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題名：貝氏廣義隱藏式類別分析法在心理計量學的應用
書刊名：測驗年刊
作者：楊志堅 引用關係／楊志強 引用關係
作者(外文)：Yang, Chih-chien／Yang, Chih-chiang
出版日期：1999
卷期：46:2
頁次：頁73-84
主題關鍵詞：吉氏取樣；貝氏廣義隱藏類別分析；模式選取；酒精上癮；Gibbs sampling；Bayesian extended latent class analysis；Model selection；Alcohol dependence
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  • 被引用次數被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0)
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　　　　　吉氏取樣（Gibbs sampling）是近幾年來極受矚目的統計計算法之一，它讓貝氏統計（Bayesian Statistics）及推論可以在有限的計算資源（computing resources）下被實際運用的，更重要的是它的多樣性（flexibility），使許多以前幾乎不可能付諸實用的統計模式成為可能，這使得它成為近幾年研究貝氏統計的最主要工具。利用吉氏取樣來做貝氏隱藏類別分析（Bayesian Latent Class Analysis, BLCA）及貝氏廣義隱藏類別分析（Bayesian Extended Latent Class Analysis, BELCA）不僅在統計計算學上有其重要性而且在醫學統計或心理計量學上亦有其實用價值。本文以酒癮關聯模式之瞼例研究來說明如何以吉氏取樣來實踐BLCA及BELCA模式，並討論其估算過程（estimation procedure）、收斂診斷（convergence diagnosis）及模式選取（model selection）等主題研究結果顯示BLCA及BELCA確是一可供心理計量學上實際推廣運用的有效統計模式。
　　　　　Theis paper introduces latent class analysis and extended latent class analysis models by using Gibbs sampling to show the significance of the two models in practical areas of statistical computation, biomedical statistics and psychometrics. The Gibbs sampling method for Bayesian implementation reasons in recent years. One of the major advancements by using Gibbs sampling is that Gibbs sampling allows researchers to execute many complex Bayesian statistical models practically because of its flexibility. This important feature also makes Gibbs sampling become one of the most popular tools for Bayesian modeling. We further demonstrate the procedures of parameter estimation, convergence diagnosis, and model selection of both models engaging Gibbs sampling by using a practical example of NLSY alcohol dependence study.
期刊論文
1.Hoijtink, H.(1998)。Constrained latent class analysis using the Gibbs samples and posterior predictive P-values: applications to educational testing。Statistica Sincia，8，691-712。  new window
2.Melton, B.、Liang, K. Y.、Pulver, A. E.(1994)。Extended latent class approach to the study of familial/sporadic forms of a disease: its application to the study of the heterogeneity of schizophrenia。Genetic Epidemiology，11，311-327。  new window
3.Pauler, D. K.、Escobar, M. D.、Sweeney, J. A.、Greenhouse, J.(1996)。Mixture models for eye- tracking data: a case study。Statistics in Medicine，15，1365-1376。  new window
4.Stern, H. S.、Arcus, D.、Kagan, J.、Rubin, D. B.、Snidman, B.(1995)。Using mixture models in temperament research。International Journal of Behavioral Development，18，407-423。  new window
5.Wang, P.、Puterman, M. L.(1998)。Mixed Logistic Regression Models。Journal of Agricultural, Biological and Environmental Statistics，3(2)，175-200。  new window
6.Yang, I.、Becker, M. P.(1997)。Latent variable modeling of diagnostics accuracy。Biometrics，53，948-958。  new window
7.Akaike, H.(1973)。Statistical predictor identification。Annals of the Institute Statistical Mathematics，22，203-217。  new window
8.Lin, T. H.、Dayton, C. M.(1997)。Model Selection Information Criteria for Non-Nested Latent Class Models。Journal of Educational and Behavioral Statistics，22(3)，249-264。  new window
9.McLachlan, G. J.(1987)。On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture。Applied Statistics，36(3)，318-324。  new window
10.Sclove, L. S.(1987)。Application of model selection criteria to some problems in multivariate analysis。Psychometrika，52，333-343。  new window
11.Yang, C. C.、Muthén, B.、Yang, C. C.(1999)。Finite Mixture Multivariate Generalized Linear Models Using Gibbs Sampling and E-M Algorithm。Proceedings of the National Science Council Part A: Physical Science and Engineering，23，695-702。  new window
12.Dempster, Arthur P.、Laird, Nan M.、Rubin, Donald B.(1977)。Maximum likelihood from incomplete data via the EM algorithm。Journal of the Royal Statistical Society: Series B (Methodological)，39(1)，1-38。  new window
13.Schwarz, Gideon(1978)。Estimating the Dimension of a model。The Annals of Statistics，6(2)，461-464。  new window
14.Goodman, Leo A.(1974)。Exploratory latent structure analysis using both identifiable and unidentifiable models。Biometrika，61(2)，215-231。  new window
會議論文
1.Muthen, B.、Brown, H.、Khoo, S.、Yang, C. C.、Jo, B.(1996)。General growth mixture modeling of latent trajectory classes: perspectives and prospects。The Prevention Science and Methodology Groups meeting。Tempe, Arizona。  new window
2.Yang, C. C.、Muthén, B.(1997)。Latent class analyses using Gibbs sampling and EM algorithm。The 1997 IMS Asian and Pacific regional meeting joint with CIPS and CSA。Taipei。  new window
3.Yang, C. C.、Muthén, B.(1997)。Finite mixture of generalized linear models using Gibbs sampling and EM algorithm。The 1997 American Statistical Association (ASA), Anaheim Joint Statistic Meeting (JSM)。California。  new window
研究報告
1.Reiser, M.、Lin, Y.(1998)。A good-of-fit test for the latent class model when expected frequencies are small。Arizona State University。  new window
學位論文
1.Yan, C. C.(1998)。Finite mixture model selection with psychometrics(博士論文)。UCLA。  new window
圖書
1.Tanner, Martin A.(1996)。Tools for statistical inference。New York：Springer Verlag。  new window
2.The Ohio State University(1994)。National Longitudinal Surveys。Columbus, Ohio：Center for Human Resource Research。  new window
3.Bartholomew, D. J.(1987)。Latent Variable Models and Factor Analysis。London：Charles Griffin & Company LTD.。  new window
4.Best, N. G.、Cowles, M. K.、Vines, S. K.(1997)。CODA: convergence diagnosis and output analysis software for Gibbs sampling output。Cambridge：MRC Biostatistics Unit。  new window
5.Spiegelhalter D.、Thomas A.、Best, N.、Gilks, W.(1995)。Bayesian inference using Gibbs sampling (BUGS)。Cambridge：MRC Biostatistics Unit, IPH。  new window
6.Spiegeihalter D.、Thomas A.、Best, N.、Gilks, W.(1995)。BUGS examples。Cambridge：MRC Biostatistics Unit, IPH。  new window
7.Agresti, A.(1996)。An introduction to categorical data analysis。John Wiley & Sons Inc.。  new window
8.American Psychiatric Association(1994)。Diagnostic and statistical manual of mental disorders。American Psychiatric Association。  new window
其他
1.Muthén, B.，Hartford, T. C.(1998)。Covariates of alcohol dependence and abuse: a multivariate analysis of the DSM-IV criteria and symptom items in a national longitudinal survey of young adults。  new window
 
 
 
 
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