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作者：Sugasawa, S.
作者单位：University of Tokyo
摘要：A two-stage normal hierarchical model called the Fay-Herriot model and the empirical Bayes estimator are widely used to obtain indirect and model-based estimates of means in small areas. However, the performance of the empirical Bayes estimator can be poor when the assumed normal distribution is misspecified. This article presents a simple modification that makes use of density power divergence and proposes a new robust empirical Bayes small area estimator. The mean squared error and estimated...
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作者：Fong, E.; Holmes, C. C.
作者单位：University of Oxford
摘要：In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using cross-validation on held-out data, either through k-fold partitioning or leave-p-out subsampling. We show that the marginal likelihood is formally equivalent to exhaustive leave-p-out cross-validation averaged over all values of p and all held-out test sets whe...
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作者：Nishimura, Akihiko; Dunson, David B.; Lu, Jianfeng
作者单位：University of California System; University of California Los Angeles; Duke University; Duke University
摘要：Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article we present an extension that can efficiently explore target distributions with discontinuous densities. Our extension in particular enables efficient sampling from ordinal parameters through the embedding of probability mass functions into continuous spaces. We motivate our approach through a theory of discontinuous Hamiltonian dynamics and develop a corresponding numerical solver. The proposed so...
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作者：Liu, X.; Zheng, S.; Feng, X.
作者单位：Shanghai University of Finance & Economics; Northeast Normal University - China
摘要：We propose a novel estimator of error variance and establish its asymptotic properties based on ridge regression and random matrix theory. The proposed estimator is valid under both low- and high-dimensional models, and performs well not only in nonsparse cases, but also in sparse ones. The finite-sample performance of the proposed method is assessed through an intensive numerical study, which indicates that the method is promising compared with its competitors in many interesting scenarios.
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作者：Chang, Jinyuan; Kolaczyk, Eric D.; Yao, Qiwei
作者单位：Southwestern University of Finance & Economics - China; Boston University; University of London; London School Economics & Political Science
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作者：Green, A. K. B.; McCormick, T. H.; Raftery, A. E.
作者单位：University of Washington; University of Washington Seattle
摘要：Respondent-driven sampling is an approach for estimating features of populations that are difficult to access using standard survey tools, e.g., the fraction of injection drug users who are HIV positive. Baraff et al. (2016) introduced an approach to estimating uncertainty in population proportion estimates from respondent-driven sampling using the tree bootstrap method. In this paper we establish the consistency of this tree bootstrap approach in the case of m-trees.
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作者：Li, Tianxi; Levina, Elizaveta; Zhu, Ji
作者单位：University of Virginia; University of Michigan System; University of Michigan
摘要：While many statistical models and methods are now available for network analysis, resampling of network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but it is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. In this paper we propose a new network resampling strategy, based on splitting node pairs rather than nodes, that is a...
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作者：Shin, Sunyoung; Liu, Yufeng; Cole, Stephen R.; Fine, Jason P.
作者单位：University of Texas System; University of Texas Dallas; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：We consider scenarios in which the likelihood function for a semiparametric regression model factors into separate components, with an efficient estimator of the regression parameter available for each component. An optimal weighted combination of the component estimators, named an ensemble estimator, may be employed as an overall estimate of the regression parameter, and may be fully efficient under uncorrelatedness conditions. This approach is useful when the full likelihood function may be ...
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作者：Padilla, Oscar Hernan Madrid; Sharpnack, James; Chen, Yanzhen; Witten, Daniela M.
作者单位：University of California System; University of California Los Angeles; University of California System; University of California Davis; Hong Kong University of Science & Technology; University of Washington; University of Washington Seattle
摘要：The fused lasso, also known as total-variation denoising, is a locally adaptive function estimator over a regular grid of design points. In this article, we extend the fused lasso to settings in which the points do not occur on a regular grid, leading to a method for nonparametric regression. This approach, which we call the K-nearest-neighbours fused lasso, involves computing the K-nearest-neighbours graph of the design points and then performing the fused lasso over this graph. We show that ...
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作者：Heuchenne, C.; De Una-Alvarez, J.; Laurent, G.
作者单位：University of Liege; Universidade de Vigo
摘要：Cross-sectional sampling is often used when investigating inter-event times, resulting in left-truncated and right-censored data. In this paper, we consider a semiparametric truncation model in which the truncating variable is assumed to belong to a certain parametric family. We examine two methods of estimating both the truncation and the lifetime distributions. We obtain asymptotic representations of the estimators for the lifetime distribution and establish their weak convergence. Both of t...
