| 書目名稱 | Random Effect and Latent Variable Model Selection |
| 編輯 | David B. Dunson |
| 視頻video | http://file.papertrans.cn/822/821039/821039.mp4 |
| 概述 | Practically motivated and clear overview of methods for selecting random effects..Leading researchers in the field describe how to appropriately test variance components equal to zero..Bayesian and fr |
| 叢書名稱 | Lecture Notes in Statistics |
| 圖書封面 |  |
| 描述 | Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors |
| 出版日期 | Book 2008 |
| 關(guān)鍵詞 | Factor analysis; Generalized linear model; Latent variable model; Latent variables; Likelihood; Variance; |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-76721-5 |
| isbn_softcover | 978-0-387-76720-8 |
| isbn_ebook | 978-0-387-76721-5Series ISSN 0930-0325 Series E-ISSN 2197-7186 |
| issn_series | 0930-0325 |
| copyright | Springer-Verlag New York 2008 |
|Archiver|手機(jī)版|小黑屋|
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