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作者：Ray, Surajit; Lindsay, Bruce G.
作者单位：Boston University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We propose a general class of risk measures which can be used for data-based evaluation of parametric models. The loss function is defined as the generalized quadratic distance between the true density and the model proposed. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a non-negative definite kernel and a bandwidth parameter. Using asymptotic results for the quadratic distances we build a quick-to-compute approximation for the ...
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作者：McCullagh, Peter
作者单位：University of Chicago
摘要：In a regression model, the joint distribution for each finite sample of units is determined by a function p(x)(y) depending only on the list of covariate values x=(x(u(1)),...,x(u(n))) on the sampled units. No random sampling of units is involved. In biological work, random sampling is frequently unavoidable, in which case the joint distribution p(y,x) depends on the sampling scheme. Regression models can be used for the study of dependence provided that the conditional distribution p(y vertic...
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作者：Bickel, Peter; Buehlmann, Peter; Yao, Qiwei; Samworth, Richard; Hall, Peter; Titterington, D. M.; Xue, Jing-Hao; Anagnostopoulos, C.; Tasoullis, D. K.; Zhang, Wenyang; Xia, Yingcun; Johnstone, Iain M.; Richardson, Sylvia; Bottolo, Leonardo; Kent, John T.; Adragni, Kofi; Cook, R. Dennis; Gather, Ursula; Guddat, Charlotte; Greenshtein, Eitan; James, Gareth M.; Radchenko, Peter; Leng, Chenlei; Wang, Hansheng; Levina, Elizaveta; Zhu, Ji; Li, Runze; Liu, Yufeng; Longford, N. T.; Luo, Weiqi; Baxter, Paul D.; Taylor, Charles C.; Marron, J. S.; Morris, Jeffrey S.; Robert, Christian P.; Yu, Keming; Zhang, Cun-Hui; Zhang, Hao Helen; Zhou, Harrison H.; Lin, Xihong; Zou, Hui
作者单位：University of California System; University of California Berkeley; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of London; London School Economics & Political Science; University of Cambridge; University of Melbourne; University of Glasgow; Imperial College London; University of Bath; National University of Singapore; Stanford University; University of Leeds; University of Minnesota System; University of Minnesota Twin Cities; Dortmund University of Technology; Duke University; University of Southern California; Peking University; University of Michigan System; University of Michigan; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of North Carolina; University of North Carolina Chapel Hill; Pompeu Fabra University; University of Texas System; UTMD Anderson Cancer Center; Universite PSL; Universite Paris-Dauphine; Institut Polytechnique de Paris; ENSAE Paris; Brunel University; Rutgers University System; Rutgers University New Brunswick; North Carolina State University; Yale University; Harvard University
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作者：Chen, Song Xi; Leung, Denis H. Y.; Qin, Jing
作者单位：Iowa State University; Peking University; Singapore Management University; National Institutes of Health (NIH) - USA
摘要：The paper considers estimating a parameter beta that defines an estimating function U(y, x, beta) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for beta depends critically on correctly specifying the conditional expectation of U given the surrogate and the covariate. When the condition...
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作者：Bayarri, M. J.; Garcia-Donato, G.
作者单位：Universidad de Castilla-La Mancha; University of Valencia
摘要：We introduce objective proper prior distributions for hypothesis testing and model selection based on measures of divergence between the competing models; we call them divergence-based (DB) priors. DB priors have simple forms and desirable properties like information (finite sample) consistency and are often similar to other existing proposals like intrinsic priors. Moreover, in normal linear model scenarios, they reproduce the Jeffreys-Zellner-Siow priors exactly, Most importantly, in challen...
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作者：Hall, Peter; Mueller, Hans-Georg; Yao, Fang
作者单位：University of California System; University of California Davis; University of Melbourne; University of Toronto
摘要：In longitudinal data analysis one frequently encounters non-Gaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson or of another discrete type or could be continuous. The timings of the repeated measurements are often sparse and irregular. We introduce a latent Gaussian process model for such data, establishing a connection to functional data analysis. The functional methods proposed are non-parametric and computa...
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作者：Cruyff, Maarten J. L. F.; van den Hout, Ardo; van der Heijden, Peter G. M.
作者单位：Utrecht University; MRC Biostatistics Unit
摘要：Randomized response (RR) is an interview technique that ensures confidentiality when questions are sensitive. In RR the answer to a sensitive question depends to a certain extent on a probability mechanism. As a result the observed data are partially misclassified, and the true status of the respondent is obscured. RR data are commonly analysed in a univariate way, with models that relate the observed responses to the prevalence of the sensitive characteristic, and with the more recent logisti...
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作者：Goetgeluk, Sylvie; Vansteelandt, Stijn; Goetghebeur, Els
作者单位：Ghent University; Harvard University; Harvard T.H. Chan School of Public Health
摘要：When regression models adjust for mediators on the causal path from exposure to outcome, the regression coefficient of exposure is commonly viewed as a measure of the direct exposure effect. This interpretation can be misleading, even with a randomly assigned exposure. This is because adjustment for post-exposure measurements introduces bias whenever their association with the outcome is confounded by more than just the exposure. By the same token, adjustment for such confounders stays problem...
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作者：Hojsgaard, Soren; Lauritzen, Steffen L.
作者单位：University of Oxford; Aarhus University
摘要：We introduce new types of graphical Gaussian models by placing symmetry restrictions on the concentration or correlation matrix. The models can be represented by coloured graphs, where parameters that are associated with edges or vertices of the same colour are restricted to being identical. We study the properties of such models and derive the necessary algorithms for calculating maximum likelihood estimates. We identify conditions for restrictions on the concentration and correlation matrice...
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作者：Zhou, Xiao-Hua; Lin, Huazhen; Johnson, Eric
作者单位：US Department of Veterans Affairs; Veterans Health Administration (VHA); Vet Affairs Puget Sound Health Care System; University of Washington; University of Washington Seattle; Sichuan University
摘要：We develop a new non-parametric heteroscedastic transformation regression model for predicting the expected value of the outcome of a patient with given patient's covariates when the distribution of the outcome is highly skewed with a heteroscedastic variance. In our model, we allow both the transformation function and the error distribution function to be unknown. We show that under some regularity conditions the estimators for regression parameters, the expected value of the original outcome...
