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Essays in Nonparametric Estimation i...

Lee, Na Kyeong.

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Essays in Nonparametric Estimation in Besov Spaces.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in Nonparametric Estimation in Besov Spaces./
作者:	Lee, Na Kyeong.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
面頁冊數:	71 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-10(E), Section: A.
Contained By:	Dissertation Abstracts International76-10A(E).
標題:	Economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3704749
ISBN:	9781321773477

Essays in Nonparametric Estimation in Besov Spaces.
Lee, Na Kyeong.

Essays in Nonparametric Estimation in Besov Spaces. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 71 p.

Source: Dissertation Abstracts International, Volume: 76-10(E), Section: A.

Thesis (Ph.D.)--University of Colorado at Boulder, 2015.

In the first chapter of this thesis, a class of local constant kernel estimators for a regression in Besov spaces is developed based on a novel set of kernels provided by Mynbaev and Martins-Filho (2010). The proposed class of local constant estimators includes the Nadaraya-Watson estimator. I show that bias reduction for the estimators in the class can be achieved without the potential negativity of the underlying estimated densities. Our estimators have faster uniform convergence rates than the Nadaraya-Watson estimator. I establish consistency and asymptotic normality of the estimators in the class. These results have been established without using higher-order kernels and imposing less restrictive conditions on the true density and the regression. A Monte Carlo study is provided to illustrate the finite sample performance of the estimators.

ISBN: 9781321773477Subjects--Topical Terms:

517137
Economics.

Essays in Nonparametric Estimation in Besov Spaces.
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500 $a Source: Dissertation Abstracts International, Volume: 76-10(E), Section: A.
500 $a Adviser: Carlos Martins-Filho.
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520 $a In the first chapter of this thesis, a class of local constant kernel estimators for a regression in Besov spaces is developed based on a novel set of kernels provided by Mynbaev and Martins-Filho (2010). The proposed class of local constant estimators includes the Nadaraya-Watson estimator. I show that bias reduction for the estimators in the class can be achieved without the potential negativity of the underlying estimated densities. Our estimators have faster uniform convergence rates than the Nadaraya-Watson estimator. I establish consistency and asymptotic normality of the estimators in the class. These results have been established without using higher-order kernels and imposing less restrictive conditions on the true density and the regression. A Monte Carlo study is provided to illustrate the finite sample performance of the estimators.
520 $a In the second chapter of this thesis, I propose a family of estimators for a measure of polarization via a kernel-based density estimator provided by Mynbaev and Martins-Filho (2010), as well as a distribution function estimator based on integration of the estimated density. The existing estimator for polarization measure proposed by Duclos et al. (2004) is based on the empirical distribution that suffers from lack of smoothness. I modified their work by using both the density estimators proposed by Mynbaev and Martins-Filho (2010) and the integration of the estimated density. I show that a class of estimators for the distribution function is asymptotically unbiased and consistent. In addition, I establish that a class of estimators for polarization measure is asymptotically unbiased. Finally, I study the behavior of the estimators using a Monte Carlo simulation.
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