2015

最新文章

• Design and Analysis of the Randomized Response Technique AI翻译
  作者：Blair, Graeme; Imai, Kosuke; Zhou, Yang-Yang
  作者单位：Columbia University; Princeton University
  摘要：About a half century ago, in 1965, Warner proposed the randomized response method as a survey technique to reduce potential bias due to nonresponse and social desirability when asking questions about sensitive behaviors and beliefs. This method asks respondents to use a randomization device, such as a coin flip, whose outcome is unobserved by the interviewer. By introducing random noise, the method conceals individual responses and protects respondent privacy. While numerous methodological adv...
• Clustering High-Dimensional Landmark-Based Two-Dimensional Shape Data AI翻译
  作者：Huang, Chao; Styner, Martin; Zhu, Hongtu
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：An important goal in image analysis is to cluster and recognize objects of interest according to the shapes of their boundaries. Clustering such objects faces at least four major challenges including a curved shape space, a high-dimensional feature space, a complex spatial correlation structure, and shape variation associated with some covariates (e.g., age or gender). The aim of this article is to develop a penalized model-based clustering framework to cluster landmark-based planar shape data...
• The E-MS Algorithm: Model Selection With Incomplete Data AI翻译
  作者：Jiang, Jiming; Thuan Nguyen; Rao, J. Sunil
  作者单位：University of California System; University of California Davis; Oregon Health & Science University; University of Miami
  摘要：We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized i...
• Testing for Nodal Dependence in Relational Data Matrices AI翻译
  作者：Volfovsky, Alexander; Hoff, Peter D.
  作者单位：University of Washington; University of Washington Seattle
  摘要：Relational data are often represented as a square matrix, the entries of which record the relationships between pairs of objects. Many statistical methods for the analysis of such data assume some degree of similarity or dependence between objects in terms of the way they relate to each other. However, formal tests for such dependence have not been developed. We provide a test for such dependence using the framework of the matrix normal model, a type of multivariate normal distribution paramet...
• New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes AI翻译
  作者：Zhao, Ying-Qi; Zeng, Donglin; Laber, Eric B.; Kosorok, Michael R.
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long-term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized trea...
• Semiparametric Relative-Risk Regression for Infectious Disease Transmission Data AI翻译
  作者：Kenah, Eben
  作者单位：State University System of Florida; University of Florida
  摘要：This article introduces semiparametric relative-risk regression models for infectious disease data. The units of analysis in these models are pairs of individuals at risk of transmission. The hazard of infectious contact from i to j consists of a baseline hazard multiplied by a relative risk function that can be a function of infectiousness covariates for i, susceptibliity covariates for j, and pairwise covariates. When who-infects-whom is observed, we derive a profile likelihood maximized ove...
• Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA AI翻译
  作者：Storlie, Curtis B.; Lane, William A.; Ryan, Emily M.; Gattiker, James R.; Higdon, David M.
  作者单位：United States Department of Energy (DOE); Los Alamos National Laboratory; Boston University
  摘要：It has become commonplace to use complex computer models to predict outcomes in regions where data do not exist. Typically these models need to be calibrated and validated using some experimental data, which often consists of multiple correlated outcomes. In addition, some of the model parameters may be categorical in nature, such as a pointer variable to alternate models (or submodels) for some of the physics of the system. Here, we present a general approach for calibration in such situation...
• High-Dimensional Variable Selection With Reciprocal L1-Regularization AI翻译
  作者：Song, Qifan; Liang, Faming
  作者单位：Purdue University System; Purdue University; State University System of Florida; University of Florida
  摘要：During the past decade, penalized likelihood methods have been widely used in variable selection problems, where the penalty functions are typically symmetric about 0, continuous and nondecreasing in (0, infinity). We propose a new penalized likelihood method, reciprocal Lasso (or in short, rLasso), based on a new class of penalty functions that are decreasing in (0,infinity), discontinuous at 0, and converge to infinity when the coefficients approach zero. The new penalty functions give nearl...
• Analysis of the Proportional Hazards Model With Sparse Longitudinal Covariates AI翻译
  作者：Cao, Hongyuan; Churpek, Mathew M.; Zeng, Donglin; Fine, Jason P.
  作者单位：University of Missouri System; University of Missouri Columbia; University of Chicago; University of Chicago; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Regression analysis of censored failure observations via the proportional hazards model permits time-varying covariates that are observed at death times. In practice, such longitudinal covariates are typically sparse and only measured at infrequent and irregularly spaced follow-up times. Full likelihood analyses of joint models for longitudinal and survival data impose stringent modeling assumptions that are difficult to verify in practice and that are complicated both inferentially and comput...
• Multi-Agent Inference in Social Networks: A Finite Population Learning Approach AI翻译
  作者：Fan, Jianqing; Tong, Xin; Zeng, Yao
  作者单位：Princeton University; Capital University of Economics & Business; University of Southern California; Harvard University
  摘要：When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to weigh the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data-collection process. To ...