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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

  • 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. In this paper, we consider analysis of matrix-variate data which are error-contaminated

  • 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 |${Y}\in\mathbb{R}^{r}$|⁠

  • General regression model for the subdistribution of a competing risk under left-truncation and right-censoring
    Biometrika (IF 1.632) Pub Date : 2020-06-17
    A Bellach; M R Kosorok; P B Gilbert; J P Fine

    Left-truncation poses extra challenges for the analysis of complex time-to-event data. We propose a general semiparametric regression model for left-truncated and right-censored competing risks data that is based on a novel weighted conditional likelihood function. Targeting the subdistribution hazard, our parameter estimates are directly interpretable with regard to the cumulative incidence function

  • Efficient posterior sampling for high-dimensional imbalanced logistic regression
    Biometrika (IF 1.632) Pub Date : 2020-06-17
    Deborshee Sen; Matthias Sachs; Jianfeng Lu; David B Dunson

    Classification with high-dimensional data is of widespread interest and often involves dealing with imbalanced data. Bayesian classification approaches are hampered by the fact that current Markov chain Monte Carlo algorithms for posterior computation become inefficient as the number |$p$| of predictors or the number |$n$| of subjects to classify gets large, because of the increasing computational

  • Regression-adjusted average treatment effect estimates in stratified randomized experiments
    Biometrika (IF 1.632) Pub Date : 2020-06-14
    Hanzhong Liu; Yuehan Yang

    Linear regression is often used in the analysis of randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and control groups. This article proposes a randomization-based inference framework for regression adjustment in stratified randomized experiments. We re-establish, under mild conditions, the finite-population central limit theorem

  • A unified approach to the calculation of information operators in semiparametric models
    Biometrika (IF 1.632) Pub Date : 2020-06-14
    Lu Mao

    The infinite-dimensional information operator for the nuisance parameter plays a key role in semiparametric inference, as it is closely related to the regular estimability of the target parameter. Calculation of information operators has traditionally proceeded in a case-by-case manner and has often entailed lengthy derivations with complicated arguments. We develop a unified framework for this task

  • On specification tests for composite likelihood inference
    Biometrika (IF 1.632) Pub Date : 2020-06-14
    Jing Huang; Yang Ning; Nancy Reid; Yong Chen

    Composite likelihood functions are often used for inference in applications where the data have a complex structure. While inference based on the composite likelihood can be more robust than inference based on the full likelihood, the inference is not valid if the associated conditional or marginal models are misspecified. In this paper, we propose a general class of specification tests for composite

  • A conditional test with demonstrated insensitivity to unmeasured bias in matched observational studies
    Biometrika (IF 1.632) Pub Date : 2020-06-14
    P R Rosenbaum

    In an observational study matched for observed covariates, an association between treatment received and outcome exhibited may indicate not an effect caused by the treatment, but merely some bias in the allocation of treatments to individuals within matched pairs. The evidence that distinguishes moderate biases from causal effects is unevenly dispersed among possible comparisons in an observational

  • A nonparametric approach to high-dimensional k-sample comparison problems
    Biometrika (IF 1.632) Pub Date : 2020-06-11
    Subhadeep Mukhopadhyay

    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

  • Combining p-values via averaging
    Biometrika (IF 1.632) Pub Date : 2020-06-11
    Vladimir Vovk; Ruodu Wang

    This paper proposes general methods for the problem of multiple testing of a single hypothesis, with a standard goal of combining a number of |$p$|-values without making any assumptions about their dependence structure. A result by Rüschendorf (1982) and, independently, Meng (1993) implies that the |$p$|-values can be combined by scaling up their arithmetic mean by a factor of 2, and no smaller factor

  • The Pitman–Yor multinomial process for mixture modelling
    Biometrika (IF 1.632) Pub Date : 2020-06-05
    Antonio Lijoi; Igor Prünster; Tommaso Rigon

    Discrete nonparametric priors play a central role in a variety of Bayesian procedures, most notably when used to model latent features, such as in clustering, mixtures and curve fitting. They are effective and well-developed tools, though their infinite dimensionality is unsuited to some applications. If one restricts to a finite-dimensional simplex, very little is known beyond the traditional Dirichlet

  • Multivariate one-sided testing in matched observational studies as an adversarial game
    Biometrika (IF 1.632) Pub Date : 2020-06-03
    P L Cohen; M A Olson; C B Fogarty

    We present a multivariate one-sided sensitivity analysis for matched observational studies, appropriate when the researcher has specified that a given causal mechanism should manifest itself in effects on multiple outcome variables in a known direction. The test statistic can be thought of as the solution to an adversarial game, where the researcher determines the best linear combination of test statistics

  • Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
    Biometrika (IF 1.632) Pub Date : 2020-06-03
    Yang Ning; Peng Sida; Kosuke Imai

    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

  • Bayesian cumulative shrinkage for infinite factorizations
    Biometrika (IF 1.632) Pub Date : 2020-05-27
    Sirio Legramanti; Daniele Durante; David B Dunson

    The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing

  • Demystifying a class of multiply robust estimators
    Biometrika (IF 1.632) Pub Date : 2020-05-25
    Wei Li; Yuwen Gu; Lan Liu

    For estimating the population mean of a response variable subject to ignorable missingness, a new class of methods, called multiply robust procedures, has been proposed. The advantage of multiply robust procedures over the traditional doubly robust methods is that they permit the use of multiple candidate models for both the propensity score and the outcome regression, and they are consistent if any

  • Estimation in linear errors-in-variables models with unknown error distribution
    Biometrika (IF 1.632) Pub Date : 2020-05-21
    Linh H Nghiem; Michael C Byrd; Cornelis J Potgieter

    Parameter estimation in linear errors-in-variables models typically requires that the measurement error distribution be known or estimable from replicate data. A generalized method of moments approach can be used to estimate model parameters in the absence of knowledge of the error distributions, but it requires the existence of a large number of model moments. In this paper, parameter estimation based

  • Inference under unequal probability sampling with the Bayesian exponentially tilted empirical likelihood
    Biometrika (IF 1.632) Pub Date : 2020-05-21
    A Yiu; R J B Goudie; B D M Tom

    Fully Bayesian inference in the presence of unequal probability sampling requires stronger structural assumptions on the data-generating distribution than frequentist semiparametric methods, but offers the potential for improved small-sample inference and convenient evidence synthesis. We demonstrate that the Bayesian exponentially tilted empirical likelihood can be used to combine the practical benefits

  • Discussion of ‘Network cross-validation by edge sampling’
    Biometrika (IF 1.632) Pub Date : 2020-05-15
    Jinyuan Chang; Eric D Kolaczyk; Qiwei Yao

    We thank the authors for their new contribution to network modelling. Data reuse, encompassing methods such as bootstrapping and cross-validation, is an area that to date has largely resisted obvious and rapid development in the network context. One of the major reasons is that mimicking the original sampling mechanisms is challenging if not impossible. To avoid deleting edges and destroying some of

  • Discussion of ‘Network cross-validation by edge sampling’
    Biometrika (IF 1.632) Pub Date : 2020-05-15
    Chao Gao; Zongming Ma

    One of the main novelties of the method is the use of matrix completion to...

  • Discussion of ‘Network cross-validation by edge sampling’
    Biometrika (IF 1.632) Pub Date : 2020-05-15
    J Lei; K Z Lin

    We congratulate the authors for a nice contribution to the literature on model selection and assessment for complex network data. The article of Li et al. (2020) enriches the collection of tools available for network data analysis, bringing together different fields such as low-rank matrix estimation, cross-validation and network modelling. This discussion will focus on the |$V$|-fold variant of the

  • Rejoinder: ‘Network cross-validation by edge sampling’
    Biometrika (IF 1.632) Pub Date : 2020-05-15
    Tianxi Li; Elizaveta Levina; Ji Zhu

    We thank the editor for organizing this discussion and giving us the opportunity to read experts’ perspectives on our work. We are grateful to all the discussants for their insightful contributions, which raise many important points and offer suggestions for potential improvements and generalizations of our method. In this rejoinder we provide some clarifications, remarks and selected numerical results

  • More efficient approximation of smoothing splines via space-filling basis selection
    Biometrika (IF 1.632) Pub Date : 2020-05-07
    Cheng Meng; Xinlian Zhang; Jingyi Zhang; Wenxuan Zhong

    We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size |$n$|⁠, the smoothing spline estimator can be expressed as a linear combination of |$n$| basis functions, requiring |$O(n^3)$| computational time when the number |$d$| of predictors is two or more. Such a sizeable computational cost hinders the broad applicability

  • Determining the dependence structure of multivariate extremes
    Biometrika (IF 1.632) Pub Date : 2020-05-07
    E S Simpson; J L Wadsworth; J A Tawn

    In multivariate extreme value analysis, the nature of the extremal dependence between variables should be considered when selecting appropriate statistical models. Interest often lies in determining which subsets of variables can take their largest values simultaneously while the others are of smaller order. Our approach to this problem exploits hidden regular variation properties on a collection of

  • Bootstrapping M-estimators in generalized autoregressive conditional heteroscedastic models
    Biometrika (IF 1.632) Pub Date : 2020-05-06
    K Mukherjee

    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

  • Adaptive critical value for constrained likelihood ratio testing
    Biometrika (IF 1.632) Pub Date : 2020-05-04
    Diaa Al Mohamad; Erik W Van Zwet; Eric Cator; Jelle J Goeman

    We present a new general method for constrained likelihood ratio testing which, when few constraints are violated, improves upon the existing approach in the literature that compares the likelihood ratio with the quantile of a mixture of chi-squared distributions; the improvement is in terms of both simplicity and power. The proposed method compares the constrained likelihood ratio statistic against

  • Estimation and inference for the indirect effect in high-dimensional linear mediation models
    Biometrika (IF 1.632) Pub Date : 2020-05-04
    Ruixuan Rachel Zhou; Liewei Wang; Sihai Dave Zhao

    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

  • Path weights in concentration graphs
    Biometrika (IF 1.632) Pub Date : 2020-04-22
    Alberto Roverato; Robert Castelo

    A graphical model provides a compact and efficient representation of the association structure in a multivariate distribution by means of a graph. Relevant features of the distribution are represented by vertices, edges and higher-order graphical structures such as cliques or paths. Typically, paths play a central role in these models because they determine the dependence relationships between variables

  • Classification with imperfect training labels
    Biometrika (IF 1.632) Pub Date : 2020-04-22
    Timothy I Cannings; Yingying Fan; Richard J Samworth

    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.

  • Sparse semiparametric canonical correlation analysis for data of mixed types
    Biometrika (IF 1.632) Pub Date : 2020-04-15
    Grace Yoon; Raymond J Carroll; Irina Gaynanova

    Canonical correlation analysis investigates linear relationships between two sets of variables, but it often works poorly on modern datasets because of high dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach to sparse canonical correlation analysis based on the Gaussian copula. The main result of this

  • Generalized integration model for improved statistical inference by leveraging external summary data
    Biometrika (IF 1.632) Pub Date : 2020-04-15
    Han Zhang; Lu Deng; Mark Schiffman; Jing Qin; Kai Yu

    Meta-analysis has become a powerful tool for improving inference by gathering evidence from multiple sources. It pools summary-level data from different studies to improve estimation efficiency with the assumption that all participating studies are analysed under the same statistical model. It is challenging to integrate external summary data calculated from different models with a newly conducted

  • ‘Variance estimation in the particle filter’
    Biometrika (IF 1.632) Pub Date : 2020-01-29
    A Lee; N Whiteley

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

  • Bayesian constraint relaxation.
    Biometrika (IF 1.632) 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

  • Model-free approach to quantifying the proportion of treatment effect explained by a surrogate marker
    Biometrika (IF 1.632) Pub Date : 2019-12-24
    Xuan Wang; Layla Parast; Lu Tian; Tianxi Cai

    In 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

  • The Hastings algorithm at fifty
    Biometrika (IF 1.632) Pub Date : 2019-12-24
    D B Dunson; J E Johndrow

    In 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 sample

  • Consistent community detection in multi-layer network data
    Biometrika (IF 1.632) Pub Date : 2019-12-22
    Jing Lei; Kehui Chen; Brian Lynch

    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,

  • Multisample estimation of bacterial composition matrices in metagenomics data
    Biometrika (IF 1.632) Pub Date : 2019-12-06
    Yuanpei Cao; Anru Zhang; Hongzhe Li

    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

  • A conditional density estimation partition model using logistic Gaussian processes
    Biometrika (IF 1.632) Pub Date : 2019-12-05
    R D Payne; N Guha; Y Ding; B K Mallick

    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

  • Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data
    Biometrika (IF 1.632) Pub Date : 2019-12-05
    Z Tan

    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

  • On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding
    Biometrika (IF 1.632) Pub Date : 2019-11-23
    C H Miles; I Shpitser; P Kanki; S Meloni; E J Tchetgen Tchetgen

    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

  • Diagnosing missing always at random in multivariate data
    Biometrika (IF 1.632) Pub Date : 2019-11-23
    Iavor I Bojinov; Natesh S Pillai; Donald B Rubin

    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

  • Bayesian sparse multiple regression for simultaneous rank reduction and variable selection
    Biometrika (IF 1.632) Pub Date : 2019-11-23
    Antik Chakraborty; Anirban Bhattacharya; Bani K Mallick

    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

  • Measurement errors in the binary instrumental variable model
    Biometrika (IF 1.632) Pub Date : 2019-11-21
    Zhichao Jiang; Peng Ding

    Instrumental 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 of the

  • Scalable inference for crossed random effects models
    Biometrika (IF 1.632) Pub Date : 2019-11-15
    O Papaspiliopoulos; G O Roberts; G Zanella

    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

  • Semiparametric estimation of structural failure time models in continuous-time processes
    Biometrika (IF 1.632) Pub Date : 2019-10-29
    S Yang; K Pieper; F Cools

    Structural 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 may invalidate

  • Analysis of grouped data using conjugate generalized linear mixed models
    Biometrika (IF 1.632) Pub Date : 2019-10-25
    Jarod Y L Lee; Peter J Green; Louise M Ryan

    This 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 fact

  • High-dimensional causal discovery under non-Gaussianity
    Biometrika (IF 1.632) Pub Date : 2019-10-25
    Y Samuel Wang; Mathias Drton

    We 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

  • Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations
    Biometrika (IF 1.632) Pub Date : 2019-10-22
    Yixin Wang; Jose R Zubizarreta

    Weighting 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 is

  • Simplified integrated nested Laplace approximation
    Biometrika (IF 1.632) Pub Date : 2019-09-23
    Simon N Wood

    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

  • Ensemble estimation and variable selection with semiparametric regression models.
    Biometrika (IF 1.632) Pub Date : 2020-04-15
    Sunyoung Shin,Yufeng Liu,Stephen R Cole,Jason P Fine

    We consider scenarios in which the likelihood function for a semiparametric regression model factors into separate components, with an efficient estimator of the regression parameter available for each component. An optimal weighted combination of the component estimators, named an ensemble estimator, may be employed as an overall estimate of the regression parameter, and may be fully efficient under

  • Estimation from cross-sectional data under a semiparametric truncation model
    Biometrika (IF 1.632) Pub Date : 2020-04-11
    C Heuchenne; J De Uña-Álvarez; G Laurent

    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

  • Network cross-validation by edge sampling
    Biometrika (IF 1.632) Pub Date : 2020-04-04
    Tianxi Li; Elizaveta Levina; Ji Zhu

    While 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 we propose

  • A robust method for shift detection in time series
    Biometrika (IF 1.632) 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

  • Empirical likelihood test for a large-dimensional mean vector
    Biometrika (IF 1.632) 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

  • Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods
    Biometrika (IF 1.632) Pub Date : 2020-03-07
    Akihiko Nishimura; David B Dunson; Jianfeng Lu

    Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article we present an extension that can efficiently explore target distributions with discontinuous densities. Our extension in particular enables efficient sampling from ordinal parameters through the embedding of probability mass functions into continuous spaces. We motivate our approach through a theory of

  • Doubly functional graphical models in high dimensions
    Biometrika (IF 1.632) Pub Date : 2020-02-11
    Xinghao Qiao; Cheng Qian; Gareth M James; Shaojun Guo

    We consider estimating a functional graphical model from multivariate functional observations. In functional data analysis, the classical assumption is that each function has been measured over a densely sampled grid. However, in practice the functions have often been observed, with measurement error, at a relatively small number of points. We propose a class of doubly functional graphical models to

  • The essential histogram
    Biometrika (IF 1.632) Pub Date : 2020-02-11
    Housen Li; Axel Munk; Hannes Sieling; Guenther Walther

    The histogram is widely used as a simple, exploratory way of displaying data, but it is usually not clear how to choose the number and size of the bins. We construct a confidence set of distribution functions that optimally deal with the two main tasks of the histogram: estimating probabilities and detecting features such as increases and modes in the distribution. We define the essential histogram

  • Lassoing eigenvalues
    Biometrika (IF 1.632) Pub Date : 2020-02-11
    David E Tyler; Mengxi Yi

    The properties of penalized sample covariance matrices depend on the choice of the penalty function. In this paper, we introduce a class of nonsmooth penalty functions for the sample covariance matrix and demonstrate how their use results in a grouping of the estimated eigenvalues. We refer to the proposed method as lassoing eigenvalues, or the elasso.

  • On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
    Biometrika (IF 1.632) Pub Date : 2020-02-03
    Matti Vihola; Jordan Franks

    Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively

  • A random-perturbation-based rank estimator of the number of factors
    Biometrika (IF 1.632) Pub Date : 2020-02-03
    Xinbing Kong

    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

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