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作者：Schmon, S. M.; Deligiannidis, G.; Doucet, A.; Pitt, M. K.
作者单位：University of Oxford; University of London; King's College London
摘要：The pseudo-marginal algorithm is a variant of the Metropolis-Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate unbiasedly an unnormalized version of its density. Practically, one has to trade off the computational resources used to obtain this estimator against the asymptotic variances of the ergodic averages obtained by the pseudo-marginal algorithm. Recent works on optimizing this trade-off rely on some strong assumptions, wh...
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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：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.
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作者：Sit, T.; Ying, Z.; Yu, Y.
作者单位：Chinese University of Hong Kong; Columbia University; University of Warwick
摘要：Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key interests is to model the event history of time-stamped interactions among nodes. We model dynamic directed networks via multivariate counting processes. A pseudo partial likelihood approach is exploited to capture the network dependence structure. Asymptotic results are established. Numerical experiments are performed to demonstrate the effectiv...
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作者：Wang, Haiying; Ma, Yanyuan
作者单位：University of Connecticut; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsampling probabilities. One version minimizes the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator and the other minimizes that of the original parameter estimator. The former does not depend on the densities of the responses given covariates and is easy to implement. Algo...
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作者：Chen, Yining
作者单位：University of London; London School Economics & Political Science
摘要：We consider the problem of segmented linear regression with a single breakpoint, with the focus on estimating the location of the breakpoint. If n is the sample size, we show that the global minimax convergence rate for this problem in terms of the mean absolute error is O(n(-1/3)). On the other hand, we demonstrate the construction of a super-efficient estimator that achieves the pointwise convergence rate of either O(n(-1)) or O(n(-1/2)) for every fixed parameter value, depending on whether ...
