Journal of the American Statistical Association

最新文章

• Using principal component analysis and correspondence analysis for estimation in latent variable models AI翻译
  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...
• The randomized clinical trial AI翻译
  作者：Harrington, DP
  作者单位：Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Harvard T.H. Chan School of Public Health
• Analysis of smoking trends with incomplete longitudinal binary responses AI翻译
  作者：Preisser, JS; Galecki, AT; Lohman, KK; Wagenknecht, LE
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Michigan System; University of Michigan; Wake Forest University; Wake Forest Baptist Medical Center
  摘要：The generalized estimating equations procedure (GEE) widely applied in the analysis of correlated binary data requires that missing data depend only on remote covariates or that they be missing completely at random (MCAR); otherwise GEE regression parameter estimates are biased. A weighted generalized estimating equations (WGEE) approach that accounts for dropouts under the less stringent assumption of missing at random (MAR) through dependence on observed responses gives unbiased estimation o...
• Linear regression with current status data AI翻译
  作者：Shen, XT
  作者单位：University System of Ohio; Ohio State University
  摘要：In survival analysis, a linear model often provides an adequate approximation after a suitable transformation of the survival times and possibly of the covariates. This article proposes a semiparametric regression method for estimating the regression parameter in the linear model without specifying the distribution of the random error, where the response variable is subject to so-called case 1 interval censoring. The method uses a constructed random-sieve likelihood and constraints, combining ...
• Multidimensional longitudinal data: Estimating a treatment effect from continuous, discrete, or time-to-event response variables AI翻译
  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...
• Environmental statistics AI翻译
  作者：Guttorp, P
  作者单位：University of Washington; University of Washington Seattle
• Estimation and inference for logistic regression with covariate misclassification and measurement error in main study/validation study designs AI翻译
  作者：Spiegelman, D; Rosner, B; Logan, R
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital
  摘要：In epidemiological studies, continuous covariates often are measured with error and categorical covariates often are misclassified. Using the logistic regression model to represent the relationship between the binary outcome and the perfectly measured and classified covariates, the model for the observed main study data is derived. This derivation relies on the assumption that the error in the continuous covariates is multivariate normally distributed and uses a chain of logistic regression mo...
• Time-varying network tomography: Router link data AI翻译
  作者：Cao, J; Davis, D; Vander Wiel, S; Yu, B
  作者单位：University of California System; University of California Berkeley
  摘要：The origin-destination (OD) traffic matrix of a computer network is useful for solving problems in design, routing, configuration debugging, monitoring, and pricing. Directly measuring this matrix is not usually feasible, but less informative Link measurements are easy to obtain. This work studies the inference of OD byte counts from link byte counts measured at router interfaces under a fixed routing scheme. A basic model of the OD counts assumes that they are independent normal over OD pairs...
• Empirical Bayes: Past, present and future AI翻译
  作者：Carlin, BP; Louis, TA
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; RAND Corporation
• Linear and log-linear models AI翻译
  作者：Christensen, R
  作者单位：University of New Mexico