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作者：Godolphin, J. D.
作者单位：University of Surrey
摘要：Two-level factorial designs are widely used in industry. For experiments involving n factors, the construction of designs comprising 2n and 2n-p factorials, arranged in blocks of size 2q is investigated. The aim is to estimate all main effects and a selected subset of two-factor interactions. Designs constructed according to minimum aberration criteria are shown to not necessarily be the most appropriate designs in this situation. A design construction approach is proposed which exploits known...
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作者：Qiu, Yumou; Tao, Jing; Zhou, Xiao-Hua
作者单位：Iowa State University; University of Washington; University of Washington Seattle; Peking University
摘要：This study proposes novel estimation and inference approaches for heterogeneous local treatment effects using high-dimensional covariates and observational data without a strong ignorability assumption. To achieve this, with a binary instrumental variable, the parameters of interest are identified on an unobservable subgroup of the population (compliers). Lasso estimation under a non-convex objective function is developed for a two-stage generalized linear model, and a debiased estimator is pr...
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作者：Reimherr, Matthew; Meng, Xiao-Li; Nicolae, Dan L.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Harvard University; University of Chicago
摘要：This paper outlines a framework for quantifying the prior's contribution to posterior inference in the presence of prior-likelihood discordance, a broader concept than the usual notion of prior-likelihood conflict. We achieve this dual purpose by extending the classic notion of prior sample size, M, in three directions: (I) estimating M beyond conjugate families; (II) formulating M as a relative notion that is as a function of the likelihood sample size k, M(k), which also leads naturally to a...
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作者：Tang, Wenpin; Zhang, Lu; Banerjee, Sudipto
作者单位：Columbia University; Columbia University; University of California System; University of California Los Angeles
摘要：Spatial process models popular in geostatistics often represent the observed data as the sum of a smooth underlying process and white noise. The variation in the white noise is attributed to measurement error, or microscale variability, and is called the 'nugget'. We formally establish results on the identifiability and consistency of the nugget in spatial models based upon the Gaussian process within the framework of in-fill asymptotics, that is the sample size increases within a sampling dom...
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作者：Su, Fangzhou; Ding, Peng
作者单位：University of California System; University of California Berkeley
摘要：Cluster-randomized experiments are widely used due to their logistical convenience and policy relevance. To analyse them properly, we must address the fact that the treatment is assigned at the cluster level instead of the individual level. Standard analytic strategies are regressions based on individual data, cluster averages and cluster totals, which differ when the cluster sizes vary. These methods are often motivated by models with strong and unverifiable assumptions, and the choice among ...
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作者：Xu, Sheng; Fan, Zhou
作者单位：Yale University
摘要：We consider estimating a piecewise-constant image, or a gradient-sparse signal on a general graph, from noisy linear measurements. We propose and study an iterative algorithm to minimize a penalized least-squares objective, with a penalty given by the l0-norm of the signal's discrete graph gradient. The method uses a non-convex variant of proximal gradient descent, applying the alpha-expansion procedure to approximate the proximal mapping in each iteration, and using a geometric decay of the p...
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作者：Toulis, Panos; Horel, Thibaut; Airoldi, Edoardo M.
作者单位：University of Chicago; Harvard University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：The need for statistical estimation with large data sets has reinvigorated interest in iterative procedures and stochastic optimization. Stochastic approximations are at the forefront of this recent development as they yield procedures that are simple, general and fast. However, standard stochastic approximations are often numerically unstable. Deterministic optimization, in contrast, increasingly uses proximal updates to achieve numerical stability in a principled manner. A theoretical gap ha...
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作者：Xia, Dong; Yuan, Ming
作者单位：Hong Kong University of Science & Technology; Columbia University
摘要：We introduce a flexible framework for making inferences about general linear forms of a large matrix based on noisy observations of a subset of its entries. In particular, under mild regularity conditions, we develop a universal procedure to construct asymptotically normal estimators of its linear forms through double-sample debiasing and low-rank projection whenever an entry-wise consistent estimator of the matrix is available. These estimators allow us to subsequently construct confidence in...
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作者：Stokell, Benjamin G.; Shah, Rajen D.; Tibshirani, Ryan J.
作者单位：University of Cambridge; Carnegie Mellon University
摘要：We propose a method for estimation in high-dimensional linear models with nominal categorical data. Our estimator, called SCOPE, fuses levels together by making their corresponding coefficients exactly equal. This is achieved using the minimax concave penalty on differences between the order statistics of the coefficients for a categorical variable, thereby clustering the coefficients. We provide an algorithm for exact and efficient computation of the global minimum of the resulting nonconvex ...
