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作者：Kaufman, C. G.; Shaby, B. A.
作者单位：University of California System; University of California Berkeley
摘要：Two canonical problems in geostatistics are estimating the parameters in a specified family of stochastic process models and predicting the process at new locations. We show that asymptotic results for a Gaussian process over a fixed domain with Matern covariance function, previously proven only in the case of a fixed range parameter, can be extended to the case of jointly estimating the range and the variance of the process. Moreover, we show that intuition and approximations derived from asy...
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作者：Luo, Xiaolong; Chen, Guang; Ouyang, S. Peter; Turnbull, Bruce W.
作者单位：Bristol-Myers Squibb; Celgene Corporation; Cornell University
摘要：We develop gatekeeping procedures that focus on comparing multiple treatments with a control when there are multiple endpoints. Our procedures utilize estimated correlations among individual test statistics without parametric assumptions. We make comparisons with other gatekeeping procedures with respect to properties of the trade-off in statistical power between families of hypotheses. We introduce a reward function to facilitate these comparisons. We illustrate our methods by simulation and ...
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作者：Lin, Yuanyuan; Chen, Kani
作者单位：Hong Kong Polytechnic University; Hong Kong University of Science & Technology
摘要：In linear regression or accelerated failure time models, complications in efficient estimation arise from the multiple roots of the efficient score and density estimation. This paper proposes a one-step efficient estimation method based on a counting process martingale, which has several advantages: it avoids the multiple-root problem, the initial estimator is easily available and the variance estimator can be obtained by employing plug-in rules. A simple and effective data-driven bandwidth se...
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作者：Yan, Ting; Xu, Jinfeng
作者单位：Central China Normal University; New York University
摘要：Chatterjee et al. (2011) established the consistency of the maximum likelihood estimator in the beta-model for undirected random graphs when the number of vertices goes to infinity. By approximating the inverse of the Fisher information matrix, we prove asymptotic normality of the maximum likelihood estimator under mild conditions. Simulation studies and a data example illustrate the theoretical results.
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作者：Krafty, Robert T.; Collinge, William O.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：Nonparametric estimation procedures that can flexibly account for varying levels of smoothness among different functional parameters, such as penalized likelihoods, have been developed in a variety of settings. However, geometric constraints on power spectra have limited the development of such methods when estimating the power spectrum of a vector-valued time series. This article introduces a penalized likelihood approach to nonparametric multivariate spectral analysis through the minimizatio...
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作者：Benjamini, Yoav; Madar, Vered; Stark, Philip B.
作者单位：Tel Aviv University; University of North Carolina; University of North Carolina Chapel Hill; University of California System; University of California Berkeley
摘要：Many studies draw inferences about multiple endpoints but ignore the statistical implications of multiplicity. Effects inferred to be positive when there is no adjustment for multiplicity can lose their statistical significance when multiplicity is taken into account, perhaps explaining why such adjustments are so often omitted. We develop new simultaneous confidence intervals that mitigate this problem; these are uniformly more likely to determine signs than are standard simultaneous confiden...
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作者：Han, Peisong; Wang, Lu
作者单位：University of Michigan System; University of Michigan
摘要：We propose an estimator that is more robust than doubly robust estimators, based on weighting complete cases using weights other than inverse probability when estimating the population mean of a response variable subject to ignorable missingness. We allow multiple models for both the propensity score and the outcome regression. Our estimator is consistent if any of the multiple models is correctly specified. Such multiple robustness against model misspecification is a significant improvement o...
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作者：Polson, N. G.; Scott, J. G.
作者单位：University of Chicago; University of Texas System; University of Texas Austin
摘要：We use the theory of normal variance-mean mixtures to derive a data-augmentation scheme for a class of common regularization problems. This generalizes existing theory on normal variance mixtures for priors in regression and classification. It also allows variants of the expectation-maximization algorithm to be brought to bear on a wider range of models than previously appreciated. We demonstrate the method on several examples, focusing on the case of binary logistic regression. We also show t...
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作者：Roverato, A.; Lupparelli, M.; La Rocca, L.
作者单位：University of Bologna; Universita di Modena e Reggio Emilia
摘要：This paper introduces a novel class of models for binary data, which we call log-mean linear models. They are specified by linear constraints on the log-mean linear parameter, defined as a log-linear expansion of the mean parameter of the multivariate Bernoulli distribution. We show that marginal independence relationships between variables can be specified by setting certain log-mean linear interactions to zero and, more specifically, that graphical models of marginal independence are log-mea...
