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作者：Jiang, Zhichao; Ding, Peng
作者单位：University of Massachusetts System; University of Massachusetts Amherst; University of California System; University of California Berkeley
摘要：Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome. We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value. We show that the measurement error of the instrumental variable does not bias the estimate, that the measurement error of the treatment ...
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作者：Lijoi, Antonio; Prunster, Igor; Rigon, Tommaso
作者单位：Bocconi University
摘要：Discrete nonparametric priors play a central role in a variety of Bayesian procedures, most notably when used to model latent features, such as in clustering, mixtures and curve fitting. They are effective and well-developed tools, though their infinite dimensionality is unsuited to some applications. If one restricts to a finite-dimensional simplex, very little is known beyond the traditional Dirichlet multinomial process, which is mainly motivated by conjugacy. This paper introduces an alter...
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作者：Simpson, E. S.; Wadsworth, J. L.; Tawn, J. A.
作者单位：Lancaster University
摘要：In multivariate extreme value analysis, the nature of the extremal dependence between variables should be considered when selecting appropriate statistical models. Interest often lies in determining which subsets of variables can take their largest values simultaneously while the others are of smaller order. Our approach to this problem exploits hidden regular variation properties on a collection of nonstandard cones, and provides a new set of indices that reveal aspects of the extremal depend...
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作者：Bellach, A.; Kosorok, M. R.; Gilbert, P. B.; Fine, J. P.
作者单位：University of Washington; University of Washington Seattle; University of North Carolina; University of North Carolina Chapel Hill; Fred Hutchinson Cancer Center
摘要：Left-truncation poses extra challenges for the analysis of complex time-to-event data. We propose a general semiparametric regression model for left-truncated and right-censored competing risks data that is based on a novel weighted conditional likelihood function. Targeting the subdistribution hazard, our parameter estimates are directly interpretable with regard to the cumulative incidence function. We compare different weights from recent literature and develop a heuristic interpretation fr...
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作者：Guo, F. Richard; Richardson, Thomas S.
作者单位：University of Washington; University of Washington Seattle
摘要：We consider testing marginal independence versus conditional independence in a trivariate Gaussian setting. The two models are nonnested, and their intersection is a union of two marginal independences. We consider two sequences of such models, one from each type of independence, that are closest to each other in the Kullback-Leibler sense as they approach the intersection. They become indistinguishable if the signal strength, as measured by the product of two correlation parameters, decreases...
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作者：Tyler, David E.; Yi, Mengxi
作者单位：Rutgers University System; Rutgers University New Brunswick; University of International Business & Economics
摘要：The properties of penalized sample covariance matrices depend on the choice of the penalty function. In this paper, we introduce a class of nonsmooth penalty functions for the sample covariance matrix and demonstrate how their use results in a grouping of the estimated eigenvalues. We refer to the proposed method as lassoing eigenvalues, or the elasso.
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作者：Dunson, D. B.; Johndrow, J. E.
作者单位：Duke University; University of Pennsylvania
摘要：In a 1970 Biometrika paper, W. K. Hastings developed a broad class of Markov chain algorithms for sampling from probability distributions that are difficult to sample from directly. The algorithm draws a candidate value from a proposal distribution and accepts the candidate with a probability that can be computed using only the unnormalized density of the target distribution, allowing one to sample from distributions known only up to a constant of proportionality. The stationary distribution o...
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作者：Wang, Yixin; Zubizarreta, Jose R.
作者单位：Columbia University; Harvard University
摘要：Weighting methods are widely used to adjust for covariates in observational studies, sample surveys, and regression settings. In this paper, we study a class of recently proposed weighting methods, which find the weights of minimum dispersion that approximately balance the covariates. We call these weights 'minimal weights' and study them under a common optimization framework. Our key observation is that finding weights which achieve approximate covariate balance is equivalent to performing sh...
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作者：Legramanti, Sirio; Durante, Daniele; Dunson, David B.
作者单位：Bocconi University; Duke University
摘要：The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumula...
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作者：Deleamont, P-Y; La Vecchia, D.
作者单位：University of Geneva
摘要：We develop and implement a novel M-estimation method for locally stationary diffusions observed at discrete time-points. We give sufficient conditions for the local stationarity of general time-inhomogeneous diffusions. Then we focus on locally stationary diffusions with time-varying parameters, for which we define our M-estimators and derive their limit theory.
