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作者：Kleiner, Ariel; Talwalkar, Ameet; Sarkar, Purnamrita; Jordan, Michael I.
作者单位：University of California System; University of California Berkeley
摘要：The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large data sets-which are increasingly prevalent-the calculation of bootstrap-based quantities can be prohibitively demanding computationally. Although variants such as subsampling and the m out of n bootstrap can be used in principle to reduce the cost of bootstrap computations, these methods are generally not robust to specification of tuning parameters (such as the numbe...
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作者：Doss, Hani; Tan, Aixin
作者单位：State University System of Florida; University of Florida; University of Iowa
摘要：In the classical biased sampling problem, we have k densities pi(1)(.), ... , pi(k)(.), each known up to a normalizing constant, i.e., for l = 1, ... , k, pi(l)(.) = v(l)(.)/m(l), where v(l)(.)is a known function and m(l) is an unknown constant. For each l, we have an independent and identically distributed sample from pi(l), and the problem is to estimate the ratios m(l)/m(s) for all l and all s. This problem arises frequently in several situations in both frequentist and Bayesian inference. ...
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作者：Rolling, Craig A.; Yang, Yuhong
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Researchers often believe that a treatment's effect on a response may be heterogeneous with respect to certain baseline covariates. This is an important premise of personalized medicine. Several methods for estimating heterogeneous treatment effects have been proposed. However, little attention has been given to the problem of choosing between estimators of treatment effects. Models that best estimate the regression function may not be best for estimating the effect of a treatment; therefore, ...
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作者：Krivitsky, Pavel N.; Handcock, Mark S.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Los Angeles
摘要：Models of dynamic networksnetworks that evolve over timehave manifold applications. We develop a discrete time generative model for social network evolution that inherits the richness and flexibility of the class of exponential family random-graph models. The modela separable temporal exponential family random-graph modelfacilitates separable modelling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model and provide...
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作者：Lei, Jing; Wasserman, Larry
作者单位：Carnegie Mellon University
摘要：We study distribution-free, non-parametric prediction bands with a focus on their finite sample behaviour. First we investigate and develop different notions of finite sample coverage guarantees. Then we give a new prediction band by combining the idea of conformal prediction' with non-parametric conditional density estimation. The proposed estimator, called COPS (conformal optimized prediction set), always has a finite sample guarantee. Under regularity conditions the estimator converges to a...
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作者：Danaher, Patrick; Wang, Pei; Witten, Daniela M.
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center
摘要：We consider the problem of estimating multiple related Gaussian graphical models from a high dimensional data set with observations belonging to distinct classes. We propose the joint graphical lasso, which borrows strength across the classes to estimate multiple graphical models that share certain characteristics, such as the locations or weights of non-zero edges. Our approach is based on maximizing a penalized log-likelihood. We employ generalized fused lasso or group lasso penalties and im...
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作者：Chambers, Ray; Chandra, Hukum; Salvati, Nicola; Tzavidis, Nikos
作者单位：University of Wollongong; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; University of Pisa; University of Southampton
摘要：Recently proposed outlier robust small area estimators can be substantially biased when outliers are drawn from a distribution that has a different mean from that of the rest of the survey data. This naturally leads one to consider an outlier robust bias correction for these estimators. We develop this idea, proposing two different analytical mean-squared error estimators for the ensuing bias-corrected outlier robust estimators. Simulations based on realistic outlier-contaminated data show tha...
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作者：Kasahara, Hiroyuki; Shimotsu, Katsumi
作者单位：University of British Columbia; University of Tokyo
摘要：We analyse the identifiability of the number of components in k-variate, M-component finite mixture models in which each component distribution has independent marginals, including models in latent class analysis. Without making parametric assumptions on the component distributions, we investigate how one can identify the number of components from the distribution function of the observed data. When k2, a lower bound on the number of components (M) is non-parametrically identifiable from the r...
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作者：Zhou, Hua; Li, Lexin
作者单位：North Carolina State University
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作者：Goga, Camelia; Ruiz-Gazen, Anne
作者单位：Universite Bourgogne Europe; Universite de Toulouse; Universite Toulouse 1 Capitole
摘要：Currently, high precision estimation of non-linear parameters such as Gini indices, low income proportions or other measures of inequality is particularly crucial. We propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a non-parametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any non-linear parameter that is asso...
