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作者：Lei, Lihua; Ramdas, Aaditya; Fithian, William
作者单位：Stanford University; Carnegie Mellon University; University of California System; University of California Berkeley
摘要：We propose a general framework based on selectively traversed accumulation rules for interactive multiple testing with generic structural constraints on the rejection set. It combines accumulation tests from ordered multiple testing with data-carving ideas from post-selection inference, allowing highly flexible adaptation to generic structural information. Our procedure defines an interactive protocol for gradually pruning a candidate rejection set, beginning with the set of all hypotheses and...
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作者：Griffin, J. E.; Latuszynski, K. G.; Steel, M. F. J.
作者单位：University of London; University College London; University of Warwick
摘要：The availability of datasets with large numbers of variables is rapidly increasing. The effective application of Bayesian variable selection methods for regression with these datasets has proved difficult since available Markov chain Monte Carlo methods do not perform well in typical problem sizes of interest. We propose new adaptive Markov chain Monte Carlo algorithms to address this shortcoming. The adaptive design of these algorithms exploits the observation that in large-p, small-n setting...
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作者：Sun, Ming; Zeng, Donglin; Wang, Yuanjia
作者单位：Columbia University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Dynamical systems based on differential equations are useful for modelling the temporal evolution of biomarkers. Such systems can characterize the temporal patterns of biomarkers and inform the detection of interactions between biomarkers. Existing statistical methods for dynamical systems deal mostly with single time-course data based on a linear model or generalized additive model. Hence, they cannot adequately capture the complex interactions between biomarkers; nor can they take into accou...
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作者：Kosmidis, Ioannis; Firth, David
作者单位：University of Warwick
摘要：Penalization of the likelihood by Jeffreys' invariant prior, or a positive power thereof, is shown to produce finite-valued maximum penalized likelihood estimates in a broad class of binomial generalized linear models. The class of models includes logistic regression, where the Jeffreys-prior penalty is known additionally to reduce the asymptotic bias of the maximum likelihood estimator, and models with other commonly used link functions, such as probit and log-log. Shrinkage towards equiproba...
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作者：Hiabu, M.; Nielsen, J. P.; Scheike, T. H.
作者单位：University of Sydney; City St Georges, University of London; University of Copenhagen
摘要：We consider an extension of Aalen's additive regression model that allows covariates to have effects that vary on two different time scales. The two time scales considered are equal up to a constant for each individual and vary across individuals, such as follow-up time and age in medical studies or calendar time and age in longitudinal studies. The model was introduced in Scheike (2001), where it was solved using smoothing techniques. We present a new backfitting algorithm for estimating the ...
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作者：Lin, Zhenhua; Wang, Jane-Ling; Zhong, Qixian
作者单位：National University of Singapore; University of California System; University of California Davis; Tsinghua University
摘要：Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. We investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly, and often much, shorter than the length of the whole inter...
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作者：Matsushita, Yukitoshi; Otsu, Taisuke
作者单位：Hitotsubashi University; University of London; London School Economics & Political Science
摘要：This article aims to shed light on inference problems for statistical models under alternative or nonstandard asymptotic frameworks from the perspective of the jackknife empirical likelihood. Examples include small-bandwidth asymptotics for semiparametric inference and goodness-of-fit testing, sparse-network asymptotics, many-covariates asymptotics for regression models, and many-weak-instruments asymptotics for instrumental variable regression. We first establish Wilks' theorem for the jackkn...
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作者：Dukes, Oliver; Vansteelandt, Stijn
作者单位：Ghent University
摘要：Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naive use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, co...
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作者：Hui, Francis K. C.
作者单位：Australian National University
摘要：Information criteria are commonly used for joint fixed and random effects selection in mixed models. While information criteria are straightforward to implement, a major difficulty in applying them is that they are typically based on maximum likelihood estimates, but calculating such estimates for one candidate mixed model, let alone multiple models, presents a major computational challenge. To overcome this hurdle, we study penalized quasilikelihood estimation and use it as the basis for perf...
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作者：McCullagh, P.; Tresoldi, M. F.
作者单位：University of Chicago
摘要：Quantile matching is a strictly monotone transformation that sends the observed response values to the quantiles of a given target distribution. A profile likelihood-based criterion is developed for comparing one target distribution with another in a linear-model setting.
