504期

最新文章

• Generalized Jackknife Estimators of Weighted Average Derivatives Comment AI翻译
  作者：Zeng, Donglin
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
• Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes AI翻译
  作者：Zhu, Bin; Dunson, David B.
  作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; Duke University
  摘要：We propose a nested Gaussian process (nGP) as a locally adaptive prior for Bayesian nonparametric regression. Specified through a set of stochastic differential equations (SDEs), the nGP imposes a Gaussian process prior for the function's mth-order derivative. The nesting comes in through including a local instantaneous mean function, which is drawn from another Gaussian process inducing adaptivity to locally varying smoothness. We discuss the support of the nGP prior in terms of the closure o...
• Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference AI翻译
  作者：Hoshino, Takahiro
  作者单位：Nagoya University
  摘要：We propose a new semiparametric Bayesian model for causal inference in which assignment to treatment depends on potential outcomes. The model uses the probit stick-breaking process mixture proposed by Chung and Dunson (2009), a variant of the Dirichlet process mixture modeling. In contrast to previous Bayesian models, the proposed model directly estimates the parameters of the marginal parametric model of potential outcomes, while it relaxes the strong ignorability assumption, and requires no ...
• Selecting the Number of Principal Components in Functional Data AI翻译
  作者：Li, Yehua; Wang, Naisyin; Carroll, Raymond J.
  作者单位：Iowa State University; University of Michigan System; University of Michigan; Texas A&M University System; Texas A&M University College Station
  摘要：Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data. We propose a Bayesian information criterion based on marginal modeling that can consistently select ...
• Statistical Learning With Time Series Dependence: An Application to Scoring Sleep in Mice Comment AI翻译
  作者：Zeng, Donglin; Wang, Yuanjia
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Columbia University
• Comment: Dimension Asymptotics and Semiparametrics AI翻译
  作者：Mammen, Enno
  作者单位：University of Mannheim
• Bayesian Inference for Logistic Models Using Polya-Gamma Latent Variables AI翻译
  作者：Polson, Nicholas G.; Scott, James G.; Windle, Jesse
  作者单位：University of Chicago; University of Texas System; University of Texas Austin; Duke University
  摘要：We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Polya-Gamma distributions, which are constructed in detail. A variety of examples are presented to show the versatility of the method, including logistic regression, negative binomial regression, nonlinear mixed-effect models, and spatial models for count data. In each case, our data-augmentation strategy leads to simple, effective methods for pos...
• Local Linear Regression on Manifolds and Its Geometric Interpretation AI翻译
  作者：Cheng, Ming-Yen; Wu, Hau-Tieng
  作者单位：National Taiwan University; University of California System; University of California Berkeley
  摘要：High-dimensional data analysis has been an active area, and the main focus areas have been variable selection and dimension reduction. In practice, it occurs often that the variables are located on an unknown, lower-dimensional nonlinear manifold. Under this manifold assumption, one purpose of this article is regression and gradient estimation on the manifold, and another is developing a new tool for manifold learning. As regards the first aim, we suggest directly reducing the dimensionality t...
• Adaptive Testing of Conditional Association Through Recursive Mixture Modeling AI翻译
  作者：Ma, Li
  作者单位：Duke University
  摘要：In many case-control studies, a central goal is to test for association or dependence between the predictors and the response. Relevant covariates must be conditioned on to avoid false positives and loss in power. Conditioning on covariates is easy in parametric frameworks such as the logistic regression-by incorporating the covariates into the model as additional variables. In contrast, nonparametric methods such as the Cochran-Mantel-Haenszel test accomplish conditioning by dividing the data...
• The International Year of Statistics: A Celebration and A Call to Action AI翻译
  作者：Davidian, Marie
  作者单位：North Carolina State University