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作者：Huang, Jing; Ning, Yang; Reid, Nancy; Chen, Yong
作者单位：University of Pennsylvania; Cornell University; University of Toronto
摘要：Composite likelihood functions are often used for inference in applications where the data have a complex structure. While inference based on the composite likelihood can be more robust than inference based on the full likelihood, the inference is not valid if the associated conditional or marginal models are misspecified. In this paper, we propose a general class of specification tests for composite likelihood inference. The test statistics are motivated by the fact that the second Bartlett i...
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作者：Meng, Cheng; Zhang, Xinlian; Zhang, Jingyi; Zhong, Wenxuan; Ma, Ping
作者单位：University System of Georgia; University of Georgia
摘要：We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size n, the smoothing spline estimator can be expressed as a linear combination of n basis functions, requiring O(n(3)) computational time when the number d of predictors is two or more. Such a sizeable computational cost hinders the broad applicability of smoothing splines. In practice, the full-sample smoothing spline estimator can be approximated by an estima...
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作者：Gao, Chao; Ma, Zongming
作者单位：University of Chicago; University of Pennsylvania
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作者：Vihola, Matti; Franks, Jordan
作者单位：University of Jyvaskyla
摘要：Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure sufficient mixing and post-pro...
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作者：Wang, Xuan; Parast, Layla; Tian, Lu; Cai, Tianxi
作者单位：Zhejiang University; RAND Corporation; Stanford University; Harvard University
摘要：In randomized clinical trials, the primary outcome, Y, often requires long-term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, S, to infer the treatment effect on Y, Delta. Identifying such an S and quantifying the proportion of treatment effect on Y explained by the effect on S are thus of great importance. Most existing methods for quantifying the proportion of treatment effect are model based and may yield biased estimates under model mi...
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作者：Mao, Lu
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：The infinite-dimensional information operator for the nuisance parameter plays a key role in semiparametric inference, as it is closely related to the regular estimability of the target parameter. Calculation of information operators has traditionally proceeded in a case-by-case manner and has often entailed lengthy derivations with complicated arguments. We develop a unified framework for this task by exploiting commonality in the form of semiparametric likelihoods. The general formula develo...
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作者：Li, Housen; Munk, Axel; Sieling, Hannes; Walther, Guenther
作者单位：University of Gottingen; Stanford University
摘要：The histogram is widely used as a simple, exploratory way of displaying data, but it is usually not clear how to choose the number and size of the bins. We construct a confidence set of distribution functions that optimally deal with the two main tasks of the histogram: estimating probabilities and detecting features such as increases and modes in the distribution. We define the essential histogram as the histogram in the confidence set with the fewest bins. Thus the essential histogram is the...
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作者：Lee, Jarod Y. L.; Green, Peter J.; Ryan, Louise M.
作者单位：University of Technology Sydney
摘要：This article concerns a class of generalized linear mixed models for two-level grouped data, where the random effects are uniquely indexed by groups and are independent. We derive necessary and sufficient conditions for the marginal likelihood to be expressed in explicit form. These models are unified under the conjugate generalized linear mixed models framework, where conjugate refers to the fact that the marginal likelihood can be expressed in closed form, rather than implying inference via ...
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作者：Sen, Deborshee; Sachs, Matthias; Lu, Jianfeng; Dunson, David B.
作者单位：Duke University; Duke University
摘要：Classification with high-dimensional data is of widespread interest and often involves dealing with imbalanced data. Bayesian classification approaches are hampered by the fact that current Markov chain Monte Carlo algorithms for posterior computation become inefficient as the number of predictors or the number of subjects to classify gets large, because of the increasing computational time per step and worsening mixing rates. One strategy is to employ a gradient-based sampler to improve mixin...
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作者：Dobriban, E.
作者单位：University of Pennsylvania
摘要：Multiple hypothesis testing problems arise naturally in science. This note introduces a new fast closed testing method for multiple testing which controls the familywise error rate. Controlling the familywise error rate is state-of-the-art in many important application areas and is preferred over false discovery rate control for many reasons, including that it leads to stronger reproducibility. The closure principle rejects an individual hypothesis if all global nulls of subsets containing it ...
