國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

狀態轉換跳躍模型下Supplemented Expectation Ma...

吳聲杰

狀態轉換跳躍模型下Supplemented Expectation Maximization演算法與Gibbs Sampling演算法之參數估計之變異數估計 = Estimating Variance of Parameter Estimators by Supplemented Expectation Maximization and Gibbs Sampling Algorithm in Regime-Switching Jump Model

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	Estimating Variance of Parameter Estimators by Supplemented Expectation Maximization and Gibbs Sampling Algorithm in Regime-Switching Jump Model
作者:	吳聲杰,
其他團體作者:	國立高雄大學
出版地:	[高雄市]
出版者:	撰者;
出版年:	2009[民98]
面頁冊數:	88面圖、表 : 30公分;
標題:	EM演算法
標題:	EM algorithm
電子資源:	http://handle.ncl.edu.tw/11296/ndltd/04191580782355823278
附註:	參考書目：面
附註:	指導教授：林士貴
摘要註:	Fuh和Lin (2004) 提出馬可夫轉換跳躍模型(Markov-switching jump model)，此模型假設訊息來臨到達率有多個狀態，並且此多個狀態服從馬可夫過程。本研究將延續馬可夫轉換跳躍模型，假設事件狀態只有兩種，此模型稱為狀態轉換跳躍模型(regime-switching jump model)，由於狀態變數、跳躍次數與跳躍幅度是隱藏變數(hidden variable)，無法從觀測值中獲得，通常難以直接計算參數的最大概似估計式，因此本文使用expectation maximization (EM)演算法估計狀態轉換跳躍模型之參數，並且利用supplemented EM (SEM)演算法估計參數估計式之變異數；另外並假設所有參數先驗分配為均勻分配下，利用Gibbs sampling 演算法進行參數估計與參數之變異數估計。在檢定方面，本文利用最大概似估計式之漸進常態的性質來檢定參數之顯著性，且使用概似比檢定法討論道瓊三十成分股之狀態轉換跳躍模型分析。 Fuh and Lin (2004) proposed a Markov-switching jump model in which economic states are assumed to describe the possibly different arrival rates of the information. In this research, we investigate the model in two states setting, the so called regime-switching jump model. Estimation of the model parameters by maximum likelihood estimation is often difficult, however, since the jump sizes, the jump frequencies and the states are hidden variables. In such an incomplete data problem, we estimate the parameters by using expectation maximization (EM) algorithm and Gibbs sampling algorithm, and the variance of parameter estimators by using supplemented EM (SEM) algorithm and Gibbs sampling algorithm. In the empirical analysis, we investigate all thirty Dow Jones Industrial stocks to find more suitable model for a jump diffusion model, a regime-switching jump model with independent jump sizes and a regime-switching jump model with dependent jump sizes by asymptotic normality of the maximum likelihood estimator, and likelihood ratio test.

狀態轉換跳躍模型下Supplemented Expectation Maximization演算法與Gibbs Sampling演算法之參數估計之變異數估計 = Estimating Variance of Parameter Estimators by Supplemented Expectation Maximization and Gibbs Sampling Algorithm in Regime-Switching Jump Model
吳, 聲杰

狀態轉換跳躍模型下Supplemented Expectation Maximization演算法與Gibbs Sampling演算法之參數估計之變異數估計 = Estimating Variance of Parameter Estimators by Supplemented Expectation Maximization and Gibbs Sampling Algorithm in Regime-Switching Jump Model / 吳聲杰撰 - [高雄市] : 撰者, 2009[民98]. - 88面 ; 圖、表 ; 30公分.
參考書目：面指導教授：林士貴.
EM演算法EM algorithm

狀態轉換跳躍模型下Supplemented Expectation Maximization演算法與Gibbs Sampling演算法之參數估計之變異數估計 = Estimating Variance of Parameter Estimators by Supplemented Expectation Maximization and Gibbs Sampling Algorithm in Regime-Switching Jump Model
LDR:03415nam0a2200277 450 001 220372
005 20170214093954.0
009 220372
010 0 $b 平裝
010 0 $b 精裝
100 $a 20170214y2009 k y0chiy09 ea
101 1 $a eng $d chi $d eng
102 $a tw
105 $a ak am 000yy
200 1 $a 狀態轉換跳躍模型下Supplemented Expectation Maximization演算法與Gibbs Sampling演算法之參數估計之變異數估計 $d Estimating Variance of Parameter Estimators by Supplemented Expectation Maximization and Gibbs Sampling Algorithm in Regime-Switching Jump Model $z eng $f 吳聲杰撰
210 $a [高雄市] $c 撰者 $d 2009[民98]
215 0 $a 88面 $c 圖、表 $d 30公分
300 $a 參考書目：面
300 $a 指導教授：林士貴
328 $a 碩士論文--國立高雄大學統計學研究所
330 $a Fuh和Lin (2004) 提出馬可夫轉換跳躍模型(Markov-switching jump model)，此模型假設訊息來臨到達率有多個狀態，並且此多個狀態服從馬可夫過程。本研究將延續馬可夫轉換跳躍模型，假設事件狀態只有兩種，此模型稱為狀態轉換跳躍模型(regime-switching jump model)，由於狀態變數、跳躍次數與跳躍幅度是隱藏變數(hidden variable)，無法從觀測值中獲得，通常難以直接計算參數的最大概似估計式，因此本文使用expectation maximization (EM)演算法估計狀態轉換跳躍模型之參數，並且利用supplemented EM (SEM)演算法估計參數估計式之變異數；另外並假設所有參數先驗分配為均勻分配下，利用Gibbs sampling 演算法進行參數估計與參數之變異數估計。在檢定方面，本文利用最大概似估計式之漸進常態的性質來檢定參數之顯著性，且使用概似比檢定法討論道瓊三十成分股之狀態轉換跳躍模型分析。 Fuh and Lin (2004) proposed a Markov-switching jump model in which economic states are assumed to describe the possibly different arrival rates of the information. In this research, we investigate the model in two states setting, the so called regime-switching jump model. Estimation of the model parameters by maximum likelihood estimation is often difficult, however, since the jump sizes, the jump frequencies and the states are hidden variables. In such an incomplete data problem, we estimate the parameters by using expectation maximization (EM) algorithm and Gibbs sampling algorithm, and the variance of parameter estimators by using supplemented EM (SEM) algorithm and Gibbs sampling algorithm. In the empirical analysis, we investigate all thirty Dow Jones Industrial stocks to find more suitable model for a jump diffusion model, a regime-switching jump model with independent jump sizes and a regime-switching jump model with dependent jump sizes by asymptotic normality of the maximum likelihood estimator, and likelihood ratio test.
510 1 $a Estimating Variance of Parameter Estimators by Supplemented Expectation Maximization and Gibbs Sampling Algorithm in Regime-Switching Jump Model $z eng
610 0 $a EM演算法 $a Gibbs sampling演算法 $a SEM演算法 $a 馬可夫轉換跳躍模型
610 1 $a EM algorithm $a Gibbs sampling algorithm $a SEM algorithm $a regime-switching jump model
681 $a 008M/0019 $b 343201 2644 $v 2007年版
700 1 $a 吳 $b 聲杰 $4 撰 $3 354039
712 0 2 $a 國立高雄大學 $b 統計學研究所 $3 166081
801 0 $a tw $b 國立高雄大學 $c 20091020 $g CCR
856 7 $2 http $u http://handle.ncl.edu.tw/11296/ndltd/04191580782355823278