TY - JOUR
AU - Chatterjee, Snigdhansu
AB - BOOK REVIEWS 411 would have been much more helpful to use graphical methods to display the This is important because even the Cox regression model in the introductory data sets, perhaps along the lines of the elegant Trellis/Lattice graphics imple- chapter immediately portends the difficulty of the field. mented by the nlme package in R and S, and described by Pinheiro and Bates Chapter 3, covering the fundamental concepts of competing risks, outlines (2004). Much material is devoted to explaining basic statistical inference topics two approaches to interpreting data: the conceptually pleasing latent variable like ML estimation, basic distributions, confidence intervals, and the like. Any- approach and the more tractable bivariate random variable approach. Unfortu- one contemplating working with mixed modeling should already have a good nately, this critical chapter is one of the book’s shortest. About a dozen func- understanding of these concepts. There is no need to clutter an otherwise won- tions are defined mathematically here. A few comments are made regarding derful book with this material, especially when the zeal for simplicity stretches interpretation and relationships. However, readers would greatly benefit from technical accuracy, as with the confidence intervals (Chap. 4), where the gen- extensive examples 
TI - Structural Equation Modeling, A Bayesian Approach
JF - Technometrics
DO - 10.1198/tech.2008.s907
DA - 2008-08-01
UR - https://www.deepdyve.com/lp/taylor-francis/structural-equation-modeling-a-bayesian-approach-5DFMWWXsDv
SP - 411
EP - 412
VL - 50
IS - 3
DP - DeepDyve
ER -