2015

最新文章

• An Adaptive Resampling Test for Detecting the Presence of Significant Predictors Comment AI翻译
  作者：Barut, Emre; Wang, Huixia Judy
  作者单位：George Washington University
• A Cluster-Based Outlier Detection Scheme for Multivariate Data AI翻译
  作者：Jobe, J. Marcus; Pokojovy, Michael
  作者单位：University System of Ohio; Miami University; University of Konstanz
  摘要：Detection power of the squared Mahalanobis distance statistic is significantly reduced when several outliers exist within a multivariate dataset of interest. To overcome this masking effect, we propose a computer-intensive cluster-based approach that incorporates a reweighted version of Rousseeuw's minimum covariance determinant method with a multi-step cluster-based algorithm that initially filters out potential masking points. Compared to the most robust procedures, simulation studies show t...
• Marginal Inferential Models: Prior-Free Probabilistic Inference on Interest Parameters AI翻译
  作者：Martin, Ryan; Liu, Chuanhai
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Purdue University System; Purdue University
  摘要：The inferential models (IM) framework provides prior-free, frequency-calibrated, and posterior probabilistic inference. The key is the use of random sets to predict unobservable auxiliary variables connected to the observable data and unknown parameters. When nuisance parameters are present, a marginalization step can reduce the dimension of the auxiliary variable which, in turn, leads to more efficient inference. For regular problems, exact marginalization can be achieved, and we give conditi...
• S-Estimators for Functional Principal Component Analysis AI翻译
  作者：Boente, Graciela; Salibian-Barrera, Matias
  作者单位：University of Buenos Aires; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); University of British Columbia
  摘要：Principal component analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate or functional datasets. These approximations can be very useful in identifying potential outliers among high-dimensional or functional observations. In this article, we propose a new class of estimators for principal components based on robust scale estimators. For a fixed dimension q, we robustly estimate the q-dimensional linear space that provides the best predict...
• Simultaneous Edit-Imputation for Continuous Microdata AI翻译
  作者：Kim, Hang J.; Cox, Lawrence H.; Karr, Alan F.; Reiter, Jerome P.; Wang, Quanli
  作者单位：University System of Ohio; University of Cincinnati; Duke University; Research Triangle Institute; Duke University
  摘要：Many statistical organizations collect data that are expected to satisfy linear constraints; as examples, component variables should sum to total variables, and ratios of pairs of variables should be bounded by expert-specified constants. When reported data violate constraints, organizations identify and replace values potentially in error in a process known as edit-imputation. To date, most approaches separate the error localization and imputation steps, typically using optimization methods t...
• Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data AI翻译
  作者：Zubizarreta, Jose R.
  作者单位：Columbia University; Columbia University
  摘要：Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of such methods is that one set of weights can be used to estimate a range of treatment effects based on different outcomes, or a variety of population means for several variables. However, this appeal can be diminished in practice by the instability of the estimated weights and by the difficulty of ad...
• Smoothed Lexis Diagrams With Applications to Lung and Breast Cancer Trends in Taiwan AI翻译
  作者：Chien, Li-Chu; Wu, Yuh-Jenn; Hsiung, Chao A.; Wang, Lu-Hai; Chang, I-Shou
  作者单位：National Health Research Institutes - Taiwan; Chung Yuan Christian University; National Health Research Institutes - Taiwan; National Health Research Institutes - Taiwan; National Health Research Institutes - Taiwan
  摘要：Cancer surveillance research often begins with a rate matrix, also called a Lexis diagram, of cancer incidence derived from cancer registry and census data. Lexis diagrams with 3- or 5-year intervals for age group and for calendar year of diagnosis are often considered. This simple smoothing approach suffers from a significant limitation; important details useful in studying time trends may be lost in the averaging process involved in generating a summary rate. This article constructs a smooth...
• Large, Sparse Optimal Matching With Refined Covariate Balance in an Observational Study of the Health Outcomes Produced by New Surgeons AI翻译
  作者：Pimentel, Samuel D.; Kelz, Rachel R.; Silber, Jeffrey H.; Rosenbaum, Paul R.
  作者单位：University of Pennsylvania; University of Pennsylvania; University of Pennsylvania; Pennsylvania Medicine; Childrens Hospital of Philadelphia
  摘要：Every newly trained surgeon performs her first unsupervised operation. How do the health outcomes of her patients compare with the patients of experienced surgeons? Using data from 498 hospitals, we compare 1252 pairs comprised of a new surgeon and an experienced surgeon working at the same hospital. We introduce a new form of matching that matches patients of each new surgeon to patients of an otherwise similar experienced surgeon at the same hospital, perfectly balancing 176 surgical procedu...
• Bayesian Inference for Multivariate Meta-Regression With a Partially Observed Within-Study Sample Covariance Matrix AI翻译
  作者：Yao, Hui; Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin
  作者单位：University of Connecticut; National Institutes of Health (NIH) - USA; NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD); University of North Carolina; University of North Carolina Chapel Hill; Merck & Company
  摘要：Multivariate meta-regression models are commonly used in settings where the response variable is naturally multidimensional. Such settings are common in cardiovascular and diabetes studies where the goal is to study cholesterol levels once a certain medication is given. In this setting, the natural multivariate endpoint is low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). In this article, we examine study level...
• Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms AI翻译
  作者：Lee, Giwhyun; Ding, Yu; Genton, Marc G.; Xie, Le
  作者单位：Texas A&M University System; Texas A&M University College Station; King Abdullah University of Science & Technology; Texas A&M University System; Texas A&M University College Station
  摘要：In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine's energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as...