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作者：Pourahmadi, Mohsen
作者单位：Northern Illinois University
摘要：Chen & Dunson (2003) have proposed a modified Cholesky decomposition of the form Sigma = DLL'D for a covariance matrix where D is a diagonal matrix with entries proportional to the square roots of the diagonal entries of Sigma and L is a unit lower-triangular matrix solely determining its correlation matrix. This total separation of variance and correlation is definitely a major advantage over the more traditional modified Cholesky decomposition of the form (LDL)-L-2' (Pourahmadi, 1999). We sh...
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作者：Martinussen, Torben; Scheike, Thomas H.
作者单位：University of Copenhagen; University of Copenhagen
摘要：We study a test comparing the full Aalen additive hazards model and the change-point model, and suggest how to estimate the parameters of the change-point model. We also study a test for no change-point effect. Both tests are provided with large sample properties and a resampling method is applied to obtain p-values. The finite-sample properties of the proposed inference procedures and estimators are assessed through a simulation study. The methods are further applied to a dataset concerning m...
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作者：Fienberg, Stephen E.; Kim, Sung-Ho
作者单位：Carnegie Mellon University; Korea Advanced Institute of Science & Technology (KAIST)
摘要：We show that, when the three-way association level among the three binary variables, X, U-1 and U-2 is fixed, D-P = pr( X = 1 | U-1 = 1) - pr( X = 1 | U-1 = 0) increases as the cross-product ratio of U-1 and U-2 increases under the assumption that X is positively associated with U-1 and U-2. We then discuss some implications of this property.
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作者：Duan, Jason A.; Guindani, Michele; Gelfand, Alan E.
作者单位：Yale University; University of New Mexico; Duke University
摘要：Many models for the study of point-referenced data explicitly introduce spatial random effects to capture residual spatial association. These spatial effects are customarily modelled as a zero-mean stationary Gaussian process. The spatial Dirichlet process introduced by Gelfand et al. (2005) produces a random spatial process which is neither Gaussian nor stationary. Rather, it varies about a process that is assumed to be stationary and Gaussian. The spatial Dirichlet process arises as a probab...
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作者：Huang, Yifan; Hsu, Jason C.
作者单位：State University System of Florida; University of South Florida; University System of Ohio; Ohio State University
摘要：Holm's method and Hochberg's method for multiple testing can be viewed as step-down and step-up versions of the Bonferroni test. We show that both are special cases of partition testing. The difference is that, while Holm's method tests each partition hypothesis using the largest order statistic, setting a critical value based on the Bonferroni inequality, Hochberg's method tests each partition hypothesis using all the order statistics, setting a series of critical values based on Simes' inequ...
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作者：Jasra, Ajay; Stephens, David A.; Holmes, Christopher C.
作者单位：Imperial College London; McGill University; University of Oxford
摘要：We present an extension of population-based Markov chain Monte Carlo to the transdimensional case. A major challenge is that of simulating from high- and transdimensional target measures. In such cases, Markov chain Monte Carlo methods may not adequately traverse the support of the target; the simulation results will be unreliable. We develop population methods to deal with such problems, and give a result proving the uniform ergodicity of these population algorithms, under mild assumptions. T...
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作者：Vansteelandt, Stijn; Rotnitzky, Andrea; Robins, James
作者单位：Ghent University; Universidad Torcuato Di Tella; Harvard University; Harvard T.H. Chan School of Public Health
摘要：We propose a new class of models for making inference about the mean of a vector of repeated outcomes when the outcome vector is incompletely observed in some study units and missingness is nonmonotone. Each model in our class is indexed by a set of unidentified selection-bias functions which quantify the residual association of the outcome at each occasion t and the probability that this outcome is missing after adjusting for variables observed prior to time t and for the past nonresponse pat...
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作者：Mammen, Enno; Nielsen, Jens Perch
作者单位：University of Mannheim; City St Georges, University of London
摘要：Very often in survival analysis one has to study martingale integrals where the integrand is not predictable and where the counting process theory of martingales is not directly applicable, as for example in nonparametric and semiparametric applications where the integrand is based on a pilot estimate. We call this the predictability issue in survival analysis. The problem has been resolved by approximations of the integrand by predictable functions which have been justified by ad hoc procedur...
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作者：Yun, Sung-Cheol; Lee, Youngjo; Kenward, Michael G.
作者单位：University of Ulsan; Seoul National University (SNU); University of London; London School of Hygiene & Tropical Medicine
摘要：Most statistical solutions to the problem of statistical inference with missing data involve integration or expectation. This can be done in many ways: directly or indirectly, analytically or numerically, deterministically or stochastically. Missing-data problems can be formulated in terms of latent random variables, so that hierarchical likelihood methods of Lee & Nelder (1996) can be applied to missing-value problems to provide one solution to the problem of integration of the likelihood. Th...
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作者：Lijoi, Antonio; Mena, Ramses H.; Prunster, Igor
作者单位：University of Pavia; Universidad Nacional Autonoma de Mexico; University of Turin
摘要：We consider the problem of evaluating the probability of discovering a certain number of new species in a new sample of population units, conditional on the number of species recorded in a basic sample. We use a Bayesian nonparametric approach. The different species proportions are assumed to be random and the observations from the population exchangeable. We provide a Bayesian estimator, under quadratic loss, for the probability of discovering new species which can be compared with well-known...
