In this dissertation I suggest a new (regularized) weighted quantile regression estimation approach for nonlinear regression models and double threshold ARCH (DTARCH) models. I allow the number of parameters in the nonlinear regression models to be fixed or diverge. The proposed estimation method is robust and efficient and is applicable to other models. I use the adaptive-LASSO and SCAD regularization to select parameters in the nonlinear regression models. I simultaneously estimate the AR and ARCH parameters in the DTARCH model using the proposed weighted quantile regression. The values of the proposed methodology are revealed. / Keywords: Weighted quantile regression, Adaptive-LASSO, High dimensionality, Model selection, Oracle property, SCAD, DTARCH models. / Under regularity conditions, I establish asymptotic distributions of the proposed estimators, which show that the model selection methods perform as well as if the correct submodels are known in advance. I also suggest an algorithm for fast implementation of the proposed methodology. Simulations are conducted to compare different estimators, and a real example is used to illustrate their performance. / Jiang, Xuejun. / Adviser: Xinyuan Song. / Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 86-92). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_344617 |
| Date | January 2009 |
| Contributors | Jiang, Xuejun, Chinese University of Hong Kong Graduate School. Division of Statistics. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (vi, 92 leaves) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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