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作者：Enki, Doyo G.; Noufaily, Angela; Farrington, C. Paddy
作者单位：Open University - UK
摘要：We propose a new parametric time-varying shared frailty model to represent changes over time in population heterogeneity, for use with bivariate current status data. The model uses a power transformation of a time-invariant frailty U, and is particularly convenient when U is a member of the generalized gamma family. This model avoids some shortcomings of a previously suggested time-varying frailty model, notably time-dependent support. We describe some key properties of the model, including it...
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作者：Jernite, Yacine; Latouche, Pierre; Bouveyron, Charles; Rivera, Patrick; Jegou, Laurent; Lamasse, Stephane
作者单位：Institut Polytechnique de Paris; Ecole Polytechnique; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); heSam Universite; Universite Pantheon-Sorbonne; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Humanities & Social Sciences (INSHS)
摘要：In the last two decades many random graph models have been proposed to extract knowledge from networks. Most of them look for communities or, more generally, clusters of vertices with homogeneous connection profiles. While the first models focused on networks with binary edges only, extensions now allow to deal with valued networks. Recently, new models were also introduced in order to characterize connection patterns in networks through mixed memberships. This work was motivated by the need o...
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作者：Volfovsky, Alexander; Hoff, Peter D.
作者单位：Harvard University; University of Washington; University of Washington Seattle
摘要：ANOVA decompositions are a standard method for describing and estimating heterogeneity among the means of a response variable across levels of multiple categorical factors. In such a decomposition, the complete set of main effects and interaction terms can be viewed as a collection of vectors, matrices and arrays that share various index sets defined by the factor levels. For many types of categorical factors, it is plausible that an ANOVA decomposition exhibits some consistency across orders ...
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作者：Huang, Yen-Tsung; VanderWeele, Tyler J.; Lin, Xihong
作者单位：Brown University; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Genetic association studies have been a popular approach for assessing the association between common Single Nucleotide Polymorphisms (SNPs) and complex diseases. However, other genomic data involved in the mechanism from SNPs to disease, for example, gene expressions, are usually neglected in these association studies. In this paper, we propose to exploit gene expression information to more powerfully test the association between SNPs and diseases by jointly modeling the relations among SNPs,...
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作者：Guolo, Annamaria; Varin, Cristiano
作者单位：University of Verona; Universita Ca Foscari Venezia
摘要：Bounded time series consisting of rates or proportions are often encountered in applications. This manuscript proposes a practical approach to analyze bounded time series, through a beta regression model. The method allows the direct interpretation of the regression parameters on the original response scale, while properly accounting for the heteroskedasticity typical of bounded variables. The serial dependence is modeled by a Gaussian copula, with a correlation matrix corresponding to a stati...
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作者：Wang, Xin; Yuan, Ke; Hellmayr, Christoph; Liu, Wei; Markowetz, Florian
作者单位：CRUK Cambridge Institute; Cancer Research UK; University of Cambridge
摘要：Inferring time-varying networks is important to understand the development and evolution of interactions over time. However, the vast majority of currently used models assume direct measurements of node states, which are often difficult to obtain, especially in fields like cell biology, where perturbation experiments often only provide indirect information of network structure. Here we propose hidden Markov nested effects models (HM-NEMs) to model the evolving network by a Markov chain on a st...
