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作者：Shah, Rajen D.; Buhlmann, Peter
作者单位：University of Cambridge
摘要：We propose a framework for constructing goodness-of-fit tests in both low and high dimensional linear models. We advocate applying regression methods to the scaled residuals following either an ordinary least squares or lasso fit to the data, and using some proxy for prediction error as the final test statistic. We call this family residual prediction tests. We show that simulation can be used to obtain the critical values for such tests in the low dimensional setting and demonstrate using bot...
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作者：Khare, Kshitij; Rajaratnam, Bala; Saha, Abhishek
作者单位：State University System of Florida; University of Florida; University of California System; University of California Davis; University of California System; University of California Davis
摘要：Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that, when the graph G is decomposable, Bayesian inference is significantly more tractable than in the general non-decomposable setting. Penalized likelihood inference in contrast has made tremendous gains in the past few years in terms of scalability and tractability. Bayesian inference, however, has not had the same level of success, though a scalable Bayesian approach has ...
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作者：Hemerik, Jesse; Goeman, Jelle J.
作者单位：Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC)
摘要：Significance analysis of microarrays (SAM) is a highly popular permutation-based multiple-testing method that estimates the false discovery proportion (FDP): the fraction of false positive results among all rejected hypotheses. Perhaps surprisingly, until now this method had no known properties. This paper extends SAM by providing 1- upper confidence bounds for the FDP, so that exact confidence statements can be made. As a special case, an estimate of the FDP is obtained that underestimates th...
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作者：Bloem-Reddy, Benjamin; Orbanz, Peter
作者单位：University of Oxford; Columbia University
摘要：We introduce a class of generative network models that insert edges by connecting the starting and terminal vertices of a random walk on the network graph. Within the taxonomy of statistical network models, this class is distinguished by permitting the location of a new edge to depend explicitly on the structure of the graph, but being nonetheless statistically and computationally tractable. In the limit of infinite walk length, the model converges to an extension of the preferential attachmen...
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作者：Deligiannidis, George; Doucet, Arnaud; Pitt, Michael K.
作者单位：University of Oxford; University of London; King's College London
摘要：The pseudomarginal algorithm is a Metropolis-Hastings-type scheme which samples asymptotically from a target probability density when we can only estimate unbiasedly an unnormalized version of it. In a Bayesian context, it is a state of the art posterior simulation technique when the likelihood function is intractable but can be estimated unbiasedly by using Monte Carlo samples. However, for the performance of this scheme not to degrade as the number T of data points increases, it is typically...
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作者：Liang, Faming; Jia, Bochao; Xue, Jingnan; Li, Qizhai; Luo, Ye
作者单位：Purdue University System; Purdue University; State University System of Florida; University of Florida; Chinese Academy of Sciences
摘要：Missing data are frequently encountered in high dimensional problems, but they are usually difficult to deal with by using standard algorithms, such as the expectation-maximization algorithm and its variants. To tackle this difficulty, some problem-specific algorithms have been developed in the literature, but there still lacks a general algorithm. This work is to fill the gap: we propose a general algorithm for high dimensional missing data problems. The algorithm works by iterating between a...
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作者：Liang, Liang; Ma, Yanyuan; Wei, Ying; Carroll, Raymond J.
作者单位：Texas A&M University System; Texas A&M University College Station; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Columbia University; University of Technology Sydney
摘要：Analysing secondary outcomes is a common practice for case-control studies. Traditional secondary analysis employs either completely parametric models or conditional mean regression models to link the secondary outcome to covariates. In many situations, quantile regression models complement mean-based analyses and provide alternative new insights on the associations of interest. For example, biomedical outcomes are often highly asymmetric, and median regression is more useful in describing the...
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作者：Wang, Fangfang; Wang, Haonan
作者单位：University of Wisconsin System; University of Wisconsin Madison; Colorado State University System; Colorado State University Fort Collins
摘要：We develop a new parameter-driven model for multivariate time series of counts. The time series is not necessarily stationary. We model the mean process as the product of modulating factors and unobserved stationary processes. The former characterizes the long-run movement in the data, whereas the latter is responsible for rapid fluctuations and other unknown or unavailable covariates. The unobserved stationary processes evolve independently of the past observed counts and might interact with ...
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作者：Lauritzen, Steffen; Rinaldo, Alessandro; Sadeghi, Kayvan
作者单位：University of Copenhagen; Carnegie Mellon University; University of Cambridge
摘要：We study conditional independence relationships for random networks and their interplay with exchangeability. We show that, for finitely exchangeable network models, the empirical subgraph densities are maximum likelihood estimates of their theoretical counterparts. We then characterize all possible Markov structures for finitely exchangeable random graphs, thereby identifying a new class of Markov network models corresponding to bidirected Kneser graphs. In particular, we demonstrate that the...
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作者：Zhao, Zifeng; Zhang, Zhengjun
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：The paper presents a novel non-linear framework for the construction of flexible multivariate dependence structure (i.e. copulas) from existing copulas based on a straightforward pairwise max-'rule. The newly constructed max-copula has a closed form and has strong interpretability. Compared with the classical linear symmetric' mixture copula, the max-copula can be viewed as a non-linear asymmetric' framework. It is capable of modelling asymmetric dependence and joint tail behaviour while also ...
