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A Minimum Aberration Type Criterion for Selecting Space-Filling Designs Biometrika (IF 1.632) Pub Date : 2021-03-30 Ye Tian, Hongquan Xu
Space-filling designs are widely used in computer experiments. Inspired by the stratified orthogonality of strong orthogonal arrays, we propose a minimum aberration type criterion for assessing the space-filling properties of designs based on design stratification properties on various grids. A space-filling hierarchy principle is proposed as a basic assumption of the criterion. The new criterion provides
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High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis Biometrika (IF 1.632) Pub Date : 2021-03-30 Pixu Shi, Yuchen Zhou, Anru R Zhang
In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical
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Efficient adjustment sets in causal graphical models with hidden variables Biometrika (IF 1.632) Pub Date : 2021-03-17 E Smucler, F Sapienza, A Rotnitzky
We study the selection of adjustment sets for estimating the interventional mean under a point exposure dynamic treatment regime, that is, a treatment rule that depends on the subject’s covariates. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set comprised of observable variables. We provide the definition of a valid adjustment set for
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Identifiability of causal effects with multiple causes and a binary outcome Biometrika (IF 1.632) Pub Date : 2021-03-12 Dehan Kong, Shu Yang, Linbo Wang
Unobserved confounding presents a major threat to causal inference from observational studies. Recently, several authors suggest that this problem may be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric
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Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods Biometrika (IF 1.632) Pub Date : 2021-03-03 Ting Ye, Yanyao Yi, Jun Shao
Covariate-adaptive randomization schemes such as minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theory for inference after covariate-adaptive randomization is mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has
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A Discrete Bouncy Particle Sampler Biometrika (IF 1.632) Pub Date : 2021-02-26 C Sherlock, A H Thiery
Most Markov chain Monte Carlo methods operate in discrete time and are reversible with respect to the target probability. Nevertheless, it is now understood that the use of nonreversible Markov chains can be beneficial in many contexts. In particular, the recently-proposed bouncy particle sampler leverages a continuous-time and nonreversible Markov process and empirically shows state-of-the-art performances
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More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics Biometrika (IF 1.632) Pub Date : 2021-02-23 Lorenzo Masoero, Federico Camerlenghi, Stefano Favaro, Tamara Broderick
While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains non-trivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. Our goal is to find the optimal allocation of resources between quantity
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Statistical inference on shape- and size-indexes for counting processes Biometrika (IF 1.632) Pub Date : 2021-02-12 Yifei Sun, Sy Han Chiou, Kieren A Marr, Chiung-Yu Huang
Single-index models have gained increased popularity in time-to-event analysis owing to their model flexibility and advantage in dimension reduction. In this paper, we propose a semiparametric framework for the rate function of a recurrent event counting process by modelling its size and shape components with single-index models. With additional monotone constraints on the two link functions for the
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Stratification and Optimal Resampling for Sequential Monte Carlo Biometrika (IF 1.632) Pub Date : 2021-02-10 Yichao Li, Wenshuo Wang, Ke Deng, Jun S Liu
Sequential Monte Carlo algorithms have been widely accepted as a powerful computational tool for making inference with dynamical systems. A key step in sequential Monte Carlo is resampling, which plays a role of steering the algorithm towards the future dynamics. Several strategies have been used in practice, including multinomial resampling, residual resampling, optimal resampling, stratified resampling
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Dimension Reduction for Covariates in Network Data Biometrika (IF 1.632) Pub Date : 2021-02-09 Junlong Zhao, Xiumin Liu, Hansheng Wang, Chenlei Leng
A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach named network-supervised dimension reduction by projecting covariates onto low-dimensional spaces for revealing the linkage pattern, without assuming a model.We propose a new loss function for estimating the parameters in the resulting
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Estimation of local treatment effects under the binary instrumental variable model Biometrika (IF 1.632) Pub Date : 2021-02-04 Linbo Wang, Yuexia Zhang, Thomas S Richardson, James M Robins
Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper, we consider estimation of the local average treatment effect under the binary instrumental variable model. We
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Missing at random: a stochastic process perspective Biometrika (IF 1.632) Pub Date : 2021-02-04 D Farewell, R Daniel, S Seaman
We offer a natural and extensible measure-theoretic treatment of missingness at random. Within the standard missing data framework, we give a novel characterization of the observed data as a stopping-set sigma algebra. We demonstrate that the usual missingness at random conditions are equivalent to requiring particular stochastic processes to be adapted to a set-indexed filtration. These measurability
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Distributed Inference for Extreme Value Index Biometrika (IF 1.632) Pub Date : 2021-01-30 Liujun Chen, Deyuan Li, Chen Zhou
This paper investigates a divide-and-conquer algorithm for estimating the extreme value index when data are stored in multiple machines. The oracle property of such an algorithm based on extreme value methods is not guaranteed by the general theory of distributed inference. We propose a distributed Hill estimator and establish its asymptotic theories. We consider various cases where the number of observations
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Large-scale model selection in misspecified generalized linear models Biometrika (IF 1.632) Pub Date : 2021-01-30 Emre Demirkaya, Yang Feng, Pallavi Basu, Jinchi Lv
Model selection is crucial both to high-dimensional learning and to inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work assumes implicitly that the models are correctly specified or have fixed dimensionality, yet both are prevalent in practice. In this paper, we exploit the framework of model
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Regression adjustment in completely randomized experiments with a diverging number of covariates Biometrika (IF 1.632) Pub Date : 2020-12-19 Lihua Lei, Peng Ding
Randomized experiments have become important tools in empirical research. In a completely randomized treatment-control experiment, the simple difference in means of the outcome is unbiased for the average treatment effect, and covariate adjustment can further improve the efficiency without assuming a correctly specified outcome model. In modern applications, experimenters often have access to many
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Heterogeneous individual risk modelling of recurrent events Biometrika (IF 1.632) Pub Date : 2020-11-19 Ma H, Peng L, Huang C, et al.
SummaryProgression of chronic disease is often manifested by repeated occurrences of disease-related events over time. Delineating the heterogeneity in the risk of such recurrent events can provide valuable scientific insight for guiding customized disease management. We propose a new sensible measure of individual risk of recurrent events and present a dynamic modelling framework thereof, which accounts
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High-dimensional empirical likelihood inference Biometrika (IF 1.632) Pub Date : 2020-10-22 Jinyuan Chang, Song Xi Chen, Cheng Yong Tang, Tong Tong Wu
High-dimensional statistical inference with general estimating equations is challenging and remains little explored. We study two problems in the area: confidence set estimation for multiple components of the model parameters, and model specifications tests. First, we propose to construct a new set of estimating equations such that the impact from estimating the high-dimensional nuisance parameters
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In search of lost mixing time: adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p Biometrika (IF 1.632) Pub Date : 2020-10-05 Griffin J, Łatuszyński K, Steel M.
SummaryThe availability of datasets with large numbers of variables is rapidly increasing. The effective application of Bayesian variable selection methods for regression with these datasets has proved difficult since available Markov chain Monte Carlo methods do not perform well in typical problem sizes of interest. We propose new adaptive Markov chain Monte Carlo algorithms to address this shortcoming
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An Assumption-Free Exact Test For Fixed-Design Linear Models With Exchangeable Errors Biometrika (IF 1.632) Pub Date : 2020-09-30 Lihua Lei, Peter J Bickel
We propose the cyclic permutation test to test general linear hypotheses for linear models. This test is nonrandomized and valid in finite samples with exact Type-I error α for an arbitrary fixed design matrix and arbitrary exchangeable errors, whenever 1 / α is an integer and n / p ≥ 1 / α – 1. The test applies the marginal rank test on 1 / α linear statistics of the outcome vector where the coefficient
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Assessing cure status prediction from survival data using receiver operating characteristic curves Biometrika (IF 1.632) Pub Date : 2020-09-30 M Amico, I Van Keilegom, B Han
Survival analysis relies on the hypothesis that, if the follow-up will be long enough, the event of interest will eventually be observed for all observations. This assumption, however, is often not realistic. The survival data then contain a cure fraction. A common approach to model and analyse this type of data consists in using cure models. Two types of information can therefore be obtained: the
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The uniform general signed rank test and its design sensitivity Biometrika (IF 1.632) Pub Date : 2020-09-24 S R Howard, S D Pimentel
A sensitivity analysis in an observational study tests whether the qualitative conclusions of an analysis would change if we were to allow for the possibility of limited bias due to confounding. The design sensitivity of a hypothesis test quantifies the asymptotic performance of the test in a sensitivity analysis against a particular alternative. We propose a new, nonasymptotic, distribution-free test
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Quasi-Oracle Estimation of Heterogeneous Treatment Effects Biometrika (IF 1.632) Pub Date : 2020-09-24 X Nie, S Wager
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical applications, such as personalized medicine and optimal resource allocation. In this article we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. First, we estimate marginal effects and treatment propensities to form an objective function
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On the Phase Transition of Wilk’s Phenomenon Biometrika (IF 1.632) Pub Date : 2020-09-24 Yinqiu He, Bo Meng, Zhenghao Zeng, Gongjun Xu
Wilk’s theorem, which offers universal chi-squared approximations for likelihood ratio tests, is widely used in many scientific hypothesis testing problems. For modern datasets with increasing dimension, researchers have found that the conventional Wilk’s phenomenon of the likelihood ratio test statistic often fails. Although new approximations have been proposed in high dimensional settings, there
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Modelling temporal biomarkers with semiparametric nonlinear dynamical systems Biometrika (IF 1.632) Pub Date : 2020-09-24 Ming Sun, Donglin Zeng, Yuanjia Wang
Dynamical systems based on differential equations are useful for modelling the temporal evolution of biomarkers. Such systems can characterize the temporal patterns of biomarkers and inform the detection of interactions between biomarkers. Existing statistical methods for dynamical systems deal mostly with single time-course data based on a linear model or generalized additive model. Hence, they cannot
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Event history analysis of dynamic networks Biometrika (IF 1.632) Pub Date : 2020-09-24 T Sit, Z Ying, Y Yu
Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key interests is to model the event history of time-stamped interactions among nodes. We model dynamic directed networks via multivariate counting processes. A pseudo partial likelihood approach is exploited to capture the network dependence structure. Asymptotic
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Jump or kink: on super-efficiency in segmented linear regression breakpoint estimation Biometrika (IF 1.632) Pub Date : 2020-09-19 Yining Chen
We consider the problem of segmented linear regression with a single breakpoint, with the focus on estimating the location of the breakpoint. If |$n$| is the sample size, we show that the global minimax convergence rate for this problem in terms of the mean absolute error is |$O(n^{-1/3})$|. On the other hand, we demonstrate the construction of a super-efficient estimator that achieves the pointwise
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Posterior contraction in sparse generalized linear models Biometrika (IF 1.632) Pub Date : 2020-09-14 Seonghyun Jeong, Subhashis Ghosal
We study posterior contraction rates in sparse high-dimensional generalized linear models using priors incorporating sparsity. A mixture of a point mass at zero and a continuous distribution is used as the prior distribution on regression coefficients. In addition to the usual posterior, the fractional posterior, which is obtained by applying the Bayes theorem on a fractional power of the likelihood
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Block bootstrap optimality and empirical block selection for sample quantiles with dependent data Biometrika (IF 1.632) Pub Date : 2020-09-14 T A Kuffner, S M S Lee, G A Young
We establish a general theory of optimality for block bootstrap distribution estimation for sample quantiles under mild strong mixing conditions. In contrast to existing results, we study the block bootstrap for varying numbers of blocks. This corresponds to a hybrid between the subsampling bootstrap and the moving block bootstrap, in which the number of blocks is between 1 and the ratio of sample
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Specification tests for covariance structures in high-dimensional statistical models Biometrika (IF 1.632) Pub Date : 2020-09-10 X Guo, C Y Tang
We consider testing the covariance structure in statistical models. We focus on developing such tests when the random vectors of interest are not directly observable and have to be derived via estimated models. Additionally, the covariance specification may involve extra nuisance parameters which also need to be estimated. In a generic additive model setting, we develop and investigate test statistics
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Inference on treatment effect parameters in potentially misspecified high-dimensional models Biometrika (IF 1.632) Pub Date : 2020-09-08 Oliver Dukes, Stijn Vansteelandt
Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators such as the Lasso, or other regularisation approaches. Naϊve use of such estimators yields confidence intervals for the conditional treatment effect parameter that
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Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity Biometrika (IF 1.632) Pub Date : 2020-09-04 S Na, M Kolar, O Koyejo
Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific investigation. Motivated by modern applications, this manuscript considers an extended setting where each group is generated by a latent variable Gaussian graphical model. Due to the existence of latent factors, the differential
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Estimating time-varying causal excursion effect in mobile health with binary outcomes Biometrika (IF 1.632) Pub Date : 2020-09-04 Tianchen Qian, Hyesun Yoo, Predrag Klasnja, Daniel Almirall, Susan Murphy
Advances in wearables and digital technology now make it possible to deliver behavioral mobile health interventions to individuals in their everyday life. The micro-randomized trial is increasingly used to provide data to inform the construction of these interventions. In a micro-randomized trial, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands
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On the use of penalized quasilikelihood information criterion for generalized linear mixed models Biometrika (IF 1.632) Pub Date : 2020-08-31 Francis K C Hui
Information criteria are a common approach for joint fixed and random effects selection in mixed models. While straightforward to implement, a major difficultly when applying information criteria is that they are typically based on maximum likelihood estimates, yet calculating such estimates for one, let alone multiple, candidate mixed models presents a major computational hurdle. To overcome this
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Characterization of parameters with a mixed bias property Biometrika (IF 1.632) Pub Date : 2020-08-31 A Rotnitzky, E Smucler, J M Robins
We study a class of parameters with the so-called mixed bias property. For parameters with this property, the bias of the semiparametric efficient one-step estimator is equal to the mean of the product of the estimation errors of two nuisance functions. In nonparametric models, parameters with the mixed bias property admit so-called rate doubly robust estimators, i.e., estimators that are consistent
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Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation Biometrika (IF 1.632) Pub Date : 2020-08-26 W Van Den Boom, G Reeves, D B Dunson
Posterior computation for high-dimensional data with many parameters can be challenging. This article focuses on a new method for approximating posterior distributions of a low- to moderate-dimensional parameter in the presence of a high-dimensional or otherwise computationally challenging nuisance parameter. The focus is on regression models and the key idea is to separate the likelihood into two
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On quadratic forms in multivariate generalized hyperbolic random vectors Biometrika (IF 1.632) Pub Date : 2020-08-26 Simon A Broda, Juan Arismendi Zambrano
Exact and approximate expressions for tail probabilities and partial moments of quadratic forms in multivariate generalized hyperbolic random vectors are obtained. The derivations involve a generalization of the classic inversion formula of Gil-Pelaez (1951). Two numerical applications are considered: the distribution of the two stage least squares estimator, and the expected shortfall of a quadratic
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An asymptotic and empirical smoothing parameters selection method for smoothing spline ANOVA models in large samples Biometrika (IF 1.632) Pub Date : 2020-08-27 Xiaoxiao Sun, Wenxuan Zhong, Ping Ma
Large samples are generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyse such large samples because of high computational costs. In particular, the daunting computational cost of selecting smoothing parameters renders smoothing spline ANOVA models impractical. In this article, we develop an asympirical, i.e., asymptotic
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Finite time analysis of vector autoregressive models under linear restrictions Biometrika (IF 1.632) Pub Date : 2020-08-21 Yao Zheng, Guang Cheng
This paper develops a unified finite-time theory for the ordinary least squares estimation of possibly unstable and even slightly explosive vector autoregressive models under linear restrictions, with the applicable region ρ(A) ≤ 1 + c/n, where ρ(A) is the spectral radius of the transition matrix A in the Var(1) representation, n is the time horizon and c > 0 is a universal constant. The linear restriction
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A nonparametric approach to high-dimensional k-sample comparison problems Biometrika (IF 1.632) Pub Date : 2020-06-11 Subhadeep Mukhopadhyay, Kaijun Wang
High-dimensional |$k$|-sample comparison is a common task in applications. We construct a class of easy-to-implement distribution-free tests based on new nonparametric tools and unexplored connections with spectral graph theory. The test is shown to have various desirable properties and a characteristic exploratory flavour that has practical consequences for statistical modelling. Numerical examples
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Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models Biometrika (IF 1.632) Pub Date : 2020-08-04 Kosmidis I, Firth D.
SummaryPenalization of the likelihood by Jeffreys’ invariant prior, or a positive power thereof, is shown to produce finite-valued maximum penalized likelihood estimates in a broad class of binomial generalized linear models. The class of models includes logistic regression, where the Jeffreys-prior penalty is known additionally to reduce the asymptotic bias of the maximum likelihood estimator, and
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Testing for measurement error in survey data analysis using paradata Biometrika (IF 1.632) Pub Date : 2020-08-04 D N Da Silva, C J Skinner
Paradata refers to survey variables which are not of direct interest themselves, but are related to the quality of data on survey variables which are of interest. We focus on a categorical paradata variable, which reflects the presence of measurement error in a variable of interest. We propose a quasi-score test of the hypothesis of no measurement error bias in the estimation of regression coefficients
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A general interactive framework for false discovery rate control under structural constraints Biometrika (IF 1.632) Pub Date : 2020-07-31 Lihua Lei, Aaditya Ramdas, William Fithian
We propose a general framework based on selectively traversed accumulation rules for interactive multiple testing with generic structural constraints on the rejection set. It combines accumulation tests from ordered multiple testing with data-carving ideas from post-selection inference, allowing for highly flexible adaptation to generic structural information. Our procedure defines an interactive protocol
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Poisson reduced-rank models with an application to political text data Biometrika (IF 1.632) Pub Date : 2020-07-31 Carsten Jentsch, Eun Ryung Lee, Enno Mammen
We discuss Poisson reduced-rank models for low-dimensional summaries of high-dimensional Poisson vectors that allow for inference on the location of individuals in a low-dimensional space. We show that under weak dependence conditions, which allow for certain correlations between the Poisson random variables, the locations can be consistently estimated using Poisson maximum likelihood estimation. Moreover
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Statistical properties of sketching algorithms Biometrika (IF 1.632) Pub Date : 2020-07-30 D. C Ahfock, W. J Astle, S Richardson
Sketching is a probabilistic data compression technique that has been largely developed in the computer science community. Numerical operations on big datasets can be intolerably slow; sketching algorithms address this issue by generating a smaller surrogate dataset. Typically, inference proceeds on the compressed dataset. Sketching algorithms generally use random projections to compress the original
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A likelihood analysis of quantile-matching transformations Biometrika (IF 1.632) Pub Date : 2020-07-30 P McCullagh, M F Tresoldi
Quantile matching is a strictly monotone transformation that sends the observed response values to the quantiles of a given target distribution. A profile likelihood-based criterion is developed for comparing one target distribution with another in a linear-model setting.
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High-quantile regression for tail-dependent time series Biometrika (IF 1.632) Pub Date : 2020-07-27 Ting Zhang
Quantile regression is a popular and powerful method for studying the effect of regressors on quantiles of a response distribution. However, existing results on quantile regression were mainly developed for cases in which the quantile level is fixed, and the data are often assumed to be independent. Motivated by recent applications, we consider the situation where (i) the quantile level is not fixed
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On testing marginal versus conditional independence Biometrika (IF 1.632) Pub Date : 2020-07-22 F Richard Guo, Thomas S Richardson
We consider testing marginal independence versus conditional independence in a trivariate Gaussian setting. The two models are nonnested, and their intersection is a union of two marginal independences. We consider two sequences of such models, one from each type of independence, that are closest to each other in the Kullback–Leibler sense as they approach the intersection. They become indistinguishable
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‘Unbiased Hamiltonian Monte Carlo with couplings’ Biometrika (IF 1.632) Pub Date : 2020-07-23 Heng J, Jacob P.
Biometrika (2019) 106, pp. 287–302.
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Functional regression on the manifold with contamination Biometrika (IF 1.632) Pub Date : 2020-07-21 Zhenhua Lin, Fang Yao
We propose a new method for functional nonparametric regression with a predictor that resides on a finite-dimensional manifold, but is observable only in an infinite-dimensional space. Contamination of the predictor due to discrete or noisy measurements is also accounted for. By using functional local linear manifold smoothing, the proposed estimator enjoys a polynomial rate of convergence that adapts
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Optimal subsampling for quantile regression in big data Biometrika (IF 1.632) Pub Date : 2020-07-21 Haiying Wang, Yanyuan Ma
We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsampling probabilities. One version minimizes the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator and the other minimizes that of the original parameter estimator. The former
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Classification via local manifold approximation Biometrika (IF 1.632) Pub Date : 2020-07-14 Didong Li, David B Dunson
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
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Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection Biometrika (IF 1.632) Pub Date : 2020-07-13 Qifan Song, Yan Sun, Mao Ye, Faming Liang
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
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Hypothesis testing for phylogenetic composition: a minimum-cost flow perspective Biometrika (IF 1.632) Pub Date : 2020-07-11 Wang S, Cai T, Li H.
SummaryQuantitative comparison of microbial composition from different populations is a fundamental task in various microbiome studies. We consider two-sample testing for microbial compositional data by leveraging phylogenetic information. Motivated by existing phylogenetic distances, we take a minimum-cost flow perspective to study such testing problems. We first show that multivariate analysis of
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Large-sample asymptotics of the pseudo-marginal method Biometrika (IF 1.632) Pub Date : 2020-07-11 S M Schmon, G Deligiannidis, A Doucet, M K Pitt
The pseudo-marginal algorithm is a variant of the Metropolis–Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate unbiasedly an unnormalized version of its density. Practically, one has to trade off the computational resources used to obtain this estimator against the asymptotic variances of the ergodic averages obtained by the pseudo-marginal
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Nonsmooth backfitting for the excess risk additive regression model with two survival time-scales Biometrika (IF 1.632) Pub Date : 2020-07-08 M Hiabu, J P Nielsen, T H Scheike
We consider an extension of Aalen’s additive regression model allowing covariates to have effects that vary on two different time-scales. The two time-scales considered are equal up to a constant that varies for each individual, such as follow-up time and age in medical studies or calendar time and age in longitudinal studies. The model was introduced in Scheike (2001) where it was solved via smoothing
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The asymptotic distribution of modularity in weighted signed networks Biometrika (IF 1.632) Pub Date : 2020-07-08 Rong Ma, Ian Barnett
Modularity is a popular metric for quantifying the degree of community structure within a network. The distribution of the largest eigenvalue of a network’s edge weight or adjacency matrix is well studied and is frequently used as a substitute for modularity when performing statistical inference. However, we show that the largest eigenvalue and modularity are asymptotically uncorrelated, which suggests
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Optimal Bayesian estimation for random dot product graphs Biometrika (IF 1.632) Pub Date : 2020-07-06 Fangzheng Xie, Yanxun Xu
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
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Lattice-based designs possessing quasi-optimal separation distance on all projections Biometrika (IF 1.632) Pub Date : 2020-07-03 Xu He
Experimental designs that spread out points apart from each other on projections are important for computer experiments when not necessarily all factors have substantial influence on the response. We provide a theoretical framework to generate designs that possess quasi-optimal separation distance on all of the projections and quasi-optimal fill distance on univariate margins. The key is to use special
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Matrix-variate logistic regression with measurement error Biometrika (IF 1.632) Pub Date : 2020-07-03 Junhan Fang, Grace Y Yi
Measurement error in covariates has been extensively studied in many conventional regression settings where covariate information is typically expressed in a vector form. However, there has been little work on error-prone matrix-variate data, which commonly arise from studies with imaging, spatial-temporal structures, etc. We consider analysis of error-contaminated matrix-variate data. We particularly
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Envelopes in multivariate regression models with nonlinearity and heteroscedasticity Biometrika (IF 1.632) Pub Date : 2020-06-17 X Zhang, C E Lee, X Shao
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 Cook et al. (2010) to incorporate a nonlinear conditional mean function and a heteroscedastic error. Given any two random vectors |${X}\in\mathbb{R}^{p}$| and |${Y}\in\mathbb{R}^{r}$|
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