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    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/76912
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/76912


    Title: 二元指數族中參數之最大概似估計與最大擬概似估計異同的研究
    On the Maximum Likelihood and Maximum Pseudo-Likelihood Estimations of Bivariate Exponential Family Parameters
    Authors: 許雲峰
    Hsu, Yun Fong
    Contributors: 宋傳欽
    姜志銘

    Song, Chuan Chin
    Jiang, Jr Ming

    許雲峰
    Hsu, Yun Fong
    Keywords: 二元指數族
    最大概似估計
    概似方程式
    最大擬概似估計
    擬概似方程式
    2x2列聯表
    均方差
    bivariate exponential family
    maximum likelihood estimate
    likelihood equation
    maximum pseudo-likelihood estimate
    pseudo-likelihood equation
    2x2 contingency table
    mean square errors
    Date: 2015
    Issue Date: 2015-07-27 11:30:12 (UTC+8)
    Abstract: 當密度函數難以完整表示,例如無法求得其正規化常數,則求最大概似估計(MLE)時會有困難。因此一種替代方案就是使用擬概似函數去求得最大擬概似估計(MPLE)以取代MLE。本研究之目的在探討二元指數族中參數之MLE與MPLE的異同。文中先以常見的三個機率模型:卜瓦松-二項分配、三項分配與二元常態分配,探討模型參數的MLE與MPLE;接著推導一般二元指數族中獲得參數之MPLE的擬概似方程式;最後考慮 列聯表中,方格內參數為三種不同情況下的MLE與MPLE。當中兩種情況可以求出其精確解,而第三種則無法求出。針對第三種情況,利用Matlab程式以模擬的方式,計算出參數的MLE與MPLE,以進行分析比較,並觀察兩者之均方差如何受參數值影響。
    If the density function is hard to express completely, e.g. hard to get normalizing constant, then it would be difficult to find the maximum likelihood estimate (MLE). An alternative way is to use pseudo-likelihood function to find the maximum pseudo-likelihood estimate (MPLE) instead of MLE. This research is to study the similarities and differences of the MLE and MLPE on the bivariate exponential distribution parameters. We first derive the MLE and the MPLE of parameters in Poisson-binomial distribution, trinomial distribution, and bivariate normal distribution. Then, we derive the pseudo-likelihood equation to be used for solving MPLE of parameters in bivariate exponential family. Finally, we consider three cases on the cell probabilities of the 2x2 contingency table. There are exact solutions on MLE and MPLE for the first two of these three cases. However, on the third case, there is no exact solution and we use Matlab program to do the numerical calculations for analyzing and comparing MLE and MPLE. We observe how the changes of mean square errors using MLE and those using MPLE affected by the value changes of parameters.
    Reference: Arnold, B. C. and Press, S. J. (1989). “Compatible Conditional Distributions.” Journal of the American Statistical Association 84(405): 152-156.

    Arnold, B. C. and Strauss, D. (1991). “Pseudolikelihood Estimation: Some Examples.” Journal of the Indian Journal of Statistics 53(2): 233-243.

    Bickel, P. J. and Doksum, K. A. (1977). “Mathematical Statistics:basic ideas and selected topics.” San Francisco: Holden-Day.

    Strauss, D. and Ikeda. M. (1990). “Pseudolikelihood Estimation for Social Networks.” Journal of the American Association 85(409):204-212.

    郭名展(2014), 列聯表模型下MLE與MPLE之比較,國立政治大學應用數學系碩士論文。
    Description: 碩士
    國立政治大學
    應用數學研究所
    100751016
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G1007510162
    Data Type: thesis
    Appears in Collections:[應用數學系] 學位論文

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