作者：Lo, AW
作者单位：Massachusetts Institute of Technology (MIT)
作者：Lynn, HS; McCulloch, CE
作者单位：Rho; Cornell University
摘要：Correspondence analysis (CA) and principal component analysis (PCA) are often used to describe multivariate data. In certain applications they have been used for estimation in latent variable models. The theoretical basis for such inference is assessed in generalized linear models where the linear predictor equals alpha(j) + x(i)beta(j) or a(j) - b(j) (x(i) - u(j))(2), (i = 1, ..., n; j = 1, ..., m), and x(i) is treated as a latent fixed effect. The PCA and CA eigenvectors/column scores are ev...
作者：Gray, SM; Brookmeyer, R
作者单位：Lancaster University; Johns Hopkins University
摘要：Multidimensional data arise when a number of different response variables are required to measure the outcome of interest. Examples of such outcomes include quality of life, cognitive ability, and health status. The goal of this: article is to develop a methodology to estimate a treatment effect from multidimensional data that have been collected longitudinally using continuous, discrete, or time-to-event responses or a mixture of these types of responses. A transformation of the time scale th...
