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  • Network cross-validation by edge sampling
    Biometrika (IF 1.641) Pub Date : 2020-04-04
    Li T, Levina E, Zhu J.

    SummaryWhile many statistical models and methods are now available for network analysis, resampling of network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but it is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. In this paper

    更新日期:2020-04-06
  • Simplified integrated nested Laplace approximation
    Biometrika (IF 1.641) Pub Date : 2019-09-23
    Wood S.

    SummaryIntegrated 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

    更新日期:2020-03-20
  • High-dimensional causal discovery under non-Gaussianity
    Biometrika (IF 1.641) Pub Date : 2019-10-25
    Wang Y, Drton M.

    SummaryWe consider graphical models based on a recursive system of linear structural equations. This implies that there is an ordering, $\sigma$, of the variables such that each observed variable $Y_v$ is a linear function of a variable-specific error term and the other observed variables $Y_u$ with $\sigma(u) < \sigma (v)$. The causal relationships, i.e., which other variables the linear functions

    更新日期:2020-03-20
  • Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations
    Biometrika (IF 1.641) Pub Date : 2019-10-22
    Wang Y, Zubizarreta J.

    SummaryWeighting 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

    更新日期:2020-03-20
  • Bayesian sparse multiple regression for simultaneous rank reduction and variable selection
    Biometrika (IF 1.641) Pub Date : 2019-11-23
    Chakraborty A, Bhattacharya A, Mallick B.

    SummaryWe 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

    更新日期:2020-03-20
  • Diagnosing missing always at random in multivariate data
    Biometrika (IF 1.641) Pub Date : 2019-11-23
    Bojinov I, Pillai N, Rubin D.

    SummaryModels 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

    更新日期:2020-03-20
  • Measurement errors in the binary instrumental variable model
    Biometrika (IF 1.641) Pub Date : 2019-11-21
    Jiang Z, Ding P.

    SummaryInstrumental 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

    更新日期:2020-03-20
  • Scalable inference for crossed random effects models
    Biometrika (IF 1.641) Pub Date : 2019-11-15
    Papaspiliopoulos O, Roberts G, Zanella G.

    SummaryWe 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

    更新日期:2020-03-20
  • Semiparametric estimation of structural failure time models in continuous-time processes
    Biometrika (IF 1.641) Pub Date : 2019-10-29
    Yang S, Pieper K, Cools F.

    SummaryStructural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which

    更新日期:2020-03-20
  • Analysis of grouped data using conjugate generalized linear mixed models
    Biometrika (IF 1.641) Pub Date : 2019-10-25
    Lee J, Green P, Ryan L.

    SummaryThis article concerns a class of generalized linear mixed models for two-level grouped data, where the random effects are uniquely indexed by groups and are independent. We derive necessary and sufficient conditions for the marginal likelihood to be expressed in explicit form. These models are unified under the conjugate generalized linear mixed models framework, where conjugate refers to the

    更新日期:2020-03-20
  • The Hastings algorithm at fifty
    Biometrika (IF 1.641) Pub Date : 2019-12-24
    Dunson D, Johndrow J.

    SummaryIn 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

    更新日期:2020-03-20
  • Consistent community detection in multi-layer network data
    Biometrika (IF 1.641) Pub Date : 2019-12-22
    Lei J, Chen K, Lynch B.

    SummaryWe 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

    更新日期:2020-03-20
  • Multisample estimation of bacterial composition matrices in metagenomics data
    Biometrika (IF 1.641) Pub Date : 2019-12-06
    Cao Y, Zhang A, Li H.

    SummaryMetagenomics 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

    更新日期:2020-03-20
  • Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data
    Biometrika (IF 1.641) Pub Date : 2019-12-05
    Tan Z.

    SummaryPropensity 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

    更新日期:2020-03-20
  • A conditional density estimation partition model using logistic Gaussian processes
    Biometrika (IF 1.641) Pub Date : 2019-12-05
    Payne R, Guha N, Ding Y, et al.

    SummaryConditional 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

    更新日期:2020-03-20
  • On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding
    Biometrika (IF 1.641) Pub Date : 2019-11-23
    Miles C, Shpitser I, Kanki P, et al.

    SummaryPath-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

    更新日期:2020-03-20
  • ‘Variance estimation in the particle filter’
    Biometrika (IF 1.641) Pub Date : 2020-01-29
    Lee A, Whiteley N.

    Biometrika (2018), 105, pp. 609–25.

    更新日期:2020-03-20
  • Model-free approach to quantifying the proportion of treatment effect explained by a surrogate marker
    Biometrika (IF 1.641) Pub Date : 2019-12-24
    Wang X, Parast L, Tian L, et al.

    SummaryIn randomized clinical trials, the primary outcome, $Y$, often requires long-term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, $S$, to infer the treatment effect on $Y$, $\Delta$. Identifying such an $S$ and quantifying the proportion of treatment effect on $Y$ explained by the effect on $S$ are thus of great importance. Most existing methods

    更新日期:2020-03-20
  • Bayesian constraint relaxation.
    Biometrika (IF 1.641) Pub Date : 2019-12-24
    Leo L Duan,Alexander L Young,Akihiko Nishimura,David B Dunson

    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

    更新日期:2020-03-20
  • A robust method for shift detection in time series
    Biometrika (IF 1.641) Pub Date : 2020-03-14
    Dehling H, Fried R, Wendler M.

    SummaryWe 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

    更新日期:2020-03-20
  • Empirical likelihood test for a large-dimensional mean vector
    Biometrika (IF 1.641) Pub Date : 2020-03-19
    Cui X, Li R, Yang G, et al.

    SummaryThis paper is concerned with empirical likelihood inference on the population mean when the dimension $p$ and the sample size $n$ satisfy $p/n\rightarrow c\in [1,\infty)$. As shown in Tsao (2004), the empirical likelihood method fails with high probability when $p/n>1/2$ because the convex hull of the $n$ observations in $\mathbb{R}^p$ becomes too small to cover the true mean value. Moreover

    更新日期:2020-03-20
  • Accounting for unobserved covariates with varying degrees of estimability in high-dimensional biological data.
    Biometrika (IF 1.641) Pub Date : 2019-11-23
    Chris McKennan,Dan Nicolae

    An important phenomenon in high-throughput biological data is the presence of unobserved covariates that can have a significant impact on the measured response. When these covariates are also correlated with the covariate of interest, ignoring or improperly estimating them can lead to inaccurate estimates of and spurious inference on the corresponding coefficients of interest in a multivariate linear

    更新日期:2019-11-01
  • Nonidentifiability in the presence of factorization for truncated data.
    Biometrika (IF 1.641) Pub Date : 2019-08-21
    B Vakulenko-Lagun,J Qian,S H Chiou,R A Betensky

    A time to event, [Formula: see text], is left-truncated by [Formula: see text] if [Formula: see text] can be observed only if [Formula: see text]. This often results in oversampling of large values of [Formula: see text], and necessitates adjustment of estimation procedures to avoid bias. Simple risk-set adjustments can be made to standard risk-set-based estimators to accommodate left truncation when

    更新日期:2019-11-01
  • Optimal designs for frequentist model averaging.
    Biometrika (IF 1.641) Pub Date : 2019-08-21
    K Alhorn,K Schorning,H Dette

    We consider the problem of designing experiments for estimating a target parameter in regression analysis when there is uncertainty about the parametric form of the regression function. A new optimality criterion is proposed that chooses the experimental design to minimize the asymptotic mean squared error of the frequentist model averaging estimate. Necessary conditions for the optimal solution of

    更新日期:2019-11-01
  • Statistical inference of genetic pathway analysis in high dimensions.
    Biometrika (IF 1.641) Pub Date : 2019-08-21
    Yang Liu,Wei Sun,Alexander P Reiner,Charles Kooperberg,Qianchuan He

    Genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits. With dense genotyping and extensive imputation, the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size [Formula: see text]. Conducting genetic pathway analysis and statistical inference in such settings

    更新日期:2019-11-01
  • Generalized meta-analysis for multiple regression models across studies with disparate covariate information.
    Biometrika (IF 1.641) Pub Date : 2019-08-21
    Prosenjit Kundu,Runlong Tang,Nilanjan Chatterjee

    Meta-analysis is widely popular for synthesizing information on common parameters of interest across multiple studies because of its logistical convenience and statistical efficiency. We develop a generalized meta-analysis approach to combining information on multivariate regression parameters across multiple studies that have varying levels of covariate information. Using algebraic relationships among

    更新日期:2019-11-01
  • On nonparametric maximum likelihood estimation with double truncation.
    Biometrika (IF 1.641) Pub Date : 2019-07-23
    J Xiao,M G Hudgens

    Doubly truncated survival data arise if failure times are observed only within certain time intervals. The nonparametric maximum likelihood estimator is widely used to estimate the underlying failure time distribution. Using a directed graph representation of the data suggested by Vardi (1985), a certain graphical condition holds if and only if the nonparametric maximum likelihood estimate exists and

    更新日期:2019-11-01
  • Homogeneity tests of covariance matrices with high-dimensional longitudinal data.
    Biometrika (IF 1.641) Pub Date : 2019-05-24
    Ping-Shou Zhong,Runze Li,Shawn Santo

    This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution

    更新日期:2019-11-01
  • Sufficient direction factor model and its application to gene expression quantitative trait loci discovery.
    Biometrika (IF 1.641) Pub Date : 2019-05-18
    F Jiang,Y Ma,Y Wei

    Rapid improvement in technology has made it relatively cheap to collect genetic data, however statistical analysis of existing data is still much cheaper. Thus, secondary analysis of single-nucleotide polymorphism, SNP, data, i.e., reanalysing existing data in an effort to extract more information, is an attractive and cost-effective alternative to collecting new data. We study the relationship between

    更新日期:2019-11-01
  • Differential Markov random field analysis with an application to detecting differential microbial community networks.
    Biometrika (IF 1.641) Pub Date : 2019-05-18
    T T Cai,H Li,J Ma,Y Xia

    Micro-organisms such as bacteria form complex ecological community networks that can be greatly influenced by diet and other environmental factors. Differential analysis of microbial community structures aims to elucidate systematic changes during an adaptive response to changes in environment. In this paper, we propose a flexible Markov random field model for microbial network structure and introduce

    更新日期:2019-11-01
  • Pseudo-population bootstrap methods for imputed survey data.
    Biometrika (IF 1.641) Pub Date : 2019-05-18
    S Chen,D Haziza,C Léger,Z Mashreghi

    The most common way to treat item nonresponse in surveys is to replace a missing value by a plausible value constructed on the basis of fully observed variables. Treating the imputed values as if they were observed may lead to invalid inferences. Bootstrap variance estimators for various finite population parameters are obtained using two pseudo-population bootstrap schemes. We establish the asymptotic

    更新日期:2019-11-01
  • Spectral density estimation for random fields via periodic embeddings.
    Biometrika (IF 1.641) Pub Date : 2019-05-18
    Joseph Guinness

    We introduce methods for estimating the spectral density of a random field on a [Formula: see text]-dimensional lattice from incomplete gridded data. Data are iteratively imputed onto an expanded lattice according to a model with a periodic covariance function. The imputations are convenient computationally, in that circulant embedding and preconditioned conjugate gradient methods can produce imputations

    更新日期:2019-11-01
  • Covariate association eliminating weights: a unified weighting framework for causal effect estimation.
    Biometrika (IF 1.641) Pub Date : 2019-04-30
    Sean Yiu,Li Su

    Weighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is

    更新日期:2019-11-01
  • Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series.
    Biometrika (IF 1.641) Pub Date : 2019-04-08
    A Tank,E B Fox,A Shojaie

    Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale,

    更新日期:2019-11-01
  • Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes.
    Biometrika (IF 1.641) Pub Date : 2019-02-26
    I Díaz,O Savenkov,K Ballman

    We consider estimation of an optimal individualized treatment rule when a high-dimensional vector of baseline variables is available. Our optimality criterion is with respect to delaying the expected time to occurrence of an event of interest. We use semiparametric efficiency theory to construct estimators with properties such as double robustness. We propose two estimators of the optimal rule, which

    更新日期:2019-11-01
  • Joint testing and false discovery rate control in high-dimensional multivariate regression.
    Biometrika (IF 1.641) Pub Date : 2019-02-26
    Yin Xia,T Tony Cai,Hongzhe Li

    Multivariate regression with high-dimensional covariates has many applications in genomic and genetic research, in which some covariates are expected to be associated with multiple responses. This paper considers joint testing for regression coefficients over multiple responses and develops simultaneous testing methods with false discovery rate control. The test statistic is based on inverse regression

    更新日期:2019-11-01
  • Discussion of 'Gene hunting with hidden Markov model knockoffs'.
    Biometrika (IF 1.641) Pub Date : 2019-02-13
    S W Jewell,D M Witten

    更新日期:2019-11-01
  • Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process.
    Biometrika (IF 1.641) Pub Date : 2019-01-29
    Jian Kang,Brian J Reich,Ana-Maria Staicu

    This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational aigorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially-varying regression coefficient functions. In addition, under some

    更新日期:2019-11-01
  • Counting process-based dimension reduction methods for censored outcomes.
    Biometrika (IF 1.641) Pub Date : 2019-01-07
    Qiang Sun,Ruoqing Zhu,Tao Wang,Donglin Zeng

    We propose counting process-based dimension reduction methods for right-censored survival data. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. Our methods address two limitations of existing approaches. First, using the counting process formulation, they do not require estimation of the censoring distribution to compensate

    更新日期:2019-11-01
  • The Change-Plane Cox Model.
    Biometrika (IF 1.641) Pub Date : 2018-12-18
    Susan Wei,Michael R Kosorok

    We propose a projection pursuit technique in survival analysis for finding lower-dimensional projections that exhibit differentiated survival outcome. This idea is formally introduced as the change-plane Cox model, a non-regular Cox model with a change-plane in the covariate space dividing the population into two subgroups whose hazards are proportional. The proposed technique offers a potential framework

    更新日期:2019-11-01
  • Nonparametric regression with adaptive truncation via a convex hierarchical penalty.
    Biometrika (IF 1.641) Pub Date : 2018-12-13
    Asad Haris,Ali Shojaie,Noah Simon

    We consider the problem of nonparametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well suited to high-dimensional sparse additive models and combines the appealing features of finite basis representation and smoothing penalties. In the case of additive models, a finite basis representation provides a parsimonious

    更新日期:2019-11-01
  • Constrained likelihood for reconstructing a directed acyclic Gaussian graph.
    Biometrika (IF 1.641) Pub Date : 2018-12-13
    Yiping Yuan,Xiaotong Shen,Wei Pan,Zizhuo Wang

    Directed acyclic graphs are widely used to describe directional pairwise relations. Such relations are estimated by reconstructing a directed acyclic graph's structure, which is challenging when the ordering of nodes of the graph is unknown. In such a situation, existing methods such as the neighbourhood and search-and-score methods have high estimation errors or computational complexities, especially

    更新日期:2019-11-01
  • Integrative linear discriminant analysis with guaranteed error rate improvement.
    Biometrika (IF 1.641) Pub Date : 2018-10-22
    Quefeng Li,Lexin Li

    Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative

    更新日期:2019-11-01
  • Robust estimation of high-dimensional covariance and precision matrices.
    Biometrika (IF 1.641) Pub Date : 2018-10-20
    Marco Avella-Medina,Heather S Battey,Jianqing Fan,Quefeng Li

    High-dimensional data are often most plausibly generated from distributions with complex structure and leptokurtosis in some or all components. Covariance and precision matrices provide a useful summary of such structure, yet the performance of popular matrix estimators typically hinges upon a sub-Gaussianity assumption. This paper presents robust matrix estimators whose performance is guaranteed for

    更新日期:2019-11-01
  • Sequential rerandomization.
    Biometrika (IF 1.641) Pub Date : 2018-09-04
    Quan Zhou,Philip A Ernst,Kari Lock Morgan,Donald B Rubin,Anru Zhang

    The seminal work of Morgan & Rubin (2012) considers rerandomization for all the units at one time.In practice, however, experimenters may have to rerandomize units sequentially. For example, a clinician studying a rare disease may be unable to wait to perform an experiment until all the experimental units are recruited. Our work offers a mathematical framework for sequential rerandomization designs

    更新日期:2019-11-01
  • Gene hunting with hidden Markov model knockoffs.
    Biometrika (IF 1.641) Pub Date : 2018-08-04
    M Sesia,C Sabatti,E J Candès

    Modern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs

    更新日期:2019-11-01
  • Theoretical limits of microclustering for record linkage.
    Biometrika (IF 1.641) Pub Date : 2018-06-09
    J E Johndrow,K Lum,D B Dunson

    There has been substantial recent interest in record linkage, where one attempts to group the records pertaining to the same entities from one or more large databases that lack unique identifiers. This can be viewed as a type of microclustering, with few observations per cluster and a very large number of clusters. We show that the problem is fundamentally hard from a theoretical perspective and, even

    更新日期:2019-11-01
  • Kernel-based covariate functional balancing for observational studies.
    Biometrika (IF 1.641) Pub Date : 2018-06-05
    Raymond K W Wong,Kwun Chuen Gary Chan

    Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation

    更新日期:2019-11-01
  • Bayesian block-diagonal variable selection and model averaging.
    Biometrika (IF 1.641) Pub Date : 2018-06-05
    O Papaspiliopoulos,D Rossell

    We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also

    更新日期:2019-11-01
  • Partial likelihood estimation of isotonic proportional hazards models.
    Biometrika (IF 1.641) Pub Date : 2018-05-29
    Yunro Chung,Anastasia Ivanova,Michael G Hudgens,Jason P Fine

    We consider the estimation of the semiparametric proportional hazards model with an unspecified baseline hazard function where the effect of a continuous covariate is assumed to be monotone. Previous work on nonparametric maximum likelihood estimation for isotonic proportional hazard regression with right-censored data is computationally intensive, lacks theoretical justification, and may be prohibitive

    更新日期:2019-11-01
  • Partition-based ultrahigh-dimensional variable screening.
    Biometrika (IF 1.641) Pub Date : 2018-04-13
    Jian Kang,Hyokyoung G Hong,Y I Li

    Traditional variable selection methods are compromised by overlooking useful information on covariates with similar functionality or spatial proximity, and by treating each covariate independently. Leveraging prior grouping information on covariates, we propose partition-based screening methods for ultrahigh-dimensional variables in the framework of generalized linear models. We show that partition-based

    更新日期:2019-11-01
  • On falsification of the binary instrumental variable model.
    Biometrika (IF 1.641) Pub Date : 2018-03-06
    Linbo Wang,James M Robins,Thomas S Richardson

    Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical

    更新日期:2019-11-01
  • Optimal pseudolikelihood estimation in the analysis of multivariate missing data with nonignorable nonresponse.
    Biometrika (IF 1.641) Pub Date : 2018-02-28
    Jiwei Zhao,Yanyuan Ma

    Tang et al. (2003) considered a regression model with missing response, where the missingness mechanism depends on the value of the response variable and hence is nonignorable. They proposed three pseudolikelihood estimators, based on different treatments of the probability distribution of the completely observed covariates. The first assumes the distribution of the covariate to be known, the second

    更新日期:2019-11-01
  • Optimal designs for active controlled dose-finding trials with efficacy-toxicity outcomes.
    Biometrika (IF 1.641) Pub Date : 2018-02-13
    K Schorning,H Dette,K Kettelhake,W K Wong,F Bretz

    We derive optimal designs to estimate efficacy and toxicity in active controlled dose-finding trials when the bivariate continuous outcomes are described using nonlinear regression models. We determine upper bounds on the required number of different doses and provide conditions under which the boundary points of the design space are included in the optimal design. We provide an analytical description

    更新日期:2019-11-01
  • On two-stage estimation of structural instrumental variable models.
    Biometrika (IF 1.641) Pub Date : 2018-02-13
    Byeong Yeob Choi,Jason P Fine,M Alan Brookhart

    Two-stage least squares estimation is popular for structural equation models with unmeasured confounders. In such models, both the outcome and the exposure are assumed to follow linear models conditional on the measured confounders and instrumental variable, which is related to the outcome only via its relation with the exposure. We consider data where both the outcome and the exposure may be incompletely

    更新日期:2019-11-01
  • Doubly robust nonparametric inference on the average treatment effect.
    Biometrika (IF 1.641) Pub Date : 2018-02-13
    D Benkeser,M Carone,M J Van Der Laan,P B Gilbert

    Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness does not readily extend to inference. We present a general theoretical

    更新日期:2019-11-01
  • Projection correlation between two random vectors.
    Biometrika (IF 1.641) Pub Date : 2018-02-13
    Liping Zhu,Kai Xu,Runze Li,Wei Zhong

    We propose the use of projection correlation to characterize dependence between two random vectors. Projection correlation has several appealing properties. It equals zero if and only if the two random vectors are independent, it is not sensitive to the dimensions of the two random vectors, it is invariant with respect to the group of orthogonal transformations, and its estimation is free of tuning

    更新日期:2019-11-01
  • Distribution-free tests of independence in high dimensions.
    Biometrika (IF 1.641) Pub Date : 2018-02-13
    Fang Han,Shizhe Chen,Han Liu

    We consider the testing of mutual independence among all entries in a [Formula: see text]-dimensional random vector based on [Formula: see text] independent observations. We study two families of distribution-free test statistics, which include Kendall's tau and Spearman's rho as important examples. We show that under the null hypothesis the test statistics of these two families converge weakly to

    更新日期:2019-11-01
  • Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies.
    Biometrika (IF 1.641) Pub Date : 2018-02-13
    Odile Stalder,Alex Asher,Liang Liang,Raymond J Carroll,Yanyuan Ma,Nilanjan Chatterjee

    Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective

    更新日期:2019-11-01
  • Expandable factor analysis.
    Biometrika (IF 1.641) Pub Date : 2018-02-13
    Sanvesh Srivastava,Barbara E Engelhardt,David B Dunson

    Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problematic. We propose expandable factor analysis for scalable inference in factor models when the number of factors is unknown. The method relies on a continuous shrinkage prior for efficient maximum a posteriori estimation of a

    更新日期:2019-11-01
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