2009

最新文章

• Bayesian Mixture Labeling by Highest Posterior Density AI翻译
  作者：Yao, Weixin; Lindsay, Bruce G.
  作者单位：Kansas State University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：A fundamental problem for Bayesian mixture model analysis is label switching, which occurs as a result of the nonidentifiability of the mixture components under symmetric priors. We propose two labeling methods to solve this problem. The first method, denoted by PM(ALG), is based on the posterior modes and an ascending algorithm generically denoted ALG. We use each Markov chain Monte Carlo sample as the starting point in an ascending algorithm, and label the sample based on the mode of the pos...
• Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign AI翻译
  作者：Hansen, Ben B.; Bowers, Jake
  作者单位：University of Michigan System; University of Michigan; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Early in the twentieth century, Fisher and Neyman demonstrated how to infer effects of agricultural interventions using only the very weakest of assumptions, by randomly varying which plots were to be manipulated. Although the methods permitted uncontrolled variation between experimental units. the), required strict control over assignment of interventions; this hindered their application to field studies With human subjects, who ordinarily could not be compelled to comply with experimenters' ...
• Jackknife Empirical Likelihood AI翻译
  作者：Jing, Bing-Yi; Yuan, Junqing; Zhou, Wang
  作者单位：Hong Kong University of Science & Technology; National University of Singapore
  摘要：Empirical likelihood has been found very useful in many different occasions. However, when applied directly to some more complicated statistics such as U-statistics, it runs into serious computational difficulties. In this paper, we introduce a so-called jackknife empirical likelihood (JEL) method. The new method is extremely simple to use in practice. In particular. the JEL is shown to be very effective in handling one and two-sample U-statistics. The JEL can be potentially useful for other n...
• Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep AI翻译
  作者：Crainiceanu, Ciprian M.; Caffo, Brian S.; Di, Chong-Zhi; Punjabi, Naresh M.
  作者单位：Johns Hopkins University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcome...
• A Bayesian Reassessment of Nearest-Neighbor Classification AI翻译
  作者：Cucala, Lionel; Marin, Jean-Michel; Robert, Christian P.; Titterington, D. M.
  作者单位：Universite Paris Saclay; Universite PSL; Universite Paris-Dauphine; University of Glasgow
  摘要：The k-nearest-neighbor (knn) procedure is a well-known deterministic method used in supervised classification. This article proposes a reassessment of this approach as a statistical technique derived from a proper probabilistic model; in particular, we modify the assessment found in Holmes and Adams, and evaluated by Manocha and Girolami, where the underlying probabilistic model is not completely well defined. Once provided with a clear probabilistic basis for the knn procedure, we derive comp...
• 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