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作者：Padilla, Oscar Hernan Madrid; Sharpnack, James; Chen, Yanzhen; Witten, Daniela M.
作者单位：University of California System; University of California Los Angeles; University of California System; University of California Davis; Hong Kong University of Science & Technology; University of Washington; University of Washington Seattle
摘要：The fused lasso, also known as total-variation denoising, is a locally adaptive function estimator over a regular grid of design points. In this article, we extend the fused lasso to settings in which the points do not occur on a regular grid, leading to a method for nonparametric regression. This approach, which we call the K-nearest-neighbours fused lasso, involves computing the K-nearest-neighbours graph of the design points and then performing the fused lasso over this graph. We show that ...
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作者：Li, Didong; Dunson, David B.
作者单位：Duke University; Duke University
摘要：Classifiers label data as belonging to one of a set of groups based on input features. It is challenging to achieve accurate classification when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports. This is particularly true when training data are limited. To address this problem, we propose a new type of classifier based on obtaining a local approximation to the support of the data within each class in a neighbourhood of the fea...
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作者：Heuchenne, C.; De Una-Alvarez, J.; Laurent, G.
作者单位：University of Liege; Universidade de Vigo
摘要：Cross-sectional sampling is often used when investigating inter-event times, resulting in left-truncated and right-censored data. In this paper, we consider a semiparametric truncation model in which the truncating variable is assumed to belong to a certain parametric family. We examine two methods of estimating both the truncation and the lifetime distributions. We obtain asymptotic representations of the estimators for the lifetime distribution and establish their weak convergence. Both of t...
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作者：Li, Tianxi; Levina, Elizaveta; Zhu, Ji
作者单位：University of Virginia; University of Michigan System; University of Michigan
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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...
