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eBook Applying Kernel and Nonparametric Estimation to Economic Topics (Advances in Econometrics) download
Moneymaking
Author: R. Carter Hill T. Fomby,T. Fomby,R. Carter Hill
ISBN: 0762305819
Subcategory: Economics
Pages 378 pages
Publisher Emerald Group Publishing Limited; 1 edition (February 2, 2001)
Language English
Category: Moneymaking
Rating: 4.4
Votes: 797
ePUB size: 1467 kb
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DJVU size: 1591 kb
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eBook Applying Kernel and Nonparametric Estimation to Economic Topics (Advances in Econometrics) download

by R. Carter Hill T. Fomby,T. Fomby,R. Carter Hill


Carter Hill, T. Fomby. Nonparametric estimation and inference is becoming increasingly popular in economics because of the advent of extensive computing power and the development of efficient computer algorithms.

Carter Hill, T. Papers in this volume present techniques that permit inference that is robust to deviations from conventional parametric assumptions. The volume is divided into two sections.

Applying Kernel and Nonparametric Estimation to Economic Topics, Volume 14.

If you would like to propose a volume for Advances in Econometrics, please contact Carter Hill: or Tom Fomby:. For volume proposal forms please contact Charlotte Maiorana:. Applying Kernel and Nonparametric Estimation to Economic Topics, Volume 14. Messy Data- Missing Observations, Outliers, and Mixed-Frequency Data, Volume 13. Applying Maximum Entropy to Econometric Problems, Volume 12. Back.

Carter Hill, R. Carter Hill, William E. Griffiths, Guay C. Lim. ISBN-13: 9780470626733. Every textbook comes with a 21-day "Any Reason" guarantee.

ISBN13: 9780762305810.

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Applying kernel and nonparametric estimation to economic topics Close. Are you sure you want to remove Applying kernel and nonparametric estimation to economic topics from your list? Applying kernel and nonparametric estimation to economic topics. Published 2000 by JAI Press in Stamford, Conn. Advances in econometrics, Advances in econometrics (Unnumbered).

CALL FOR PAPERS - Advances in Econometrics, Volume 40. Topics in identification, limited dependent variables, partial observability, experimentation, and flexible modeling - download full CFP details here. Tom Fomby Southern Methodist University, USA tfomby.

Nonparametric statistics is the branch of statistics that is not based solely on parametrized families of probability distributions (common examples of parameters are the mean and variance). Nonparametric statistics is based on either being distribution-free or having a specified distribution but with the distribution's parameters unspecified. Nonparametric statistics includes both descriptive statistics and statistical inference.

Hardbound. Nonparametric estimation and inference is becoming increasingly popular in economics because of the advent of extensive computing power and the development of efficient computer algorithms. Papers in this volume present techniques that permit inference that is robust to deviations from conventional parametric assumptions. The volume is divided into two sections. The first section contains papers concerned with methodology while the second section contains papers that emphasize the application of nonparametric techniques to practical problems.