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作者：Wood, Simon N.
作者单位：University of Bath
摘要：Testing that random effects are zero is difficult, because the null hypothesis restricts the corresponding variance parameter to the edge of the feasible parameter space. In the context of generalized linear mixed models, this paper exploits the link between random effects and penalized regression to develop a simple test for a zero effect. The idea is to treat the variance components not being tested as fixed at their estimates and then to express the likelihood ratio as a readily computed qu...
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作者：Kleiber, William; Genton, Marc G.
作者单位：University of Colorado System; University of Colorado Boulder; King Abdullah University of Science & Technology
摘要：We derive sufficient conditions for the cross-correlation coefficient of a multivariate spatial process to vary with location when the spatial model is augmented with nugget effects. The derived class is valid for any choice of covariance functions, and yields substantial flexibility between multiple processes. The key is to identify the cross-correlation coefficient matrix with a contraction matrix, which can be either diagonal, implying a parsimonious formulation, or a fully general contract...
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作者：Shen, Yu; Ning, Jing; Qin, Jing
作者单位：University of Texas System; UTMD Anderson Cancer Center; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
摘要：The full likelihood approach in statistical analysis is regarded as the most efficient means for estimation and inference. For complex length-biased failure time data, computational algorithms and theoretical properties are not readily available, especially when a likelihood function involves infinite-dimensional parameters. Relying on the invariance property of length-biased failure time data under the semiparametric density ratio model, we present two likelihood approaches for the estimation...
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作者：Eastoe, Emma F.; Tawn, Jonathan A.
作者单位：Lancaster University
摘要：A standard approach to model the extreme values of a stationary process is the peaks over threshold method, which consists of imposing a high threshold, identifying clusters of exceedances of this threshold and fitting the maximum value from each cluster using the generalized Pareto distribution. This approach is strongly justified by underlying asymptotic theory. We propose an alternative model for the distribution of the cluster maxima that accounts for the subasymptotic theory of extremes o...
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作者：Zhu, Hong; Wang, Mei-Cheng
作者单位：University System of Ohio; Ohio State University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
摘要：In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initia...
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作者：Sun, Liuquan; Song, Xinyuan; Zhang, Zhigang
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese University of Hong Kong; Memorial Sloan Kettering Cancer Center
摘要：The mean residual life provides the remaining life expectancy of a subject who has survived to a certain time-point. When covariates are present, regression models are needed to study the association between the mean residual life function and potential regression covariates. In this paper, we propose a flexible class of semiparametric mean residual life models where some effects may be time-varying and some may be constant over time. In the presence of right censoring, we use the inverse prob...
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作者：Su, Zhihua; Cook, R. Dennis
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：In this article we propose a new model, called the inner envelope model, which leads to efficient estimation in the context of multivariate normal linear regression. The asymptotic distribution and the consistency of its maximum likelihood estimators are established. Theoretical results, simulation studies and examples all show that the efficiency gains can be substantial relative to standard methods and to the maximum likelihood estimators from the envelope model introduced recently by Cook e...
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作者：Stigler, Stephen M.
作者单位：University of Chicago
摘要：Karl Pearson's role in the transformation that took the 19th century statistics of Laplace and Gauss into the modern era of 20th century multivariate analysis is examined from a new point of view. By viewing Pearson's work in the context of a motto he adopted from Charles Darwin, a philosophical theme is identified in Pearson's statistical work, and his three major achievements are briefly described.
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作者：Kraus, David; Panaretos, Victor M.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：Inferences related to the second-order properties of functional data, as expressed by covariance structure, can become unreliable when the data are non-Gaussian or contain unusual observations. In the functional setting, it is often difficult to identify atypical observations, as their distinguishing characteristics can be manifold but subtle. In this paper, we introduce the notion of a dispersion operator, investigate its use in probing the second-order structure of functional data, and devel...
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作者：Sun, Tingni; Zhang, Cun-Hui
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs little beyond the computation of a path or grid of the sparse regression estimator for penalty levels above a proper threshold. For the scaled lasso, the algo...
