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作者：Jin, Shaobo; Andersson, Bjorn
作者单位：Uppsala University; University of Oslo
摘要：Numerical quadrature methods are needed for many models in order to approximate integrals in the likelihood function. In this note, we correct the error rate given by Liu & Pierce (1994) for integrals approximated with adaptive Gauss-Hermite quadrature and show that the approximation is less accurate than previously thought. We discuss the relationship between the error rates of adaptive Gauss-Hermite quadrature and Laplace approximation, and provide a theoretical explanation of simulation res...
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作者：Bojinov, Iavor I.; Pillai, Natesh S.; Rubin, Donald B.
作者单位：Harvard University; Harvard University; Tsinghua University
摘要：Models for analysing multivariate datasets with missing values require strong, often unassessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable, which is a two-fold assumption dependent on the mode of inference. The first part, which is the focus here, under the Bayesian and direct-likelihood paradigms requires that the missing data be missing at random; in contrast, the frequentist-likelihood paradigm demands that the missing data mech...
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作者：Papaspiliopoulos, O.; Roberts, G. O.; Zanella, G.
作者单位：ICREA; University of Warwick; Bocconi University; Bocconi University
摘要：We develop methodology and complexity theory for Markov chain Monte Carlo algorithms used in inference for crossed random effects models in modern analysis of variance. We consider a plain Gibbs sampler and propose a simple modification, referred to as a collapsed Gibbs sampler. Under some balancedness conditions on the data designs and assuming that precision hyperparameters are known, we demonstrate that the plain Gibbs sampler is not scalable, in the sense that its complexity is worse than ...
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作者：Song, Qifan; Sun, Yan; Ye, Mao; Liang, Faming
作者单位：Purdue University System; Purdue University
摘要：Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-sc...
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作者：Chang, Jinyuan; Kolaczyk, Eric D.; Yao, Qiwei
作者单位：Southwestern University of Finance & Economics - China; Boston University; University of London; London School Economics & Political Science
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作者：Nghiem, Linh H.; Byrd, Michael C.; Potgieter, Cornelis J.
作者单位：Australian National University; Southern Methodist University; Texas Christian University
摘要：Parameter estimation in linear errors-in-variables models typically requires that the measurement error distribution be known or estimable from replicate data. A generalized method of moments approach can be used to estimate model parameters in the absence of knowledge of the error distributions, but it requires the existence of a large number of model moments. In this paper, parameter estimation based on the phase function, a normalized version of the characteristic function, is considered. T...
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作者：Al Mohamad, Diaa; Van Zwet, Erik W.; Cator, Eric; Goeman, Jelle J.
作者单位：Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; Radboud University Nijmegen
摘要：We present a new general method for constrained likelihood ratio testing which, when few constraints are violated, improves upon the existing approach in the literature that compares the likelihood ratio with the quantile of a mixture of chi-squared distributions; the improvement is in terms of both simplicity and power. The proposed method compares the constrained likelihood ratio statistic against the quantile of only one chi-squared random variable with data-dependent degrees of freedom. Th...
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作者：Vovk, Vladimir; Wang, Ruodu
作者单位：University of London; Royal Holloway University London; University of Waterloo
摘要：This paper proposes general methods for the problem of multiple testing of a single hypothesis, with a standard goal of combining a number of p-values without making any assumptions about their dependence structure. A result by Ruschendorf (1982) and, independently, Meng (1993) implies that the p-values can be combined by scaling up their arithmetic mean by a factor of 2, and no smaller factor is sufficient in general. A similar result by Mattner about the geometric mean replaces 2 by e. Based...
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作者：Zhang, X.; Lee, C. E.; Shao, X.
作者单位：State University System of Florida; Florida State University; University of Tennessee System; University of Tennessee Knoxville; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Envelopes have been proposed in recent years as a nascent methodology for sufficient dimension reduction and efficient parameter estimation in multivariate linear models. We extend the classical definition of envelopes in to incorporate a nonlinear conditional mean function and a heteroscedastic error. Given any two random vectors and , we propose two new model-free envelopes, called the martingale difference divergence envelope and the central mean envelope, and study their relationships to t...
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作者：Kedagni, Desire; Mourifie, Ismael
作者单位：Iowa State University; University of Toronto
摘要：This paper proposes a new set of testable implications for the instrumental variable independence assumption for discrete treatment, but unrestricted outcome and instruments: generalized instrumental inequalities. When outcome and treatment are both binary, but instruments are unrestricted, we show that the generalized instrumental inequalities are necessary and sufficient to detect all observable violations of the instrumental variable independence assumption. To test the generalized instrume...
