Journal of the American Statistical Association

最新文章

• Variable Selection With Prior Information for Generalized Linear Models via the Prior LASSO Method AI翻译
  作者：Jiang, Yuan; He, Yunxiao; Zhang, Heping
  作者单位：Oregon State University; Nielsen Holdings Inc.; Yale University; Yale University
  摘要：LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedical studies, the power of LASSO can be limited. Fortunately, so much biological and biomedical data have been collected and they may contain useful information about the importance of certain variables. This article proposes an extension of LASSO, namely, prio...
• Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings Comment AI翻译
  作者：Held, Leonhard; Muff, Stefanie
  作者单位：University of Zurich; Swiss School of Public Health (SSPH+)
• Exact Post-Selection Inference for Sequential Regression Procedures AI翻译
  作者：Tibshirani, Ryan J.; Taylor, Jonathan; Lockhart, Richard; Tibshirani, Robert
  作者单位：Carnegie Mellon University; Carnegie Mellon University
  摘要：We propose new inference tools for forward stepwise regression, least angle regression, and the lasso. Assuming a Gaussian model for the observation vector y, we first describe a general scheme to perform valid inference after any selection event that can be characterized as y falling into a polyhedral set. This framework allows us to derive conditional (post-selection) hypothesis tests at any step of forward stepwise or least angle regression, or any step along the lasso regularization path, ...
• Estimation and Testing of Varying Coefficients in Quantile Regression AI翻译
  作者：Feng, Xingdong; Zhu, Liping
  作者单位：Shanghai University of Finance & Economics; Renmin University of China
  摘要：In this article, we establish a novel connection between the null hypothesis H-0 on the coefficients and a rank-reducible form of the varying coefficient model in quantile regression. We use B-splines to approximate the varying coefficients in the rank-reducible model, and make use of the fact that the null hypothesis H-0 implies a unidimensional structure of a transformed coefficient matrix for the B-spline basis functions. By evaluating the unidimensional structure, we alleviate the difficul...
• Inference for Monotone Functions Under Short- and Long-Range Dependence: Confidence Intervals and New Universal Limits AI翻译
  作者：Bagchi, Pramita; Banerjee, Moulinath; Stoev, Stilian A.
  作者单位：Ruhr University Bochum; University of Michigan System; University of Michigan
  摘要：We introduce new point-wise confidence interval estimates for monotone functions observed with additive, dependent noise. Our methodology applies to both short- and long-range dependence regimes for the errors. The interval estimates are obtained via the method of inversion of certain discrepancy statistics. This approach avoids the estimation of nuisance parameters such as the derivative of the unknown function, which previous methods are forced to deal with. The resulting estimates are there...
• Modeling the Evolution of Dynamic Brain Processes During an Associative Learning Experiment AI翻译
  作者：Fiecas, Mark; Ombao, Hernando
  作者单位：University of Warwick; University of California System; University of California Irvine
  摘要：We develop a new time series model to investigate the dynamic interactions between the nucleus accumbens and the hippocampus during an associative learning experiment. Preliminary analyses indicated that the spectral properties of the local field potentials at these two regions changed over the trials of the experiment. While many models already take into account nonstationarity within a single trial, the evolution of the dynamics across trials is often ignored. Our proposed model, the slowly ...
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Qian, Min
  作者单位：Columbia University
  摘要：This comment deals with issues related to the article by Chen, Zeng, and Kosorok. We present several potential modifications of the outcome weighted learning approach.Those modifications are basecIon truncated l(2) loss. One advantage of l(2) loss is that it is differentiable everywhere, which makes it more stable and computationally more tractable.
• Graphical Procedures for Multiple Comparisons Under General Dependence AI翻译
  作者：Bennett, Christopher J.; Thompson, Brennan S.
  作者单位：Toronto Metropolitan University
  摘要：It has been more than half a century since Tukey first introduced graphical displays that relate nonoverlap of confidence intervals to statistically significant differences between parameter estimates. In this article, we show how Tukey's graphical overlap procedure can be modified to accommodate general forms of dependence within and across samples. We also develop a procedure that can be used to more effectively resolve rankings within the tails of the distributions of parameter values, ther...
• Spline-Lasso in High-Dimensional Linear Regression AI翻译
  作者：Guo, Jianhua; Hu, Jianchang; Jing, Bing-Yi; Zhang, Zhen
  作者单位：Hong Kong University of Science & Technology; National University of Singapore
  摘要：We consider a high-dimensional linear regression problem, where the covariates (features) are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. The fused lasso of Tibshirani et al. is designed especially to tackle this type of problems; it yields sparse coefficients and selects grouped variables, and encourages local constant coefficient profile within each group. However, in some applications, the effects of different features within a g...
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Ogburn, Elizabeth L.
  作者单位：Johns Hopkins University