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作者：Liu, Jingchen; Xu, Gongjun
作者单位：Columbia University
摘要：In this paper, we derive tail approximations of integrals of exponential functions or Gaussian random fields with varying mean functions and approximations of the associated point processes. This study is motivated naturally by multiple applications such as hypothesis testing for spatial models and financial applications.
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作者：Foygel, Rina; Draisma, Jan; Drton, Mathias
作者单位：University of Chicago; Eindhoven University of Technology
摘要：A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a mixed graph in which directed edges encode the linear equations and bidirected edges indicate possible correlations among noise terms. We study parameter identifiability in these models, that is, we ask for conditions that ensure that the edge coefficients and correlations appearing in a linear structural equation m...
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作者：Tchetgen, Eric J. Tchetgen; Shpitser, Ilya
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health
摘要：While estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon evaluating the total effect of the exposure, investigators routinely wish to make inferences about the direct or indirect pathways of the effect of the exposure, through a mediator...
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作者：Fienberg, Stephen E.; Rinaldo, Alessandro
作者单位：Carnegie Mellon University
摘要：We study maximum likelihood estimation in log-linear models under conditional Poisson sampling schemes. We derive necessary and sufficient conditions for existence of the maximum likelihood estimator (MLE) of the model parameters and investigate estimability of the natural and mean-value parameters under a nonexistent MLE. Our conditions focus on the role of sampling zeros in the observed table. We situate our results within the framework of extended exponential families, and we exploit the ge...
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作者：Letac, Gerard; Massam, Helene
作者单位：Universite de Toulouse; Universite Toulouse III - Paul Sabatier; York University - Canada
摘要：A standard tool for model selection in a Bayesian framework is the Bayes factor which compares the marginal likelihood of the data under two given different models. In this paper, we consider the class of hierarchical loglinear models for discrete data given under the form of a contingency table with multinomial sampling. We assume that the prior distribution on the loglinear parameters is the Diaconis-Ylvisaker conjugate prior, and the uniform is the prior distribution on the space of models....
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作者：Tibshirani, Ryan J.; Taylor, Jonathan
作者单位：Carnegie Mellon University; Stanford University
摘要：We derive the degrees of freedom of the lasso fit, placing no assumptions on the predictor matrix X. Like the well-known result of Zou, Hastie and Tibshirani [Ann. Statist. 35 (2007) 2173-2192], which gives the degrees of freedom of the lasso fit when X has full column rank, we express our result in terms of the active set of a lasso solution. We extend this result to cover the degrees of freedom of the generalized lasso fit for an arbitrary predictor matrix X (and an arbitrary penalty matrix ...
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作者：Tang, Runlong; Banerjee, Moulinath; Kosorok, Michael R.
作者单位：Princeton University; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
摘要：In this paper, we study the nonparametric maximum likelihood estimator for an event time distribution function at a point in the current status model with observation times supported on a grid of potentially unknown sparsity and with multiple subjects sharing the same observation time. This is of interest since observation time ties occur frequently with current status data. The grid resolution is specified as cn(-gamma) with c > 0 being a scaling constant and gamma > 0 regulating the sparsity...
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作者：Yuan, Ming
作者单位：University System of Georgia; Georgia Institute of Technology
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作者：Einmahl, John H. J.; Krajina, Andrea; Segers, Johan
作者单位：Tilburg University; University of Gottingen; Universite Catholique Louvain
摘要：Consider a random sample in the max-domain of attraction of a multivariate extreme value distribution such that the dependence structure of the attractor belongs to a parametric model. A new estimator for the unknown parameter is defined as the value that minimizes the distance between a vector of weighted integrals of the tail dependence function and their empirical counterparts. The minimization problem has, with probability tending to one, a unique, global solution. The estimator is consist...
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作者：Wu, Yuan; Zhang, Ying
作者单位：University of California System; University of California San Diego; University of Iowa
摘要：The analysis of the joint cumulative distribution function (CDF) with bivariate event time data is a challenging problem both theoretically and numerically. This paper develops a tensor spline-based sieve maximum likelihood estimation method to estimate the joint CDF with bivariate current status data. The I-splines are used to approximate the joint CDF in order to simplify the numerical computation of a constrained maximum likelihood estimation problem. The generalized gradient projection alg...
