489期

最新文章

• Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models AI翻译
  作者：Mao, Meng; Wang, Jane-Ling
  作者单位：University of California System; University of California Davis
  摘要：We present a mixture cure model with the survival time of the uncured group coming from a class of linear transformation models, which is an extension of the proportional odds model This class of model. first proposed by Dabrowska and Doksum ( 988). which we term generalized proportional odds model, is well suited for the mixture cure model setting due to a clear separation between long-term and short-term effects A standard expectation-maximization algorithm can he employed to locate the nonp...
• Bayesian and Frequentist Methods for Provider Profiling Using Risk-Adjusted Assessments of Medical Outcomes AI翻译
  作者：Racz, Michael J.; Sedransk, J.
  作者单位：Albany College of Pharmacy & Health Sciences; State University of New York (SUNY) System; University at Albany, SUNY; University System of Ohio; Case Western Reserve University
  摘要：Provider profiling is the evaluation of the performance of hospitals, doctors, and other medical practitioners to enhance the quality of medical care We propose a new method and compare conventional and Bayesian methodologies that are used or proposed for use for such report cards Conventional statistical approaches to these provider assessments use likelihood-based frequentist methodologies and the new Bayesian method is patterned after these For each of three models, we compare the frequenti...
• Linear Mixed-Effects Modeling by Parameter Cascading AI翻译
  作者：Cao, J.; Ramsay, J. O.
  作者单位：Simon Fraser University; McGill University
  摘要：A linear mixed-effects model (LME) is a familial example of a multilevel parameter structure involving nuisance and structural parameters. as well as parameters that essentially control the model's complexity Marginalization Over nuisance parameters. such as the restricted maximization likelihood method, has been the usual estimation strategy, but it can Involve onerous and complex algorithms to achieve the integrations involved Parameter cascading Is described as a multicriterion optimization...
• Variational Inference for Large-Scale Models of Discrete Choice AI翻译
  作者：Braun, Michael; McAuliffe, Jon
  作者单位：Massachusetts Institute of Technology (MIT); University of California System; University of California Berkeley
  摘要：Discrete choice models are commonly used by applied statisticians in numerous fields. such as marketing. economics. finance. and operations research When agents in discrete choice models are assumed to have differing preferences. exact inference is often intractable Markov chain Monte Carlo techniques make approximate inference possible. but the computational cos is prohibitive on the large damsels now becoming untimely available Variational I methods provide a deterministic alternative for ap...
• Regularization Parameter Selections via Generalized Information Criterion AI翻译
  作者：Zhang, Yiyun; Li, Runze; Tsai, Chin-Ling
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Davis; Novartis; Novartis USA
  摘要：We apply the nonconcave penalized likelihood approach to obtain variable selections as well as shrinkage estimators This approach relies heavily on the choice of regularization parameter, which controls the model complexity In this paper, we propose employing the generalized in criterion. encompassing the commonly used Akaike in criterion (AIC) and Bayesian information criterion (BIC), for selecting the regularization parameter Our proposal makes a connection between the classical variable sel...
• Statistical Agent-Based Models for Discrete Spatio-Temporal Systems AI翻译
  作者：Hooten, Mevin B.; Wikle, Christopher K.
  作者单位：Utah System of Higher Education; Utah State University; University of Missouri System; University of Missouri Columbia
  摘要：Agent-based models have been used to mimic natural processes in a variety of fields. from biology to social science By specifying mechanistic models that describe how small-scale processes hi net and then scaling them up. agent-based approaches can result in very complicated large-scale behavior while often relying on only a small set of initial conditions and intuitive rules Although many agent-based models are used strictly la a Simulation context. statistical implementations are less common...
• How Many People Do You Know?: Efficiently Estimating Personal Network Size AI翻译
  作者：McCormick, Tyler H.; Salganik, Matthew J.; Zheng, Tian
  作者单位：Columbia University; Princeton University; Princeton University
  摘要：In this article we develop a method to estimate both individual social network size (ie, degree) and die distribution of network sizes in a population by asking respondents how many people they know in specific subpopulations (c g people named Michael) Building on the scale-up method of Killworth ei al (1998b) and other previous attempts to estimate individual network size we propose a latent non-random mixing model which resolves three known problems with previous approaches As a byproduct ou...
• Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random AI翻译
  作者：Chen, Baojiang; Yi, Grace Y.; Cook, Richard J.
  作者单位：University of Washington; University of Washington Seattle; University of Waterloo
  摘要：Longitudinal studies of ten feature incomplete response and covariate data It is well known that biases can arise from naive analyses of available data. but the precise impact of Incomplete data depends on the frequency of missing data and the strength of the association between the response variables and emanates and the missing-data indicators Various factors may influence the availability of response and covariate data at scheduled assessment times, and at any given assessment time the resp...
• A Moving Average Approach for Spatial Statistical Models of Stream Networks AI翻译
  作者：Ver Hoef, Jay M.; Peterson, Erin E.
  作者单位：National Oceanic Atmospheric Admin (NOAA) - USA; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
  摘要：In this article we use moving averages to develop new classes of models in a flexible modeling framework for stream networks Streams and rivers are among our most important resources, yet models with autocorrelated errors for spatially continuous stream networks have been described only recently We develop models based on stream distance rather than on Euclidean distance Spatial autocovariance models developed for Euclidean distance may not be valid when using stream distance We begin by descr...
• Approximate Methods for State-Space Models AI翻译
  作者：Koyama, Shinsuke; Perez-Bolde, Lucia Castellanos; Shalizi, Cosma Rohilla; Kass, Robert E.
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; The Santa Fe Institute
  摘要：State-space models provide an important body of techniques for analyzing time series. but their use requires estimating Unobserved states The optimal estimate of the state Is its conditional expectation given the observation histories. and computing this expectation is hard when there are nonlinearities Existing filtering methods, including sequential Monte Carlo. tend to be either inaccurate or slow In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models. whic...