-
作者：Lin, Wei; Lv, Jinchi
作者单位：University of Pennsylvania; University of Southern California
摘要：High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by modern applications in high-throughput genomic data analysis and credit risk analysis. In this article, we propose a class of regularization methods for simultaneous variable selection and estimation in the additive hazards model, by combining the nonconcave penalized likelihood approach and the pseudoscore method. In a high-dimensional setting where the dimensionality can grow fast...
-
作者：Page, Garritt; Bhattacharya, Abhishek; Dunson, David
作者单位：Pontificia Universidad Catolica de Chile; Indian Statistical Institute; Indian Statistical Institute Kolkata; Duke University
摘要：It has become common for datasets to contain large numbers of variables in studies conducted in areas such as genetics, machine vision, image analysis, and many others. When analyzing such data, parametric models are often too inflexible while nonparametric procedures tend to be nonrobust because of insufficient data on these high-dimensional spaces. This is particularly true when interest lies in building efficient classifiers in the presence of many predictor variables. When dealing with the...
-
作者：Schennach, S. M.; Hu, Yingyao
作者单位：Brown University; Johns Hopkins University
摘要：Virtually all methods aimed at correcting for covariate measurement error in regressions rely on some form of additional information (e.g., validation data, known error distributions, repeated measurements, or instruments). In contrast, we establish that the fully nonparametric classical errors-in-variables model is identifiable from data on the regressor and the dependent variable alone, unless the model takes a very specific parametric form. This parametric family includes (but is not limite...
-
作者：Jiang, Qian; Wang, Hansheng; Xia, Yingcun; Jiang, Guohua
作者单位：National University of Singapore; Peking University; National University of Singapore
摘要：We propose a novel varying coefficient model (VCM), called principal varying coefficient model (PVCM), by characterizing the varying coefficients through linear combinations of a few principal functions. Compared with the conventional VCM, PVCM reduces the actual number of nonparametric functions and thus has better estimation efficiency. Compared with the semivarying coefficient model (SVCM), PVCM is more flexible but with the same estimation efficiency when the number of principal functions ...
-
作者：Cho, Haeran; Goude, Yannig; Brossat, Xavier; Yao, Qiwei
作者单位：University of London; London School Economics & Political Science; Electricite de France (EDF); Peking University
摘要：We propose a hybrid approach for the modeling and the short-term forecasting of electricity loads. Two building blocks of our approach are (1) modeling the overall trend and seasonality by fitting a generalized additive model to the weekly averages of the load and (2) modeling the dependence structure across consecutive daily loads via curve linear regression. For the latter, a new methodology is proposed for linear regression with both curve response and curve regressors. The key idea behind ...
