Journal of the American Statistical Association

最新文章

• A Bayesian Hierarchical Model for Analysis of Single-Nucleotide Polymorphisms Diversity in Multilocus, Multipopulation Samples AI翻译
  作者：Guo, Feng; Dey, Dipak K.; Holsinger, Kent E.
  作者单位：Virginia Polytechnic Institute & State University; University of Connecticut; University of Connecticut
  摘要：The distribution of genetic variation among populations is conveniently measured by Wright's F-ST,. which is a scaled variance taking on values in [0,I]. For certain types of genetic markers and for single-nucleotide polymorphisms (SNPs) in particular, it is reasonable to presume that allelic differences at most loci are selectively neutral. For such loci, the distribution of genetic variation among populations is determined by the size of local populations, the pattern and rate of migration a...
• A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature Comment AI翻译
  作者：Higdon, D.
  作者单位：United States Department of Energy (DOE); Los Alamos National Laboratory
• Nonparametric Prediction in Measurement Error Models Comment AI翻译
  作者：Fan, Jianqing; Feng, Yang
  作者单位：Princeton University
• Semiparametric Efficient Estimation for Incomplete Longitudinal Binary Data, With Application to Smoking Trends AI翻译
  作者：Perin, Jamie; Preisser, John S.; Rathouz, Paul J.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Chicago
  摘要：Incomplete longitudinal data often are analyzed with estimating equations for inference on a parameter from a marginal mean regression model. Generalized estimating equations, although commonly used for incomplete longitudinal data, are invalid for data that are not missing completely at random. There exists a class of inverse probability weighted estimating equations that are valid under dropouts missing at random, including an easy-to-implement but inefficient member. A relatively computatio...
• Nonparametric Prediction in Measurement Error Models Comment AI翻译
  作者：Schennach, Susanne M.
  作者单位：University of Chicago
• Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models AI翻译
  作者：Shen, Yu; Ning, Jing; Qin, Jing
  作者单位：University of Texas System; UTMD Anderson Cancer Center; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
  摘要：Right-censored time-to-event data are often observed from a cohort of prevalent cases that are subject to length-biased sampling. Informative right censoring of data from the prevalent cohort within the population often makes it difficult to model risk factors on the unbiased failure times for the general population. because the observed failure times are length biased. In this paper. we consider two classes of flexible semiparametric models: the transformation models and the accelerated failu...
• Modeling Hazard Rates as Functional Data for the Analysis of Cohort Lifetables and Mortality Forecasting AI翻译
  作者：Chiou, Jeng-Min; Mueller, Hans-Georg
  作者单位：University of California System; University of California Davis
  摘要：As world populations age, the analysis of demographic mortality data and demographic predictions of future mortality have met with increasing interest. The study of mortality patterns and the forecasting of future mortality with its associated impacts on social welfare. health care, and societal planning has become a more pressing issue. An ideal set of data to study Patterns of change in long-term mortality is the well-known historical Swedish cohort mortality data. because of its high qualit...
• Computationally Efficient Nonparametric Importance Sampling AI翻译
  作者：Neddermeyer, Jan C.
  作者单位：Ruprecht Karls University Heidelberg
  摘要：The variance reduction established by importance sampling strongly depends on the choice of the importance sampling distribution. A good choice is often hard to achieve especially for high-dimensional integration problems. Nonparametric estimation of the optimal importance sampling distribution (known as nonparametric importance sampling) is a reasonable alternative to parametric approaches. In this article, nonparametric variants of both the self-normalized and the unnormalized importance sam...
• Logistic Regression With Brownian-Like Predictors AI翻译
  作者：Lindquist, Martin A.; McKeague, Ian W.
  作者单位：Columbia University; Columbia University
  摘要：This article introduces a new type of logistic regression model involving functional predictors of binary responses, and provides an extension of this approach to generalized linear models. The predictors are trajectories that have certain sample path properties in common with Brownian motion. Time points are treated as parameters of interest, and confidence intervals are developed tinder prospective and retrospective (case-control) sampling designs. In an application to functional magnetic re...
• Forward Regression for Ultra-High Dimensional Variable Screening AI翻译
  作者：Wang, Hansheng
  作者单位：Peking University
  摘要：Motivated by the seminal theory of Sure Independence Screening (Fan and Lv 2008, SIS), we investigate here another popular and classical variable screening method, namely, forward regression (FR). Our theoretical analysis reveals that FR can identify all relevant predictors consistently, even if the predictor dimension is substantially larger than the sample size. In particular, if the dimension of the true model is finite, FR can discover all relevant predictors within a finite number of step...