VARIABLE SELECTION FOR FUNCTIONAL INDEX COEFFICIENT MODELS AND ITS APPLICATIONS IN FINANCE AND ENGINEERING

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Yang, B. (2012). VARIABLE SELECTION FOR FUNCTIONAL INDEX COEFFICIENT MODELS AND ITS APPLICATIONS IN FINANCE AND ENGINEERING. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Variable selection with a non-concave penalty function has become popular in recent years, since it has ability to select significant variables and to estimate unknown regression coefficients simultaneously. In this dissertation, I consider variable selection in a functional index coefficient model under strong mixing context. My selection procedures with smoothly clipped absolute deviation penalty function consist of two steps. The first is to select significant covariates with functional coefficients and it is then to do variable selection for local significant variables with parametric coefficients. The asymptotic properties such as consistency, sparsity and the oracle property of these two step estimators are established, whereas easy computational algorithms are suggested to highlight the implementation of the proposed procedures. Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed estimators and selection procedures. Meanwhile, two financial examples including functional index coefficient autoregressive models and functional index coefficient models for the stock return predictability are presented. Finally, two engineering applications of local annual average daily traffic (AADT) estimation and seasonal factors calculation are extensively studied in two chapters respectively.

Details
Author

Yang, Bingduo

Title

VARIABLE SELECTION FOR FUNCTIONAL INDEX COEFFICIENT MODELS AND ITS APPLICATIONS IN FINANCE AND ENGINEERING

Physical Description

1 online resource (107 pages) : PDF

Date

2012

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Variable selection with a non-concave penalty function has become popular in recent years, since it has ability to select significant variables and to estimate unknown regression coefficients simultaneously. In this dissertation, I consider variable selection in a functional index coefficient model under strong mixing context. My selection procedures with smoothly clipped absolute deviation penalty function consist of two steps. The first is to select significant covariates with functional coefficients and it is then to do variable selection for local significant variables with parametric coefficients. The asymptotic properties such as consistency, sparsity and the oracle property of these two step estimators are established, whereas easy computational algorithms are suggested to highlight the implementation of the proposed procedures. Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed estimators and selection procedures. Meanwhile, two financial examples including functional index coefficient autoregressive models and functional index coefficient models for the stock return predictability are presented. Finally, two engineering applications of local annual average daily traffic (AADT) estimation and seasonal factors calculation are extensively studied in two chapters respectively.

Genre

doctoral dissertations

Subjects--Topics

Engineering
Statistics
Finance

Degree

Ph.D.

Keywords

AADT
Functional Index Coefficient Model
Oracle Property
Scad Penalty Function
Stock Return Predictability
Variable Selection

Subject Area

Applied Mathematics

Advisor(s)

Cai, Zongwu

Committee Members

Jiang, Jiancheng
Zhou, Weihua
Wang, Sheng-Guo

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2012.

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Rights Holder Information

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Identifier

Yang_uncc_0694D_10305

Permalink

http://hdl.handle.net/20.500.13093/etd:135