Journal of the royal statistical society series b-methodological最新文献_第5页

On Use of the EM Algorithm for Penalized Likelihood Estimation EM算法在惩罚似然估计中的应用

Journal of the royal statistical society series b-methodological Pub Date : 1990-07-01 DOI: 10.1111/J.2517-6161.1990.TB01798.X

P. Green

{"title":"On Use of the EM Algorithm for Penalized Likelihood Estimation","authors":"P. Green","doi":"10.1111/J.2517-6161.1990.TB01798.X","DOIUrl":"https://doi.org/10.1111/J.2517-6161.1990.TB01798.X","url":null,"abstract":"SUMMARY The EM algorithm is a popular approach to maximum likelihood estimation but has not been much used for penalized likelihood or maximum a posteriori estimation. This paper discusses properties of the EM algorithm in such contexts, concentrating on rates of conver- gence, and presents an alternative that is usually more practical and converges at least as quickly. The EM algorithm is a general approach to maximum likelihood estimation, rather than a specific algorithm. Dempster et al. (1977) discussed the method and derived basic properties, demonstrating that a variety of procedures previously developed rather informally could be unified. The common strand to problems where the approach is applicable is a notion of 'incomplete data'; this includes the conventional sense of 'missing data' but is much broader than that. The EM algorithm demon- strates its strength in situations where some hypothetical experiment yields data from which estimation is particularly convenient and economical: the 'incomplete' data actually at hand are regarded as observable functions of these 'complete' data. The resulting algorithms, while usually slow to converge, are often extremely simple and remain practical in large problems where no other approaches may be feasible. Dempster et al. (1977) briefly refer to the use of the same approach to the problem of finding the posterior mode (maximum a posteriori estimate) in a Bayesian estima-","PeriodicalId":17425,"journal":{"name":"Journal of the royal statistical society series b-methodological","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87336707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 393

Models for exceedances over high thresholds 超过高阈值的超标模型

Journal of the royal statistical society series b-methodological Pub Date : 1990-07-01 DOI: 10.1111/J.2517-6161.1990.TB01796.X

A. Davison, Richard L. Smith

引用次数: 1583

Estimation and Inference by Compact Coding 基于压缩编码的估计与推理

Journal of the royal statistical society series b-methodological Pub Date : 1987-07-01 DOI: 10.1111/J.2517-6161.1987.TB01695.X

C. S. Wallace, P. Freeman

引用次数: 586

Bartlett Adjustments to the Likelihood Ratio Statistic and the Distribution of the Maximum Likelihood Estimator 巴特利特似然比统计量的调整及最大似然估计量的分布

Journal of the royal statistical society series b-methodological Pub Date : 1984-07-01 DOI: 10.1111/J.2517-6161.1984.TB01321.X

O. Barndorff-Nielsen, D. Cox

引用次数: 144

Efficient Nonparametric Estimation of Mixture Proportions 混合比例的有效非参数估计

Journal of the royal statistical society series b-methodological Pub Date : 1984-07-01 DOI: 10.1111/J.2517-6161.1984.TB01319.X

P. Hall, D. Titterington

引用次数: 40

Poisson Convergence for Dissociated Statistics 解离统计的泊松收敛性

Journal of the royal statistical society series b-methodological Pub Date : 1984-07-01 DOI: 10.1111/J.2517-6161.1984.TB01311.X

G. Eagleson

引用次数: 57

Multinomial goodness-of-fit tests 多项拟合优度检验

Journal of the royal statistical society series b-methodological Pub Date : 1984-07-01 DOI: 10.1111/J.2517-6161.1984.TB01318.X

N. Cressie, Timothy R. C. Read

引用次数: 1244

Piecewise‐Deterministic Markov Processes: A General Class of Non‐Diffusion Stochastic Models 分段确定性马尔可夫过程:一类非扩散随机模型

Journal of the royal statistical society series b-methodological Pub Date : 1984-07-01 DOI: 10.1111/J.2517-6161.1984.TB01308.X

Mark H. A. Davis

引用次数: 1019

Asymptotic Behaviour of Conditional Maximum Likelihood Estimators in a Certain Exponential Model 一类指数模型中条件极大似然估计的渐近性

Journal of the royal statistical society series b-methodological Pub Date : 1984-07-01 DOI: 10.1111/J.2517-6161.1984.TB01316.X

S. Bar-Lev

引用次数: 6

Outlier Models and Prior Distributions in Bayesian Linear Regression 贝叶斯线性回归中的离群值模型和先验分布

Journal of the royal statistical society series b-methodological Pub Date : 1984-07-01 DOI: 10.1111/J.2517-6161.1984.TB01317.X

M. West

{"title":"Outlier Models and Prior Distributions in Bayesian Linear Regression","authors":"M. West","doi":"10.1111/J.2517-6161.1984.TB01317.X","DOIUrl":"https://doi.org/10.1111/J.2517-6161.1984.TB01317.X","url":null,"abstract":"SUMMARY Bayesian inference in regression models is considered using heavy-tailed error distri- butions to accommodate outliers. The particular class of distributions that can be con- structed as scale mixtures of normal distributions are examined and use is made of them as both error models and prior distributions in Bayesian linear modelling, includ- ing simple regression and more complex hierarchical models with structured priors depending on unknown hyperprior parameters. The modelling of outliers in nominally normal linear regression models using alternative error distributions which are heavy-tailed relative to the normal provides an automatic means of both detecting and accommodating possibly aberrant observations. Such realistic models do, however, often lead to analytically intractable analyses with complex posterior distributions in several dimensions that are difficult to summarize and understand. In this paper we consider a special yet rather wide class of heavy-tailed, unimodal and symmetric error distributions for which the analyses, though apparently intractable, can be examined in some depth by exploiting certain properties of the assumed error form. The distributions concerned are those that can be con- structed as scale mixtures of normal distributions. In his paper concerning location parameters, de Finetti (1961) discusses such distributions and suggests the hypothetical interpretation that \"each observation is taken using an instrument with normal error, but each time chosen at random from a collection of instruments of different precisions, the distribution of the","PeriodicalId":17425,"journal":{"name":"Journal of the royal statistical society series b-methodological","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1984-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84397291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 251