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作者：Jacob, Pierre E.; O'Leary, John; Atchade, Yves F.
作者单位：Harvard University; Boston University
摘要：Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to infinity. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretica...
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作者：Wang, Gao; Sarkar, Abhishek; Carbonetto, Peter; Stephens, Matthew
作者单位：University of Chicago
摘要：We introduce a simple new approach to variable selection in linear regression, with a particular focus onquantifying uncertainty in which variables should be selected. The approach is based on a new model-the 'sum of single effects' model, called 'SuSiE'-which comes from writing the sparse vector of regression coefficients as a sum of 'single-effect' vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure-iterative Bayesian stepwise selection (IBSS)-whi...
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作者：Liu, Bin; Zhou, Cheng; Zhang, Xinsheng; Liu, Yufeng
作者单位：Fudan University; Tencent; University of North Carolina; University of North Carolina Chapel Hill
摘要：In recent years, change point detection for a high dimensional data sequence has become increasingly important in many scientific fields such as biology and finance. The existing literature develops a variety of methods designed for either a specified parameter (e.g. the mean or covariance) or a particular alternative pattern (sparse or dense), but not for both scenarios simultaneously. To overcome this limitation, we provide a general framework for developing tests that are suitable for a lar...
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作者：Javanmard, Adel; Lee, Jason D.
作者单位：University of Southern California; Princeton University
摘要：Hypothesis testing in the linear regression model is a fundamental statistical problem. We consider linear regression in the high dimensional regime where the number of parameters exceeds the number of samples (p>n). To make informative inference, we assume that the model is approximately sparse, i.e. the effect of covariates on the response can be well approximated by conditioning on a relatively small number of covariates whose identities are unknown. We develop a framework for testing very ...
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作者：Grover, Lovleen Kumar; Kaur, Amanpreet
作者单位：Guru Nanak Dev University
摘要：We point out a minor mistake in published in 2006, 'A new randomized response model', which as been cited by various researchers, though no one has pointed out the mistake.
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作者：Engelke, Sebastian; Hitz, Adrien S.
作者单位：University of Geneva; University of Oxford
摘要：Conditional independence, graphical models and sparsity are key notions for parsimonious statistical models and for understanding the structural relationships in the data. The theory of multivariate and spatial extremes describes the risk of rare events through asymptotically justified limit models such as max-stable and multivariate Pareto distributions. Statistical modelling in this field has been limited to moderate dimensions so far, partly owing to complicated likelihoods and a lack of un...
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作者：Khismatullina, Marina; Vogt, Michael
作者单位：University of Bonn
摘要：We develop new multiscale methods to test qualitative hypotheses about the function m in the non-parametric regression model Y-t,Y-T=m(t/T)+e(t) with time series errors e(t). In time series applications, m represents a non-parametric time trend. Practitioners are often interested in whether the trend m has certain shape properties. For example, they would like to know whether m is constant or whether it is increasing or decreasing in certain time intervals. Our multiscale methods enable us to ...
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作者：Jiang, Zhenyu; Ling, Nengxiang; Lu, Zudi; Tjostheim, Dag; Zhang, Qiang
作者单位：University of Southampton; Hefei University of Technology; University of Bergen; Beijing University of Chemical Technology
摘要：Bandwidth choice is crucial in spatial kernel estimation in exploring non-Gaussian complex spatial data. The paper investigates the choice of adaptive and non-adaptive bandwidths for density estimation given data on a spatial lattice. An adaptive bandwidth depends on local data and hence adaptively conforms with local features of the spatial data. We propose a spatial cross-validation (SCV) choice of a global bandwidth. This is done first with a pilot density involved in the expression for the...
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作者：Zhao, Puying; Ghosh, Malay; Rao, J. N. K.; Wu, Changbao
作者单位：Yunnan University; State University System of Florida; University of Florida; Carleton University; University of Waterloo
摘要：We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non-differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically valid design-based frequentist...
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作者：Lane, Adam
作者单位：Cincinnati Children's Hospital Medical Center
摘要：Expected Fisher information can be founda prioriand as a result its inverse is the primary variance approximation used in the design of experiments. This is in contrast with the common claim that the inverse of the observed Fisher information is a better approximation of the variance of the maximum likelihood estimator. Observed Fisher information cannot be knowna priori; however, if an experiment is conducted sequentially, in a series of runs, the observed Fisher information from previous run...
