Biometrika

最新文章

• Default Bayesian analysis with global-local shrinkage priors AI翻译
  作者：Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon
  作者单位：Purdue University System; Purdue University; Duke University; University of Chicago
  摘要：We provide a framework for assessing the default nature of a prior distribution using the property of regular variation, which we study for global-local shrinkage priors. In particular, we show that the horseshoe priors, originally designed to handle sparsity, are regularly varying and thus are appropriate for default Bayesian analysis. To illustrate our methodology, we discuss four problems of noninformative priors that have been shown to be highly informative for nonlinear functions. In each...
• On the win-ratio statistic in clinical trials with multiple types of event AI翻译
  作者：Oakes, D.
  作者单位：University of Rochester
  摘要：Pocock et al. (2012), following Finkelstein & Schoenfeld (1999), has popularized the win ratio for analysis of controlled clinical trials with multiple types of outcome event. The approach uses pairwise comparisons between patients in the treatment and control groups using a primary outcome, say the time to death, with ties broken using a secondary outcome, say the time to hospitalization. In general the observed pairwise preferences and the weight they attach to the component rankings will de...
• The correlation space of Gaussian latent tree models and model selection without fitting AI翻译
  作者：Shiers, N.; Zwiernik, P.; Aston, J. A. D.; Smith, J. Q.
  作者单位：University of Warwick; Pompeu Fabra University; University of Cambridge; University of Warwick
  摘要：We provide a complete description of possible distributions consistent with any Gaussian latent tree model. This description consists of polynomial equations and inequalities involving covariances between the observed variables. Testing inequality constraints can be done using the inverse Wishart distribution and this leads to simple preliminary assessment of tree-compatibility. To test equality constraints we employ general techniques of tetrad analyses. This approach is effective even for sm...
• Nonparametric identification and maximum likelihood estimation for hidden Markov models AI翻译
  作者：Alexandrovich, G.; Holzmann, H.; Leister, A.
  作者单位：Philipps University Marburg
  摘要：Nonparametric identification and maximum likelihood estimation for finite-state hidden Markov models are investigated. We obtain identification of the parameters as well as the order of the Markov chain if the transition probability matrices have full-rank and are ergodic, and if the state-dependent distributions are all distinct, but not necessarily linearly independent. Based on this identification result, we develop a nonparametric maximum likelihood estimation theory. First, we show that t...
• On distributions of ratios AI翻译
  作者：Broda, Simon A.; Kan, Raymond
  作者单位：University of Amsterdam; University of Toronto
  摘要：Inversion formulae are derived that express the density and distribution function of a ratio of random variables in terms of the joint characteristic function of the numerator and denominator. The resulting expressions are amenable to numerical evaluation and lead to simple asymptotic expansions. The expansions reduce to known results when the denominator is almost surely positive. Their accuracy is demonstrated with numerical examples.
• An extended hazard model with longitudinal covariates AI翻译
  作者：Tseng, Y. K.; Su, Y. R.; Mao, M.; Wang, J. L.
  作者单位：National Central University; Fred Hutchinson Cancer Center; University of California System; University of California Davis
  摘要：In clinical trials and other medical studies, it has become increasingly common to observe simultaneously an event time of interest and longitudinal covariates. In the literature, joint modelling approaches have been employed to analyse both survival and longitudinal processes and to investigate their association. However, these approaches focus mostly on developing adaptive and flexible longitudinal processes based on a prespecified survival model, most commonly the Cox proportional hazards m...
• Covariance-enhanced discriminant analysis AI翻译
  作者：Xu, Peirong; Zhu, Ji; Zhu, Lixing; Li, Yi
  作者单位：Southeast University - China; University of Michigan System; University of Michigan; Hong Kong Baptist University; University of Michigan System; University of Michigan
  摘要：Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced discriminant analysis method ...
• Doubly robust learning for estimating individualized treatment with censored data AI翻译
  作者：Zhao, Y. Q.; Zeng, D.; Laber, E. B.; Song, R.; Yuan, M.; Kosorok, M. R.
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of Wisconsin System; University of Wisconsin Madison
  摘要：Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data. To adjust for censoring, we propose a doubly robust estimator which requires correct specification of either the censoring model or surviv...
• Strong control of the familywise error rate in observational studies that discover effect modification by exploratory methods AI翻译
  作者：Hsu, Jesse Y.; Zubizarreta, Jose R.; Small, Dylan S.; Rosenbaum, Paul R.
  作者单位：University of Pennsylvania; Columbia University; University of Pennsylvania
  摘要：An effect modifier is a pretreatment covariate that affects the magnitude of the treatment effect or its stability. When there is effect modification, an overall test that ignores an effect modifier may be more sensitive to unmeasured bias than a test that combines results from subgroups defined by the effect modifier. If there is effect modification, one would like to identify specific subgroups for which there is evidence of effect that is insensitive to small or moderate biases. In this pap...
• Clarifying missing at random and related definitions, and implications when coupled with exchangeability AI翻译
  作者：Mealli, Fabrizia; Rubin, Donald B.
  作者单位：University of Florence; Harvard University
  摘要：We clarify the key concept of missingness at random in incomplete data analysis. We first distinguish between data being missing at random and the missingness mechanism being a missing-at-random one, which we call missing always at random and which is more restrictive. We further discuss how, in general, neither of these conditions is a statement about conditional independence. We then consider the implication of the more restrictive missing-always-at-random assumption when coupled with full u...