-
作者：Anceschi, Niccolo; Fasano, Augusto; Durante, Daniele; Zanella, Giacomo
作者单位：Bocconi University; Bocconi University
摘要：A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including classical Gaussian response settings, this class also encompasses probit, multinomial probit and tobit regression, among others, thereby yielding one of the most widely-implemented families of models in routine applications. The relevance of such representations has stimulated decades of research in the Bayesian field, mostly motivate...
-
作者：Mohammadi, Reza; Massam, Helene; Letac, Gerard
作者单位：University of Amsterdam; York University - Canada; Universite de Toulouse; Universite Toulouse III - Paul Sabatier
摘要：Bayesian structure learning in Gaussian graphical models is often done by search algorithms over the graph space.The conjugate prior for the precision matrix satisfying graphical constraints is the well-known G-Wishart.With this prior, the transition probabilities in the search algorithms necessitate evaluating the ratios of the prior normalizing constants of G-Wishart.In moderate to high-dimensions, this ratio is often approximated by using sampling-based methods as computationally expensive ...
-
作者：Tuo, Rui; He, Shiyuan; Pourhabib, Arash; Ding, Yu; Huang, Jianhua Z.
作者单位：Texas A&M University System; Texas A&M University College Station; Renmin University of China; Oklahoma State University System; Oklahoma State University - Stillwater; Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station
摘要：This article develops a frequentist solution to the functional calibration problem, where the value of a calibration parameter in a computer model is allowed to vary with the value of control variables in the physical system. The need of functional calibration is motivated by engineering applications where using a constant calibration parameter results in a significant mismatch between outputs from the computer model and the physical experiment. Reproducing kernel Hilbert spaces (RKHS) are use...
-
作者：Chen, Yu-Ting; Chiou, Jeng-Min; Huang, Tzee-Ming
作者单位：National Chengchi University; Academia Sinica - Taiwan
摘要：We present a new approach known as greedy segmentation (GS) to identify multiple changepoints for a functional data sequence. The proposed multiple changepoint detection criterion links detectability with the projection onto a suitably chosen subspace and the changepoint locations. The changepoint estimator identifies the true changepoints for any predetermined number of changepoint candidates, either over-reporting or under-reporting. This theoretical finding supports the proposed GS estimato...
-
作者：Jiang, Roulan; Zhan, Xiang; Wang, Tianying
作者单位：Tsinghua University; Tsinghua University; Peking University; Peking University
摘要：In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reflect true taxa abundance patterns in the ecosystem. Repeated measures, including longitudinal study designs, may be potential solutions to mitigate the discrepancy between observed abundances and true under...
-
作者：Blackwell, Matthew; Pashley, Nicole E.
作者单位：Harvard University; Harvard University; Rutgers University System; Rutgers University New Brunswick
摘要：Factorial experiments are widely used to assess the marginal, joint, and interactive effects of multiple concurrent factors. While a robust literature covers the design and analysis of these experiments, there is less work on how to handle treatment noncompliance in this setting. To fill this gap, we introduce a new methodology that uses the potential outcomes framework for analyzing 2(K) factorial experiments with noncompliance on any number of factors. This framework builds on and extends th...
-
作者：Ren, Zhimei; Wei, Yuting; Candes, Emmanuel
作者单位：University of Chicago; University of Pennsylvania; Stanford University
摘要：Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously controlling the number or fraction of false positives. Model-X knockoffs is a randomized procedure which relies on the one-time construction of synthetic (random) variables. This article introduces a derandomization method by aggregating the selection results across multiple runs of the knockoffs algorithm. The derand...
-
作者：Chen, Hui; Ren, Haojie; Yao, Fang; Zou, Changliang
作者单位：Nankai University; Nankai University; Shanghai Jiao Tong University; Peking University
摘要：In multiple change-point analysis, one of the main difficulties is to determine the number of change-points. Various consistent selection methods, including the use of Schwarz information criterion and cross-validation, have been proposed to balance the model fitting and complexity. However, there is lack of systematic approaches to provide theoretical guarantee of significance in determining the number of changes. In this paper, we introduce a data-adaptive selection procedure via error rate ...
-
作者：Chen, Yuxin; Fan, Jianqing; Wang, Bingyan; Yan, Yuling
作者单位：Princeton University; Princeton University
摘要：We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i.e., a sort of random Fourier design andGaussian design). Despite the wide applicability, the theoretical understanding about these two paradigms remains largely inadequate in the presence of random noise. The current article makes two contributions by demonstrating that (i) a two-stage nonconvex algorithm attains minimax-optimal accuracy withi...
-
作者：Fan, Jianqing; Guo, Yongyi; Wang, Kaizheng
作者单位：Princeton University; Columbia University
摘要：When the data are stored in a distributed manner, direct applications of traditional statistical inference procedures are often prohibitive due to communication costs and privacy concerns. This article develops and investigates two communication-efficient accurate statistical estimators (CEASE), implemented through iterative algorithms for distributed optimization. In each iteration, node machines carry out computation in parallel and communicate with the central processor, which then broadcas...
