted and confirms the efficiency gain over conventional nonparametric regression estimators in finite samples. We use our method in several common applications concerning stock returns.
