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作者：Wood, Simon N.
作者单位：University of Bristol
摘要：Integrated nested Laplace approximation provides accurate and efficient approximations for marginal distributions in latent Gaussian random field models. Computational feasibility of the original Rue et al. (2009) methods relies on efficient approximation of Laplace approximations for the marginal distributions of the coefficients of the latent field, conditional on the data and hyperparameters. The computational efficiency of these approximations depends on the Gaussian field having a Markov ...
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作者：Zhou, Ruixuan Rachel; Wang, Liewei; Zhao, Sihai Dave
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Mayo Clinic
摘要：Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under compl...
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作者：Cannings, Timothy, I; Fan, Yingying; Samworth, Richard J.
作者单位：University of Edinburgh; University of Southern California; University of Cambridge
摘要：We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent...
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作者：Xie, Fangzheng; Xu, Yanxun
作者单位：Johns Hopkins University
摘要：We propose and prove the optimality of a Bayesian approach for estimating the latent positions in random dot product graphs, which we call posterior spectral embedding. Unlike classical spectral-based adjacency, or Laplacian spectral embedding, posterior spectral embedding is a fully likelihood-based graph estimation method that takes advantage of the Bernoulli likelihood information of the observed adjacency matrix. We develop a minimax lower bound for estimating the latent positions, and sho...
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作者：Ning, Yang; Sida, Peng; Imai, Kosuke
作者单位：Cornell University; Microsoft; Harvard University
摘要：We propose a robust method to estimate the average treatment effects in observational studies when the number of potential confounders is possibly much greater than the sample size. Our method consists of three steps. We first use a class of penalized M-estimators for the propensity score and outcome models. We then calibrate the initial estimate of the propensity score by balancing a carefully selected subset of covariates that are predictive of the outcome. Finally, the estimated propensity ...
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作者：Mukherjee, K.
作者单位：Lancaster University
摘要：We consider the weighted bootstrap approximation to the distribution of a class of M-estimators for the parameters of the generalized autoregressive conditional heteroscedastic model. We prove that the bootstrap distribution, given the data, is a consistent estimate in probability of the distribution of the M-estimator, which is asymptotically normal. We propose an algorithm for the computation of M-estimates which at the same time is useful for computing bootstrap replicates from the given da...
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作者：Lei, J.; Lin, K. Z.
作者单位：Carnegie Mellon University
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作者：Dehling, H.; Fried, R.; Wendler, M.
作者单位：Ruhr University Bochum; Dortmund University of Technology; Otto von Guericke University
摘要：We present a robust and nonparametric test for the presence of a changepoint in a time series, based on the two-sample Hodges-Lehmann estimator. We develop new limit theory for a class of statistics based on two-sample U-quantile processes in the case of short-range dependent observations. Using this theory, we derive the asymptotic distribution of our test statistic under the null hypothesis of a constant level. The proposed test shows better overall performance under normal, heavy-tailed and...
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作者：Kong, Xinbing
作者单位：Nanjing Audit University
摘要：We introduce a random-perturbation-based rank estimator of the number of factors of a large-dimensional approximate factor model. An expansion of the rank estimator demonstrates that the random perturbation reduces the biases due to the persistence of the factor series and the dependence between the factor and error series. A central limit theorem for the rank estimator with convergence rate higher than root n gives a new hypothesis-testing procedure for both one-sided and two-sided alternativ...
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作者：Lee, C. E.; Zhang, X.; Shao, X.
作者单位：University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; University of Illinois System; University of Illinois Urbana-Champaign
摘要：We propose a new nonparametric conditional mean independence test for a response variable Y and a predictor variable X where either or both can be function-valued. Our test is built on a new metric, the so-called functional martingale difference divergence, which fully characterizes the conditional mean dependence of Y given X and extends the martingale difference divergence proposed by Shao & Zhang (2014). We define an unbiased estimator of functional martingale difference divergence by using...
