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• On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding AI翻译
  作者：Miles, C. H.; Shpitser, I; Kanki, P.; Meloni, S.; Tchetgen, E. J. Tchetgen
  作者单位：Columbia University; Johns Hopkins University; Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
  摘要：Path-specific effects constitute a broad class of mediated effects from an exposure to an outcome via one or more causal pathways along a set of intermediate variables. Most of the literature concerning estimation of mediated effects has focused on parametric models, with stringent assumptions regarding unmeasured confounding. We consider semiparametric inference of a path-specific effect when these assumptions are relaxed. In particular, we develop a suite of semiparametric estimators for the...
• Bayesian constraint relaxation AI翻译
  作者：Duan, Leo L.; Young, Alexander L.; Nishimura, Akihiko; Dunson, David B.
  作者单位：State University System of Florida; University of Florida; Duke University; University of California System; University of California Los Angeles
  摘要：Prior information often takes the form of parameter constraints. Bayesian methods include such information through prior distributions having constrained support. By using posterior sampling algorithms, one can quantify uncertainty without relying on asymptotic approximations. However, sharply constrained priors are not necessary in some settings and tend to limit modelling scope to a narrow set of distributions that are tractable computationally. We propose to replace the sharp indicator func...
• Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data AI翻译
  作者：Tan, Z.
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Propensity scores are widely used with inverse probability weighting to estimate treatment effects in observational studies. We study calibrated estimation as an alternative to maximum likelihood estimation for fitting logistic propensity score models. We show that, with possible model misspecification, minimizing the expected calibration loss underlying the calibrated estimators involves reducing both the expected likelihood loss and a measure of relative errors between the limiting and true ...
• Diagnosing missing always at random in multivariate data AI翻译
  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...
• Scalable inference for crossed random effects models AI翻译
  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 ...
• A conditional density estimation partition model using logistic Gaussian processes AI翻译
  作者：Payne, R. D.; Guha, N.; Ding, Y.; Mallick, B. K.
  作者单位：Eli Lilly; Lilly Research Laboratories; University of Massachusetts System; University of Massachusetts Lowell; Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station
  摘要：Conditional density estimation seeks to model the distribution of a response variable conditional on covariates. We propose a Bayesian partition model using logistic Gaussian processes to perform conditional density estimation. The partition takes the form of a Voronoi tessellation and is learned from the data using a reversible jump Markov chain Monte Carlo algorithm. The methodology models data in which the density changes sharply throughout the covariate space, and can be used to determine ...
• Bayesian sparse multiple regression for simultaneous rank reduction and variable selection AI翻译
  作者：Chakraborty, Antik; Bhattacharya, Anirban; Mallick, Bani K.
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-sparse matrices in a high-dimensional multiple-response linear regression model. We consider a carefully devised shrinkage prior on the matrix of regression coefficients which obviates the need to specify a prior on the rank, and shrinks the regression matrix towards low-rank and row-sparse structures. We provide theoretical support to the proposed methodology by proving minimax optimality of the posterior me...
• Consistent community detection in multi-layer network data AI翻译
  作者：Lei, Jing; Chen, Kehui; Lynch, Brian
  作者单位：Carnegie Mellon University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：We consider multi-layer network data where the relationships between pairs of elements are reflected in multiple modalities, and may be described by multivariate or even high-dimensional vectors. Under the multi-layer stochastic block model framework we derive consistency results for a least squares estimation of memberships. Our theorems show that, as compared to single-layer community detection, a multi-layer network provides much richer information that allows for consistent community detec...
• Simplified integrated nested Laplace approximation AI翻译
  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 ...
• Multisample estimation of bacterial composition matrices in metagenomics data AI翻译
  作者：Cao, Yuanpei; Zhang, Anru; Li, Hongzhe
  作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison
  摘要：Metagenomics sequencing is routinely applied to quantify bacterial abundances in microbiome studies, where bacterial composition is estimated based on the sequencing read counts. Due to limited sequencing depth and DNA dropouts, many rare bacterial taxa might not be captured in the final sequencing reads, which results in many zero counts. Naive composition estimation using count normalization leads to many zero proportions, which tend to result in inaccurate estimates of bacterial abundance a...