Journal of the American Statistical Association

最新文章

• Empirical Likelihood in Missing Data Problems AI翻译
  作者：Qin, Jing; Zhang, Biao; Leung, Denis H. Y.
  作者单位：National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); University System of Ohio; University of Toledo; Singapore Management University
  摘要：Missing data is a ubiquitous problem in medical and social sciences. It is well known that inferences based only on the complete data may not only lose efficiency, but may also lead to biased results if the data is not missing completely at random (MCAR). The inverse-probability weighting method proposed by Horvitz and Thompson (1952) is a popular alternative when the data is not MCAR. The Horvitz-Thompson method, however, is sensitive to the inverse weights and may suffer from loss of efficie...
• Statistical Estimation in Generalized Multiparameter Likelihood Models AI翻译
  作者：Cheng, Ming-Yen; Zhang, Wenyang; Chen, Lu-Hung
  作者单位：National Taiwan University; University of London; University College London; University of Bath
  摘要：Multiparameter likelihood models (MLMs) with multiple covariates have a wide range of applications: however. they encounter the curse of dimensionality problem when the dimension of the covariates is large. We develop a generalized multiparameter likelihood model that copes with multiple covariates and adapts to dynamic structural changes well. It includes some popular models, such as the partially linear and varying-coefficient models, as special cases. We present a simple, effective two-step...
• Testing for the Supremacy of a Multinomial Cell Probability AI翻译
  作者：Nettleton, Dan
  作者单位：Iowa State University
  摘要：Tests for the supremacy of a multinomial cell probability are developed. The tested null hypothesis states that a particular cell of interest is not more probable than all others, Rejection of this null leads to the conclusion that the cell of interest has a strictly greater probability than all other cells. The null hypothesis constrains the multinomial probability vector to a nonconvex region that is a union of closed convex cones. The likelihood ratio test for this problem is derived and sh...
• A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring AI翻译
  作者：Othus, Megan; Li, Yi; Tiwari, Ram C.
  作者单位：Harvard University; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; US Food & Drug Administration (FDA); National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI)
  摘要：Modern cancer treatments have substantially improved cure rates and have generated a great interest in and need for proper statistical tools to analyze survival data with nonnegligible cure fractions. Data with Cure fractions often ire complicated by dependent censoring, and the analysis of this type of data typically involves untestable parametric assumptions on the dependence of the censoring mechanism and the true survival times. Motivated by the analysis of prostate cancer survival trends,...
• Intrinsically Autoregressive Spatiotemporal Models With Application to Aggregated Birth Outcomes AI翻译
  作者：Norton, Jonathan D.; Niu, Xu-Feng
  作者单位：US Food & Drug Administration (FDA); State University System of Florida; Florida State University
  摘要：A class of hierarchical Bayesian models is proposed for adverse birth outcomes such as preterm birth, which are conditional binomial distribution. The log-odds of an adverse outcome in a particular county, logit(p(i)), follow a linear model that includes observed covariates and normally-distributed random effects. Spatial dependence between neighboring regions is allowed for by including an intrinsically autoregressive (IAR) prior or air IAR convolution prior in the linear predictor. Temporal ...
• Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis AI翻译
  作者：Chen, Lisha; Buja, Andreas
  作者单位：Yale University; University of Pennsylvania
  摘要：In the past decade there has been a resurgence of interest in nonlinear dimension reduction. Among new proposals are Local Linear Embedding, Isomap, and Kernel Principal Components Analysis which all construct global low-dimensional embeddings from local affine or metric information, We introduce a competing method called Local Multidimensional Scaling (LMDS). Like LLE, Isomap, and KPCA, LMDS constructs its global embedding from local information, but it uses instead a combination of MDS and f...
• A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature AI翻译
  作者：Lemos, Ricardo T.; Sanso, Bruno
  作者单位：Universidade de Lisboa; Universidade de Lisboa; University of California System; University of California Santa Cruz
  摘要：We consider the problem of fitting a statistical model to 30 years of sea Surface temperature records collected over a large portion of the Northern Atlantic. The observations were collected sparsely in space and time with different levels of accuracy. The purpose of the model is to produce an atlas of oceanic properties, including climatological mean fields, estimates of historical trends, and a spatio-temporal reconstruction of the anomalies, i.e., the transient deviations from the climatolo...
• Variable Selection for Partially Linear Models With Measurement Errors AI翻译
  作者：Liang, Hua; Li, Runze
  作者单位：University of Rochester; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-called correction-for-attenuation approach, whereas the second procedure corrects the bias by using ortho...
• Bayesian Modeling of Uncertainty in Ensembles of Climate Models AI翻译
  作者：Smith, Richard L.; Tebaldi, Claudia; Nychka, Doug; Mearns, Linda O.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; National Center Atmospheric Research (NCAR) - USA
  摘要：Projections of future climate change caused by increasing greenhouse gases depend critically on numerical climate model, coupling the ocean and atmosphere (global climate models [GCMs]). However, different models differ substantially in their projections, which raises the question of how the different models can best be combined into a probability distribution of future climate change. For this analysis, we have collected both Current and future projected mean temperatures produced by nine cli...
• Nonparametric Prediction in Measurement Error Models AI翻译
  作者：Carroll, Raymond J.; Delaigle, Aurore; Hall, Peter
  作者单位：Texas A&M University System; Texas A&M University College Station; University of Bristol; University of Melbourne; University of California System; University of California Davis
  摘要：Predicting the value of it variable Y corresponding to a future value of an explanatory variable X, based on a sample of previously observed independent data pairs (X-1, Y-1),...,(X-n, Y-n,) distributed like (X, Y), is very important in statistics. In the error-free case, where X is observed accurately, this problem is strongly related to that of standard regression estimation, since prediction of Y can be achieved via estimation of the regression Curve E(Y vertical bar X). When the observed X...