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作者：Rothenhausler, D.; Meinshausen, N.; Buhlmann, P.; Peters, J.
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作者：Chen, Mingli; Kato, Kengo; Leng, Chenlei
作者单位：University of Warwick; Cornell University; University of Warwick
摘要：Data in the form of networks are increasingly available in a variety of areas, yet statistical models allowing for parameter estimates with desirable statistical properties for sparse networks remain scarce. To address this, we propose the Sparse beta-Model (S beta M), a new network model that interpolates the celebrated Erdos-Renyi model and the beta-model that assigns one different parameter to each node. By a novel reparameterization of the beta-model to distinguish global and local paramet...
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作者：Henzi, Alexander; Ziegel, Johanna F.; Gneiting, Tilmann
作者单位：University of Bern; Heidelberg Institute for Theoretical Studies; Helmholtz Association; Karlsruhe Institute of Technology
摘要：Isotonic distributional regression (IDR) is a powerful non-parametric technique for the estimation of conditional distributions under order restrictions. In a nutshell, IDR learns conditional distributions that are calibrated, and simultaneously optimal relative to comprehensive classes of relevant loss functions, subject to isotonicity constraints in terms of a partial order on the covariate space. Non-parametric isotonic quantile regression and non-parametric isotonic binary regression emerg...
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作者：Lei, Lihua; Candes, Emmanuel J.
作者单位：Stanford University; Stanford University
摘要：Evaluating treatment effect heterogeneity widely informs treatment decision making. At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms. While these methods enjoy some theoretical appeal in terms of consistency and convergence rates, they generally perform poorly in terms of uncertainty quantification. This is troubling since assessing risk is crucial for reliable decision-making in sensitive and uncertai...
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作者：Kim, Byol; Liu, Song; Kolar, Mladen
作者单位：University of Chicago; University of Bristol; Alan Turing Institute; University of Chicago
摘要：Markov networks are frequently used in sciences to represent conditional independence relationships underlying observed variables arising from a complex system. It is often of interest to understand how an underlying network differs between two conditions. In this paper, we develop methods for comparing a pair of high-dimensional Markov networks where we allow the number of observed variables to increase with the sample sizes. By taking the density ratio approach, we are able to learn the netw...
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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：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 ...
