Journal of the American Statistical Association

最新文章

• Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax AI翻译
  作者：Sarkar, Abhra; Chabout, Jonathan; Macopson, Joshua Jones; Jarvis, Erich D.; Dunson, David B.
  作者单位：University of Texas System; University of Texas Austin; Howard Hughes Medical Institute; Rockefeller University; Duke University
  摘要：Studying the neurological, genetic, and evolutionary basis of human vocal communication mechanisms using animal vocalization models is an important field of neuroscience. The datasets typically comprise structured sequences of syllables or songs produced by animals from different genotypes under different social contexts. It has been difficult to come up with sophisticated statistical methods that appropriately model animal vocal communication syntax. We address this need by developing a novel...
• On Inverse Probability Weighting for Nonmonotone Missing at Random Data AI翻译
  作者：Sun, BaoLuo; Tchetgen, Eric J. Tchetgen
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：The development of coherent missing data models to account for nonmonotone missing at random (MAR) data by inverse probability weighting (IPW) remains to date largely unresolved. As a consequence, IPW has essentially been restricted for use only in monotone MAR settings. We propose a class of models for nonmonotone missing data mechanisms that spans the MAR model, while allowing the underlying full data law to remain unrestricted. For parametric specifications within the proposed class, we int...
• Statistical Methods for Standard Membrane-Feeding Assays to Measure Transmission Blocking or Reducing Activity in Malaria AI翻译
  作者：Swihart, Bruce J.; Fay, Michael P.; Miura, Kazutoyo
  作者单位：National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
  摘要：Transmission blocking vaccines for malaria are not designed to directly protect vaccinated people from malaria disease, but to reduce the probability of infecting other people by interfering with the growth of the malaria parasite in mosquitoes. Standard membrane-feeding assays compare the growth of parasites in mosquitoes from a test sample (using antibodies from a vaccinated person) compared to a control sample. There is debate about whether to estimate the transmission reducing activity (TR...
• Latent Variable Poisson Models for Assessing the Regularity of Circadian Patterns over Time AI翻译
  作者：Kim, Sungduk; Albert, Paul S.
  作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
  摘要：Many researchers in biology and medicine have focused on trying to understand biological rhythms and their potential impact on disease. A common biological rhythm is circadian, where the cycle repeats itself every 24 hours. However, a disturbance of the circadian pattern may be indicative of future disease. In this article, we develop new statistical methodology for assessing the degree of disturbance or irregularity in a circadian pattern for count sequences that are observed over time in a p...
• A Massive Data Framework for M-Estimators with Cubic-Rate AI翻译
  作者：Shi, Chengchun; Lu, Wenbin; Song, Rui
  作者单位：North Carolina State University
  摘要：The divide and conquer method is a common strategy for handling massive data. In this article, we study the divide and conquer method for cubic-rate estimators under the massive data framework. We develop a general theory for establishing the asymptotic distribution of the aggregated M-estimators using a weighted average with weights depending on the subgroup sample sizes. Under certain condition on the growing rate of the number of subgroups, the resulting aggregated estimators are shown to h...
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests AI翻译
  作者：Wager, Stefan; Athey, Susan
  作者单位：Stanford University
  摘要：Many scientific and engineering challengesranging from personalized medicine to customized marketing recommendationsrequire an understanding of treatment effect heterogeneity. In this article, we develop a nonparametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm. In the potential outcomes framework with unconfoundedness, we show that causal forests are pointwise consistent for the true treatment effect and have an asy...
• Cross-Screening in Observational Studies That Test Many Hypotheses AI翻译
  作者：Zhao, Qingyuan; Small, Dylan S.; Rosenbaum, Paul R.
  作者单位：University of Pennsylvania
  摘要：We discuss observational studies that test many causal hypotheses, either hypotheses about many outcomes or many treatments. To be credible an observational study that tests many causal hypotheses must demonstrate that its conclusions are neither artifacts of multiple testing nor of small biases from nonrandom treatment assignment. In a sense that needs to be defined carefully, hidden within a sensitivity analysis for nonrandom assignment is an enormous correction for multiple testing: In the ...
• Modeling Motor Learning Using Heteroscedastic Functional Principal Components Analysis AI翻译
  作者：Backenroth, Daniel; Goldsmith, Jeff; Harran, Michelle D.; Cortes, Juan C.; Krakauer, John W.; Kitago, Tomoko
  作者单位：Columbia University; Columbia University; Johns Hopkins University; Johns Hopkins University
  摘要：We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretab...
• Scalable Bayesian Modeling, Monitoring, and Analysis of Dynamic Network Flow Data AI翻译
  作者：Chen, Xi; Irie, Kaoru; Banks, David; Haslinger, Robert; Thomas, Jewell; West, Mike
  作者单位：Duke University; University of Tokyo
  摘要：Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of Internet browser traffic flow through site-segments of an international news website, we present Bayesian analyses of two linked classes of models which, in tandem, allow fast, scalable, and interpretable Bayesian inference. We first develop flexible state-space models for streaming count data, able to...
• A Weighted Edge-Count Two-Sample Test for Multivariate and Object Data AI翻译
  作者：Chen, Hao; Chen, Xu; Su, Yi
  作者单位：University of California System; University of California Davis; Duke University
  摘要：Two-sample tests for multivariate data and non-Euclidean data are widely used in many fields. Parametric tests are mostly restrained to certain types of data that meets the assumptions of the parametric models. In this article, we study a nonparametric testing procedure that uses graphs representing the similarity among observations. It can be applied to any data types as long as an informative similarity measure on the sample space can be defined. The classic test based on a similarity graph ...