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作者：Lindsay, Bruce G.; Markatou, Marianthi; Ray, Surajit
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; State University of New York (SUNY) System; University at Buffalo, SUNY; State University of New York (SUNY) System; University at Buffalo, SUNY; State University of New York (SUNY) System; University at Buffalo, SUNY; University of Glasgow
摘要：In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests. First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel. We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of a noncentrality index, an analogue of the traditional noncentrality parameter, on it. This leads to a method akin to the Neyman-Pearson lemma for c...
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作者：Zhu, Yunzhang; Shen, Xiaotong; Pan, Wei
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
摘要：Gaussian graphical models are useful to analyze and visualize conditional dependence relationships between interacting units. Motivated from network analysis under different experimental conditions, such as gene networks for disparate cancer subtypes, we model structural changes over multiple networks with possible heterogeneities. In particular, we estimate multiple precision matrices describing dependencies among interacting units through maximum penalized likelihood. Of particular interest ...
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作者：Aue, Alexander; Cheung, Rex C. Y.; Lee, Thomas C. M.; Zhong, Ming
作者单位：University of California System; University of California Davis
摘要：This article proposes new model-fitting techniques for quantiles of an observed data sequence, including methods for data segmentation and variable selection. The main contribution, however, is in providing a means to perform these two tasks simultaneously. This is achieved by matching the data with the best-fitting piecewise quantile regression model, where the fit is determined by a penalization derived from the minimum description length principle. The resulting optimization problem is solv...
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作者：Efron, Bradley
作者单位：Stanford University
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作者：Han, Fang; Liu, Han
作者单位：Johns Hopkins University; Princeton University
摘要：We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high-dimensional non-Gaussian data. Compared with sparse PCA, our method has a weaker modeling assumption and is more robust to possible data contamination. Theoretically, the proposed method achieves a parametric rate of convergence in estimating the parameter of interests under a flexible semiparametric distribution family; computationally, the proposed method exploits a rank-based ...
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作者：Zeng, Donglin; Lin, D. Y.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Under two-phase cohort designs, such as case-cohort and nested case-control sampling, information on observed event times, event indicators, and inexpensive covariates is collected in the first-phase, and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase; inexpensive covariates are also used in the data analysis to control for confounding and to evaluate interactions. This article provides efficient estimation of semiparametric ...
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作者：Gu, Kelvin; Pati, Debdeep; Dunson, David B.
作者单位：Stanford University; State University System of Florida; Florida State University; Duke University
摘要：Modeling object boundaries based on image or point cloud data is frequently necessary in medical and scientific applications ranging from detecting tumor contours for targeted radiation therapy, to the classification of organisms based on their structural information. In low-contrast images or sparse and noisy point clouds, there is often insufficient data to recover local segments of the boundary in isolation. Thus, it becomes critical to model the entire boundary in the form of a closed curv...
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作者：Politis, Dimitris N.
作者单位：University of California System; University of California San Diego
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作者：Di Marzio, Marco; Panzera, Agnese; Taylor, Charles C.
作者单位：G d'Annunzio University of Chieti-Pescara; University of Florence; University of Leeds
摘要：We develop nonparametric smoothing for regression when both the predictor and the response variables are defined on a sphere of whatever dimension. A local polynomial fitting approach is pursued, which retains all the advantages in terms of rate optimality, interpretability, and ease of implementation widely observed in the standard setting. Our estimates have a multi-output nature, meaning that each coordinate is separately estimated, within a scheme of a regression with a linear response. Th...
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作者：Zigler, Corwin Matthew; Dominici, Francesca
作者单位：Harvard University; Harvard T.H. Chan School of Public Health
摘要：Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of big data are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include in the PS model to satisfy the assumptions necessary for estimating average causal effects. Typically, ...
