Journal of the American Statistical Association

最新文章

• Model Selection Criteria for Missing-Data Problems Using the EM Algorithm AI翻译
  作者：Ibrahim, Joseph G.; Zhu, Hongtu; Tang, Niansheng
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Yunnan University
  摘要：We consider novel methods for the Computation of model selection criteria in missing-data problems based on the output of the EM algorithm The methodology is very general and can be applied to numerous simulations involving incomplete data within an EM framework, from covariates missing at random in arbitrary regression models to nonignorably missing longitudinal responses and/or covariates. Toward this goal, we develop a class of information criteria for missing-data problems called ICH,Q, wh...
• Exploring Voting Blocs Within the Irish Electorate: A Mixture Modeling Approach AI翻译
  作者：Gormley, Isobel Claire; Murphy, Thomas Brendan
  作者单位：University College Dublin
  摘要：Irish elections use a voting system called proportion representation by means of a single transferable vote(PR-STV). Under this system, voters express their vote by ranking some (or all) of the candidates in order of preference. Which candidates are elected is determined through a series of counts where candidates are eliminated and surplus votes are distributed. The electorate in any election forms a heterogeneous population: that is voters with different political and ideological persuasions...
• Semiparametric analysis of heterogeneous data using varying-scale generalized linear models AI翻译
  作者：Xie, Minge; Simpson, Douglas G.; Carroll, Raymond J.
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Illinois System; University of Illinois Urbana-Champaign; Texas A&M University System; Texas A&M University College Station
  摘要：This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric components of the models. Such models provide a means to adapt for heterogeneity in the data due to varying exposures, varying levels of aggregation, and so on. The class of models considered includes generalized partially linear models and nonparametrically scale...
• Laplace periodogram for time series analysis AI翻译
  作者：Li, Ta-Hsin
  摘要：A new type of periodogram, called the Laplace periodogram, is derived by replacing least squares with least absolute deviations in the harmonic regression procedure that produces the ordinary periodogram of a time series. An asymptotic analysis reveals a connection between the Laplace periodogram and the zero-crossing spectrum. This relationship provides a theoretical justification for use of the Laplace periodogram as a nonparametric tool for analyzing the serial dependence of time series dat...
• Survival analysis with quantile regression models AI翻译
  作者：Peng, Limin; Huang, Yijian
  作者单位：Emory University; Rollins School Public Health
  摘要：Quantile regression offers great flexibility in assessing covariate effects on event times, thereby attracting considerable interests in its applications in survival analysis. But currently available methods often require stringent assumptions or complex algorithms. In this article we develop a new quantile regression approach for survival data subject to conditionally independent censoring. The proposed martingale-based estimating equations naturally lead to a simple algorithm that involves m...
• Toward causal inference with interference AI翻译
  作者：Hudgens, Michael G.; Halloran, M. Elizabeth
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this article, we consider a...
• Inference for a proton accelerator using convolution models AI翻译
  作者：Lee, Herbert K. H.; Sanso, Bruno; Zhou, Weining; Higdon, David M.
  作者单位：University of California System; University of California Santa Cruz; Yahoo! Inc; United States Department of Energy (DOE); Los Alamos National Laboratory
  摘要：Proton beams present difficulties in analysis because of the limited data that can be collected. The study of such beams must depend on complex computer simulators that incorporate detailed physical equations. The statistical problem of interest is to infer the initial state of the beam from the limited data collected as the beam passes through a series of focusing magnets. We are thus faced with a classic inverse problem where the computer simulator links the initial state to the observables....
• Using SIMEX for smoothing-parameter choice in errors-in-variables problems AI翻译
  作者：Delaigle, Aurore; Hall, Peter
  作者单位：University of Bristol; University of Melbourne
  摘要：SIMEX methods are attractive for solving curve estimation problems in errors-in-variables regression, using parametric or semiparametric techniques. However, nonparametric approaches are generally of quite a different type, being based on, for example, kernels, local-linear modeling, ridging, orthogonal series, or splines. All of these techniques involve the challenging (and not well studied) issue of empirical smoothing parameter choice. We show that SIMEX can be used effectively for selectin...
• Order Selection in Finite Mixture Models With a Nonsmooth Penalty AI翻译
  作者：Chen, Jiahua; Khalili, Abbas
  作者单位：University of British Columbia
  摘要：Order selection is a fundamental and challenging problem in the application of finite mixture models. We develop a new penalized likelihood approach that we call MSCAD. MSCAD deviates from information-based methods, such as Akaike information criterion and the Bayes information criterion, by introducing two penalty functions that depend on the mixing proportions and the component parameters. It is consistent in estimating both the order of the mixture model and the mixing distribution. Simulat...
• Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements AI翻译
  作者：Wang, Lifeng; Li, Hongzhe; Huang, Jianhua Z.
  作者单位：University of Pennsylvania; Texas A&M University System; Texas A&M University College Station
  摘要：Nonparametric varying-coefficient models are commonly used for analyzing data measured repeatedly over time, including longitudinal and functional response data. Although many procedures have been developed for estimating varying coefficients. the problem of variable selection for such models has rot been addressed to date. la this article we present a regularized estimation procedure for variable selection that combines basis function approximations and the smoothly clipped absolute deviation...