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作者：Sung, Chih-Li; Hung, Ying; Rittase, William; Zhu, Cheng; Wu, C. F. J.
作者单位：Michigan State University; Rutgers University System; Rutgers University New Brunswick; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
摘要：Calibration refers to the estimation of unknown parameters which are present in computer experiments but not available in physical experiments. An accurate estimation of these parameters is important because it provides a scientific understanding of the underlying system which is not available in physical experiments. Most of the work in the literature is limited to the analysis of continuous responses. Motivated by a study of cell adhesion experiments, we propose a new calibration framework f...
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作者：Chen, Yilin; Li, Pengfei; Wu, Changbao
作者单位：University of Waterloo
摘要：We establish a general framework for statistical inferences with nonprobability survey samples when relevant auxiliary information is available from a probability survey sample. We develop a rigorous procedure for estimating the propensity scores for units in the nonprobability sample, and construct doubly robust estimators for the finite population mean. Variance estimation is discussed under the proposed framework. Results from simulation studies show the robustness and the efficiency of our...
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作者：Zhang, Jingfei; Sun, Will Wei; Li, Lexin
作者单位：University of Miami; Purdue University System; Purdue University; University of California System; University of California Berkeley
摘要：Time-varying networks are fast emerging in a wide range of scientific and business applications. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect network model that characterizes the continuous time-varying behavior of the network at the population level, meanwhile taking into account both the individual subject variability as well as the prior module information. We develop a multistep optimization proce...
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作者：Hedayat, A. S.; Xu, Heng; Zheng, Wei
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Nektar Therapeutics; University of Tennessee System; University of Tennessee Knoxville
摘要：Recently, there have been some major advances in the theory of optimal designs for interference models when the block is arranged in one-dimensional layout. Relatively speaking, the study for two-dimensional interference model is quite limited partly due to technical difficulties. This article tries to fill this gap. Specifically, we set the tone by characterizing all possible universally optimal designs simultaneously through one linear equations system (LES) with respect to the proportions o...
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作者：Hu, Jianwei; Qin, Hong; Yan, Ting; Zhao, Yunpeng
作者单位：Central China Normal University; Zhongnan University of Economics & Law; Arizona State University; Arizona State University-Tempe
摘要：Estimating the number of communities is one of the fundamental problems in community detection. We re-examine the Bayesian paradigm for stochastic block models (SBMs) and propose a corrected Bayesian information criterion (CBIC), to determine the number of communities and show that the proposed criterion is consistent under mild conditions as the size of the network and the number of communities go to infinity. The CBIC outperforms those used in Wang and Bickel and Saldana, Yu, and Feng which ...
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作者：Franks, Jordan J.
作者单位：Newcastle University - UK
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作者：Benkeser, David; Petersen, Maya; van der Laan, Mark J.
作者单位：Emory University; University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：When predicting an outcome is the scientific goal, one must decide on a metric by which to evaluate the quality of predictions. We consider the problem of measuring the performance of a prediction algorithm with the same data that were used to train the algorithm. Typical approaches involve bootstrapping or cross-validation. However, we demonstrate that bootstrap-based approaches often fail and standard cross-validation estimators may perform poorly. We provide a general study of cross-validat...
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作者：Lu, Junwei; Kolar, Mladen; Liu, Han
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Chicago; Northwestern University
摘要：We develop a novel procedure for constructing confidence bands for components of a sparse additive model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popular nonparametric estimation methods in the literature, the kernel regression and the spline method, and is of interest in its own right. Existing methods for fitting sparse additive model are primarily based on sieve estimators, while the literature on confidence bands for nonparametric models are pri...
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作者：Loh, Po-Ling
作者单位：University of Wisconsin System; University of Wisconsin Madison
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作者：Ni, Yang; Mueller, Peter; Ji, Yuan
作者单位：Texas A&M University System; Texas A&M University College Station; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Chicago
摘要：Electronic health records (EHR) provide opportunities for deeper understanding of human phenotypes-in our case, latent disease-based on statistical modeling. We propose a categorical matrix factorization method to infer latent diseases from EHR data. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously allocates features to the ...
