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作者：Casella, G.; Roberts, G.
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作者：Ambroise, Christophe; Matias, Catherine
作者单位：Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS)
摘要：Random-graph mixture models are very popular for modelling real data networks. Parameter estimation procedures usually rely on variational approximations, either combined with the expectation-maximization (EM) algorithm or with Bayesian approaches. Despite good results on synthetic data, the validity of the variational approximation is, however, not established. Moreover, these variational approaches aim at approximating the maximum likelihood or the maximum a posteriori estimators, whose beha...
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作者：Dellaportas, Petros; Kontoyiannis, Ioannis
作者单位：Athens University of Economics & Business; Athens University of Economics & Business
摘要：A general methodology is introduced for the construction and effective application of control variates to estimation problems involving data from reversible Markov chain Monte Carlo samplers. We propose the use of a specific class of functions as control variates, and we introduce a new consistent estimator for the values of the coefficients of the optimal linear combination of these functions. For a specific Markov chain Monte Carlo scenario, the form and proposed construction of the control ...
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作者：Casella, G.; Roberts, G.
摘要：Many methods for estimation or control of the false discovery rate (FDR) can be improved by incorporating information about pi(0), the proportion of all tested null hypotheses that are true. Estimates of pi(0) are often based on the number of p-values that exceed a threshold lambda. We first give a finite sample proof for conservative point estimation of the FDR when the lambda-parameter is fixed. Then we establish a condition under which a dynamic adaptive procedure, whose lambda-parameter is...
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作者：Chen, Kehui; Mueller, Hans-Georg
作者单位：University of California System; University of California Davis
摘要：Motivated by the conditional growth charts problem, we develop a method for conditional quantile analysis when predictors take values in a functional space. The method proposed aims at estimating conditional distribution functions under a generalized functional regression framework. This approach facilitates balancing of model flexibility and the curse of dimensionality for the infinite dimensional functional predictors. Its good performance in comparison with other methods, both for sparsely ...
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作者：Fan, Jianqing; Guo, Shaojun; Hao, Ning
作者单位：Princeton University; Chinese Academy of Sciences; University of Arizona
摘要：Variance estimation is a fundamental problem in statistical modelling. In ultrahigh dimensional linear regression where the dimensionality is much larger than the sample size, traditional variance estimation techniques are not applicable. Recent advances in variable selection in ultrahigh dimensional linear regression make this problem accessible. One of the major problems in ultrahigh dimensional regression is the high spurious correlation between the unobserved realized noise and some of the...
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作者：Lee, Stephen M. S.
作者单位：University of Hong Kong
摘要：We consider the general problem of constructing confidence regions for, possibly multi-dimensional, parameters when we have available more than one approach for the construction. These approaches may be motivated by different model assumptions, different levels of approximation, different settings of tuning parameters or different Monte Carlo algorithms. Their effectiveness is often governed by different sets of conditions which are difficult to vindicate in practice. We propose two procedures...
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作者：Casella, G.; Roberts, G. Z.
摘要：Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n(3)) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new appr...
