Journal of the American Statistical Association

最新文章

• Joint Modeling of Self-Rated Health and Changes in Physical Functioning AI翻译
  作者：Hubbard, Rebecca A.; Inoue, Lurdes Y. T.; Diehr, Paula
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：Self-rated health is an important indicator of future morbidity and mortality. Research has indicated that self-rated health is related to both levels of and changes in physical functioning. But no previous study has jointly modeled longitudinal functional Status and self-rated health trajectories. We propose a joint model for self-rated health and physical functioning that describes the relationship between perceptions of health and the rate of change of physical functioning or disability. Ou...
• Estimating the Time-Varying Rate of Transmission of SARS in Singapore and Hong Kong Under Two Environments AI翻译
  作者：Kuk, Anthony Y. C.; Tan, C. C.
  作者单位：National University of Singapore; National University of Singapore
  摘要：In modeling disease transmission there is much emphasis on how many people are infected but less attention is paid to when the infections occur, and the unrealistic assumption of constant infectiousness is often made. We propose a missing data formulation that enables us to apply the ECME algorithm to estimate a discretized intensity function of an inhomogeneous Poisson process. This approach requires interval-censored data only, but known infection times can be incorporated as well. We apply ...
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions AI翻译
  作者：Johnstone, Iain M.; Lu, Arthur Yu
  作者单位：Stanford University
  摘要：Principal components analysis (PCA) is a classic method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. Contemporary datasets often have p comparable with or even much larger than n. Our main assertions, in such settings, are (a) that some initial reduction in dimensionality is desirable before applying any PCA-type search for principal modes, and (b) the initial reduction in dimensionality is best achieved by working in a basi...
• Singular Value Decomposition-Based Alternative Splicing Detection AI翻译
  作者：Hu, Jianhua; He, Xuming; Cote, Gilbert J.; Krahe, Ralf
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Altered alternative splicing has been identified as an important factor in tumorigenesis. The Affymetrix exon tiling array is designed for detecting alternative splicing events in a transcriptome-wide fashion; however. there are currently few analysis tools that have been well Studied for effective detection of alternative splicing events. We propose a new screening procedure based on singular value decomposition (SVD) of the residual matrix from a robust additive model fit to probe selection ...
• Prior Distributions From Pseudo-Likelihoods in the Presence of Nuisance Parameters AI翻译
  作者：Ventura, Laura; Cabras, Stefano; Racugno, Walter
  作者单位：University of Padua; University of Cagliari
  摘要：Consider a mode parameterized by 0 = (psi, lambda), where psi is the parameter of interest. The problern of eliminating the nuisance parameter lambda the nuis can be tackled by resorting to a pseudo-likelihood function L*(psi) for psi-namely a function of psi only and the data y with properties similar to those of a likelihood function. If one treats L*(psi) as a true likelihood. the posterior distribution pi*(psi vertical bar y) alpha pi(psi)L*(psi) for psi can be considered. where pi(psi) is...
• 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...