497期

最新文章

• MRI Tissue Classification Using High-Resolution Bayesian Hidden Markov Normal Mixture Models AI翻译
  作者：Feng, Dai; Tierney, Luke; Magnotta, Vincent
  作者单位：Merck & Company; University of Iowa; University of Iowa
  摘要：Magnetic resonance imaging (MRI) is used to identify the major tissues within a subject's brain. Classification is usually based on a single image providing one measurement for each volume element, or voxel, in a discretization of the brain. A simple model views each voxel as homogeneous, belonging entirely to one of the three major tissue types: gray matter, white matter, or cerebrospinal fluid. The measurements are normally distributed, with means and variances depending on the tissue types ...
• Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach AI翻译
  作者：Bevilacqua, Moreno; Gaetan, Carlo; Mateu, Jorge; Porcu, Emilio
  作者单位：Universita Ca Foscari Venezia; Universitat Jaume I; University of Gottingen
  摘要：In this article, we propose two methods for estimating space and space-time covariance functions from a Gaussian random field, based on the composite likelihood idea. The first method relies on the maximization of a weighted version of the composite likelihood function, while the second one is based on the solution of a weighted composite score equation. This last scheme is quite general and could be applied to any kind of composite likelihood. An information criterion for model selection base...
• Estimation of Copula Models With Discrete Margins via Bayesian Data Augmentation AI翻译
  作者：Smith, Michael S.; Khaled, Mohamad A.
  作者单位：University of Melbourne; University of Technology Sydney
  摘要：Estimation of copula models with discrete margins can be difficult beyond the bivariate case. We show how this can be achieved by augmenting the likelihood with continuous latent variables, and computing inference using the resulting augmented posterior. To evaluate this, we propose two efficient Markov chain Monte Carlo sampling schemes. One generates the latent variables as a block using a Metropolis Hastings step with a proposal that is close to its target distribution, the other generates ...
• Nonparametric Covariate-Adjusted Association Tests Based on the Generalized Kendall's Tau AI翻译
  作者：Zhu, Wensheng; Jiang, Yuan; Zhang, Heping
  作者单位：Yale University; Yale University; Sun Yat Sen University
  摘要：Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in different scales, such as dichotomous, ordinal, and quantitative. In the absence of covariates, nonparametric association test...
• Information Ratio Test for Model Misspecification in Quasi-Likelihood Inference AI翻译
  作者：Zhou, Qian M.; Song, Peter X. -K.; Thompson, Mary E.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Michigan System; University of Michigan; University of Waterloo
  摘要：In this article, we focus on the circumstances in quasi-likelihood inference that the estimation accuracy of mean structure parameters is guaranteed by correct specification of the first moment, but the estimation efficiency could be diminished due to misspecification of the second moment. We propose an information ratio (IR) statistic to test for model misspecification of the variance/covariance structure through a comparison between two forms of information matrix: the negative sensitivity m...
• Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection AI翻译
  作者：Fan, Jianqing; Li, Yingying; Yu, Ke
  作者单位：Princeton University; Hong Kong University of Science & Technology
  摘要：Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of portfolios selection among a vast pool of assets, as demonstrated by Fan, Zhang, and Yu. The required high-dimensional volatility matrix can be estimated by using high-frequency financial data. This enables us to better adapt to the local volatilities and local correlations among a vast number of assets and to increase significantly the sample size for estimating the volatilit...
• Bayesian Inference for Dynamic Treatment Regimes: Mobility, Equity, and Efficiency in Student Tracking AI翻译
  作者：Zajonc, Tristan
  作者单位：Harvard University
  摘要：Policies in health, education, and economics often unfold sequentially and adapt to changing conditions. Such time-varying treatments pose problems for standard program evaluation methods because intermediate outcomes are simultaneously pretreatment confounders and posttreatment outcomes. This article extends the Bayesian perspective on causal inference and optimal treatment to these types of dynamic treatment regimes. A unifying idea remains ignorable treatment assignment, which now sequentia...
• Interim Design Modifications in Time-to-Event Studies AI翻译
  作者：Irle, Sebastian; Schaefer, Helmut
  作者单位：Philipps University Marburg
  摘要：We propose a flexible method for interim design modifications in time-to-event studies. With this method, it is possible to inspect the data at any time during the course of the study, without the need for prespecification of a learning phase, and to make certain types of design modifications depending on the interim data without compromising the Type I error risk. The method can be applied to studies designed with a conventional statistical test, fixed sample, or group sequential, even when n...
• Multiple Imputation for M-Regression With Censored Covariates AI翻译
  作者：Wang, Huixia Judy; Feng, Xingdong
  作者单位：North Carolina State University; Shanghai University of Finance & Economics
  摘要：We develop a new multiple imputation approach for M-regression models with censored covariates. Instead of specifying parametric likelihoods, our method imputes the censored covariates by their conditional quantiles given the observed data, where the conditional quantiles are estimated through fitting a censored quantile regression process. The resulting estimator is shown to be consistent and asymptotically normal, and it improves the estimation efficiency by using information from cases with...
• Bayesian Estimation and Prediction for Inhomogeneous Spatiotemporal Log-Gaussian Cox Processes Using Low-Rank Models, With Application to Criminal Surveillance AI翻译
  作者：Rodrigues, Alexandre; Diggle, Peter J.
  作者单位：Universidade Federal do Espirito Santo; Lancaster University; Lancaster University
  摘要：In this article, we propose a method for conducting likelihood-based inference for a class of nonstationary spatiotemporal log-Gaussian Cox processes. The method uses convolution-based models to capture spatiotemporal correlation structure, is computationally feasible even for large datasets, and does not require knowledge of the underlying spatial intensity of the process. We describe an application to a surveillance system for detecting emergent spatiotemporal clusters of homicides in Belo H...