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Bayesian analysis of multinomial regression with gamma utilities. / CUHK electronic theses & dissertations collection

多項式回歸模型可用來模擬賽馬過程。不同研究者對模型中馬匹的效用的分佈採取不同的假設,包括指數分佈,它與Harville 模型(Harville, 1973)相同,伽馬分佈(Stern, 1990)和正態分佈(Henery, 1981)。Harville 模型無法模擬賽馬過程中競爭第二位和第三位等非冠軍位置時增加的隨機性(Benter, 1994)。Stern 模型假設效用服從形狀參數大於一的伽馬分佈,Henery 模型假設效用服從正態分佈。Bacon-Shone,Lo 和 Busche(1992),Lo 和 Bacon-Shone(1994)和 Lo(1994)研究證明了相較於Harville 模型,這兩個模型能更好地模擬賽馬過程。本文利用賽馬歷史數據,採用貝葉斯方法對賽馬結果中馬匹勝出的概率進行預測。本文假設效用服從伽馬分佈。本文針對多項式回歸模型,提出一個在Metropolis-Hastings 抽樣方法中選擇提議分佈的簡便方法。此方法由Scott(2008)首次提出。我們在似然函數中加入服從伽馬分佈的效用作為潛變量。通過將服從伽馬分佈的效用變換成一個服從Mihram(1975)所描述的廣義極值分佈的隨機變量,我們得到一個線性回歸模型。由此線性模型我們可得到最小二乘估計,本文亦討論最小二乘估計的漸進抽樣分佈。我們利用此估計的方差得到Metropolis-Hastings 抽樣方法中的提議分佈。最後,我們可以得到回歸參數的後驗分佈樣本。本文用香港賽馬數據做模擬賽馬投資以檢驗本文提出的估計方法。 / In multinomial regression of racetrack betting, dierent distributions of utilities have been proposed: exponential distribution which is equivalent to Harville’s model (Harville, 1973), gamma distribution (Stern, 1990) and normal distribution (Henery, 1981). Harville’s model has the drawback that it ignores the increasing randomness of the competitions for the second and third place (Benter, 1994). The Stern’s model using gamma utilities with shape parameter greater than 1 and the Henery’s model using normal utilities have been shown to produce a better t (Bacon-Shone, Lo and Busche, 1992; Lo and Bacon-Shone, 1994; Lo, 1994). In this thesis, we use the Bayesian methodology to provide prediction on the winning probabilities of horses with the historical observed data. The gamma utility is adopted throughout the thesis. In this thesis, a convenient method of selecting Metropolis-Hastings proposal distributions for multinomial models is developed. A similar method is rst exploited by Scott (2008). We augment the gamma distributed utilities in the likelihood as latent variables. The gamma utility is transformed to a variable that follows generalized extreme value distribution described by Mihram (1975) through which we get a linear regression model. Least squares estimate of the parameters is easily obtained from this linear model. The asymptotic sampling distribution of the least squares estimate is discussed. The Metropolis-Hastings proposal distribution is generated conditioning on the variance of the estimator. Finally, samples from the posterior distribution of regression parameters are obtained. The proposed method is tested through betting simulations using data from Hong Kong horse racing market. / Detailed summary in vernacular field only. / Xu, Wenjun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 46-48). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Hong Kong Horse Racing Market and Models in Horse Racing --- p.4 / Chapter 2.1 --- Hong Kong Horse Racing Market --- p.4 / Chapter 2.2 --- Models in Horse Racing --- p.6 / Chapter 3 --- Metropolis-Hastings Algorithm in Multinomial Regression with Gamma Utilities --- p.10 / Chapter 3.1 --- Notations and Posterior Distribution --- p.10 / Chapter 3.2 --- Metropolis-Hastings Algorithm --- p.11 / Chapter 4 --- Application --- p.15 / Chapter 4.1 --- Variables --- p.16 / Chapter 4.2 --- Markov Chain Simulation --- p.17 / Chapter 4.3 --- Model Selection --- p.27 / Chapter 4.4 --- Estimation Result --- p.31 / Chapter 4.5 --- Betting Strategies and Comparisons --- p.33 / Chapter 5 --- Conclusion --- p.41 / Appendix A --- p.43 / Appendix B --- p.44 / Bibliography --- p.46

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328299
Date January 2012
ContributorsXu, Wenjun., Chinese University of Hong Kong Graduate School. Division of Statistics.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatelectronic resource, electronic resource, remote, 1 online resource (vi, 48 leaves) : ill. (some col.)
RightsUse 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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