562 results match your criteria Biometrika[Journal]


Counting process-based dimension reduction methods for censored outcomes.

Biometrika 2019 Mar 7;106(1):181-196. Epub 2019 Jan 7.

Department of Biostatistics, University of North Carolina at Chapel Hill, 3101 McGavran-Greenberg Hall, Chapel Hill, North Carolina, USA.

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. Read More

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http://dx.doi.org/10.1093/biomet/asy064DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373420PMC

Constrained likelihood for reconstructing a directed acyclic Gaussian graph.

Biometrika 2019 Mar 13;106(1):109-125. Epub 2018 Dec 13.

Department of Industrial and Systems Engineering, University of Minnesota, 111 Church St S.E., Minneapolis, Minnesota, U.S.A.

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 when a local or sequential approach is used to enumerate edge directions by testing or optimizing a criterion locally, as a local method may break down even for moderately sized graphs. Read More

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http://dx.doi.org/10.1093/biomet/asy057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373419PMC

Discussion of 'Gene hunting with hidden Markov model knockoffs'.

Biometrika 2019 Mar 13;106(1):23-26. Epub 2019 Feb 13.

Departments of Statistics and Biostatistics, University of Washington, Seattle, Washington, U.S.A.

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http://dx.doi.org/10.1093/biomet/asy061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373413PMC

Gene hunting with hidden Markov model knockoffs.

Biometrika 2019 Mar 4;106(1):1-18. Epub 2018 Aug 4.

Department of Statistics, Stanford University, 390 Serra Mall, Stanford, California, USA.

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 to problems where the distribution of the covariates can be described by a hidden Markov model. Read More

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http://dx.doi.org/10.1093/biomet/asy033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373422PMC

Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes.

Biometrika 2018 Sep 7;105(3):723-738. Epub 2018 May 7.

Division of Biostatistics, Weill Cornell Medicine, 402 East 67th Street, New York, New York, U.S.A.

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. Read More

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http://dx.doi.org/10.1093/biomet/asy017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374011PMC
September 2018

Optimal pseudolikelihood estimation in the analysis of multivariate missing data with nonignorable nonresponse.

Biometrika 2018 Jun 28;105(2):479-486. Epub 2018 Feb 28.

Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, U.S.A.

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. Read More

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http://dx.doi.org/10.1093/biomet/asy007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373018PMC
June 2018
1 Read

Joint testing and false discovery rate control in high-dimensional multivariate regression.

Biometrika 2018 Jun 16;105(2):249-269. Epub 2018 Feb 16.

Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.

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 and bias-corrected group lasso estimates of the regression coefficients and is shown to have an asymptotic chi-squared null distribution. Read More

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http://dx.doi.org/10.1093/biomet/asx085DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374004PMC

Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process.

Biometrika 2018 Mar 19;105(1):165-184. Epub 2018 Jan 19.

Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, U.S.A.

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. Read More

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http://dx.doi.org/10.1093/biomet/asx075DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345249PMC
March 2018
2 Reads

The Change-Plane Cox Model.

Biometrika 2018 Dec 17;105(4):891-903. Epub 2018 Oct 17.

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.,

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 for principled subgroup discovery. Read More

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http://dx.doi.org/10.1093/biomet/asy050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289527PMC
December 2018
1 Read

Robust estimation of high-dimensional covariance and precision matrices.

Biometrika 2018 Jun 27;105(2):271-284. Epub 2018 Mar 27.

Department of Biostatistics, University of North Carolina at Chapel Hill, 3105D McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, U.S.A.

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 a much richer class of distributions. Read More

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https://academic.oup.com/biomet/article/105/2/271/4955410
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http://dx.doi.org/10.1093/biomet/asy011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188670PMC
June 2018
4 Reads

Sequential rerandomization.

Biometrika 2018 Sep 24;105(3):745-752. Epub 2018 Jun 24.

Department of Statistics, University of Wisconsin-Madison, 1300 University Ave., Madison, Wisconsin 53706, U.S.A.

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. Read More

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http://dx.doi.org/10.1093/biomet/asy031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6109990PMC
September 2018

Theoretical limits of microclustering for record linkage.

Biometrika 2018 Jun 19;105(2):431-446. Epub 2018 Mar 19.

Department of Statistical Science, Duke University, Box 90251, Durham, North Carolina 27708, U.S.A.

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 in idealized cases, accurate entity resolution is effectively impossible unless the number of entities is small relative to the number of records and/or the separation between records from different entities is extremely large. Read More

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http://dx.doi.org/10.1093/biomet/asy003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5963577PMC

Kernel-based covariate functional balancing for observational studies.

Biometrika 2018 Mar 8;105(1):199-213. Epub 2017 Dec 8.

Department of Biostatistics, University of Washington, 1959 NE Pacific St., Seattle, Washington 98195, U.S.A.

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 in terms of an eigenvalue optimization problem. Read More

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http://dx.doi.org/10.1093/biomet/asx069DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5976457PMC

Bayesian block-diagonal variable selection and model averaging.

Biometrika 2017 Jun 24;104(2):343-359. Epub 2017 Apr 24.

Department of Economics and Business, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain.

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 provides a novel and efficient solution to the frequentist best subset selection problem for block-diagonal designs. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975653PMC
June 2017
1 Read

Partial likelihood estimation of isotonic proportional hazards models.

Biometrika 2018 Mar 5;105(1):133-148. Epub 2017 Dec 5.

Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, U.S.A.

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 in large samples. In this paper, partial likelihood estimation is studied. Read More

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http://dx.doi.org/10.1093/biomet/asx064DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5969539PMC
March 2018
1 Read

Partition-based ultrahigh-dimensional variable screening.

Biometrika 2017 Nov 9;104(4):785-800. Epub 2017 Oct 9.

Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48109, U.S.A.

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 screening exhibits the sure screening property with a vanishing false selection rate, and we propose a data-driven partition screening framework with unavailable or unreliable prior knowledge on covariate grouping and investigate its theoretical properties. Read More

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http://dx.doi.org/10.1093/biomet/asx052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5890472PMC
November 2017
6 Reads

On falsification of the binary instrumental variable model.

Biometrika 2017 Mar 23;104(1):229-236. Epub 2017 Jan 23.

Department of Statistics, University of Washington, Box 354322, Washington 98195,

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 tests with known properties. Read More

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http://dx.doi.org/10.1093/biomet/asw064DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819759PMC
March 2017
1 Read

Optimal designs for active controlled dose-finding trials with efficacy-toxicity outcomes.

Biometrika 2017 Dec 9;104(4):1003-1010. Epub 2017 Oct 9.

Statistical Methodology, Novartis Pharma AG, 4002 Basel,

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 of minimally supported optimal designs and show that they do not depend on the correlation between the bivariate outcomes. Read More

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http://dx.doi.org/10.1093/biomet/asx057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793717PMC
December 2017

On two-stage estimation of structural instrumental variable models.

Biometrika 2017 Dec 26;104(4):881-899. Epub 2017 Oct 26.

Department of Epidemiology, University of North Carolina, 2105F McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599,

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 observed, with particular attention to the case where both are censored event times. Read More

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http://dx.doi.org/10.1093/biomet/asx056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793491PMC
December 2017

Doubly robust nonparametric inference on the average treatment effect.

Biometrika 2017 Dec 16;104(4):863-880. Epub 2017 Oct 16.

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, PO Box 19024, Seattle, Washington 98109,

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. Read More

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http://fdslive.oup.com/www.oup.com/pdf/production_in_progres
Publisher Site
http://dx.doi.org/10.1093/biomet/asx053DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793673PMC
December 2017
1 Read

Projection correlation between two random vectors.

Biometrika 2017 Dec 4;104(4):829-843. Epub 2017 Sep 4.

Wang Yanan Institute for Studies in Economics, School of Economics, Xiamen University, Fujian 361005, China

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 parameters and does not require moment conditions on the random vectors. Read More

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http://dx.doi.org/10.1093/biomet/asx043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793497PMC
December 2017
10 Reads

Distribution-free tests of independence in high dimensions.

Biometrika 2017 Dec 3;104(4):813-828. Epub 2017 Oct 3.

Department of Operations Research and Financial Engineering, Princeton University, Sherrerd Hall, Charlton Street, Princeton, New Jersey 08544,

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 Gumbel distributions, and we propose tests that control the Type I error in the high-dimensional setting where [Formula: see text]. Read More

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http://dx.doi.org/10.1093/biomet/asx050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793489PMC
December 2017
5 Reads

Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies.

Biometrika 2017 Dec 15;104(4):801-812. Epub 2017 Sep 15.

Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, Maryland 21205,

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 methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors. We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. Read More

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http://dx.doi.org/10.1093/biomet/asx045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793684PMC
December 2017
1 Read

Expandable factor analysis.

Biometrika 2017 Sep 16;104(3):649-663. Epub 2017 Jun 16.

Department of Statistical Science, Duke University, Box 90251, Durham, North Carolina 27708,

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 low-rank and sparse loadings matrix. Read More

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http://dx.doi.org/10.1093/biomet/asx030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793687PMC
September 2017
2 Reads

Robust reduced-rank regression.

Authors:
Y She K Chen

Biometrika 2017 Sep 12;104(3):633-647. Epub 2017 Jul 12.

Department of Statistics, University of Connecticut, 215 Glenbrook Road U-4120, Storrs, Connecticut 06269,

In high-dimensional multivariate regression problems, enforcing low rank in the coefficient matrix offers effective dimension reduction, which greatly facilitates parameter estimation and model interpretation. However, commonly used reduced-rank methods are sensitive to data corruption, as the low-rank dependence structure between response variables and predictors is easily distorted by outliers. We propose a robust reduced-rank regression approach for joint modelling and outlier detection. Read More

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http://dx.doi.org/10.1093/biomet/asx032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793675PMC
September 2017
1 Read

Identification and estimation of causal effects with outcomes truncated by death.

Biometrika 2017 Sep 11;104(3):597-612. Epub 2017 Jul 11.

Department of Statistics, University of Washington, Seattle, Washington 98195,

It is common in medical studies that the outcome of interest is truncated by death, meaning that a subject has died before the outcome could be measured. In this case, restricted analysis among survivors may be subject to selection bias. Hence, it is of interest to estimate the survivor average causal effect, defined as the average causal effect among the subgroup consisting of subjects who would survive under either exposure. Read More

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http://dx.doi.org/10.1093/biomet/asx034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793679PMC
September 2017

Joint sufficient dimension reduction and estimation of conditional and average treatment effects.

Biometrika 2017 Sep 19;104(3):583-596. Epub 2017 May 19.

Department of Biostatistics, University of Washington, Seattle, Washington 98105,

The estimation of treatment effects based on observational data usually involves multiple confounders, and dimension reduction is often desirable and sometimes inevitable. We first clarify the definition of a central subspace that is relevant for the efficient estimation of average treatment effects. A criterion is then proposed to simultaneously estimate the structural dimension, the basis matrix of the joint central subspace, and the optimal bandwidth for estimating the conditional treatment effects. Read More

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https://academic.oup.com/biomet/article/104/3/583/3836906
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http://dx.doi.org/10.1093/biomet/asx028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793490PMC
September 2017
1 Read

Multiple robustness in factorized likelihood models.

Biometrika 2017 Sep 15;104(3):561-581. Epub 2017 Jun 15.

Department of Epidemiology, Harvard T. H. Chan School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115,

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finite-dimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct. Read More

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http://dx.doi.org/10.1093/biomet/asx027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793686PMC
September 2017

Covariate-assisted spectral clustering.

Biometrika 2017 Jun 19;104(2):361-377. Epub 2017 Mar 19.

Department of Statistics, University of Wisconsin, 1300 University Avenue, Madison, Wisconsin 53706,

Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Read More

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http://dx.doi.org/10.1093/biomet/asx008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793492PMC

An improved and explicit surrogate variable analysis procedure by coefficient adjustment.

Biometrika 2017 Jun 21;104(2):303-316. Epub 2017 Apr 21.

Department of Biostatistics, University of Florida, 2004 Mowry Rd, Gainesville, Florida 32611,

Unobserved environmental, demographic and technical factors canadversely affect the estimation and testing of the effects ofprimary variables. Surrogate variable analysis, proposed to tacklethis problem, has been widely used in genomic studies. To estimatehidden factors that are correlated with the primary variables,surrogate variable analysis performs principal component analysiseither on a subset of features or on all features, but weightingeach differently. Read More

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http://dx.doi.org/10.1093/biomet/asx018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5627626PMC
June 2017
2 Reads

Roy's largest root test under rank-one alternatives.

Biometrika 2017 Mar 13;104(1):181-193. Epub 2017 Jan 13.

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, P.O. Box 26, Rehovot 76100,

Roy's largest root is a common test statistic in multivariate analysis, statistical signal processing and allied fields. Despite its ubiquity, provision of accurate and tractable approximations to its distribution under the alternative has been a longstanding open problem. Assuming Gaussian observations and a rank-one alternative, or concentrated noncentrality, we derive simple yet accurate approximations for the most common low-dimensional settings. Read More

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http://dx.doi.org/10.1093/biomet/asw060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793689PMC

On pseudolikelihood inference for semiparametric models with boundary problems.

Biometrika 2017 Mar 18;104(1):165-179. Epub 2017 Feb 18.

Department of Biostatistics and Epidemiology, University of Pennsylvania, 210 Blockley Hall, 423 Guardian Drive, Philadelphia, Pennsylvania 19104, U.S.A.

Consider a semiparametric model indexed by a Euclidean parameter of interest and an infinite-dimensional nuisance parameter. In many applications, pseudolikelihood provides a convenient way to infer the parameter of interest, where the nuisance parameter is replaced by a consistent estimator. The purpose of this paper is to establish the asymptotic behaviour of the pseudolikelihood ratio statistic under semiparametric models. Read More

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http://dx.doi.org/10.1093/biomet/asw072DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793681PMC

Generalized R-squared for detecting dependence.

Biometrika 2017 Mar 22;104(1):129-139. Epub 2017 Feb 22.

Department of Statistics, Harvard University, One Oxford Street, Cambridge, Massachusetts 02138, U.S.A.

Detecting dependence between two random variables is a fundamental problem. Although the Pearson correlation coefficient is effective for capturing linear dependence, it can be entirely powerless for detecting nonlinear and/or heteroscedastic patterns. We introduce a new measure, G-squared, to test whether two univariate random variables are independent and to measure the strength of their relationship. Read More

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http://dx.doi.org/10.1093/biomet/asw071DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793683PMC

Principal weighted support vector machines for sufficient dimension reduction in binary classification.

Biometrika 2017 Mar 19;104(1):67-81. Epub 2017 Jan 19.

Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, 354 Hanes Hall, Chapel Hill, North Carolina 27599,

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. Read More

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http://dx.doi.org/10.1093/biomet/asw057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793677PMC
March 2017
2 Reads
1.420 Impact Factor

Bayesian Local Extremum Splines.

Biometrika 2017 Dec;104(4):939-952

Department of Statistical Science, Duke University, Box 90251, Durham, NC 27708.

We consider shape restricted nonparametric regression on a closed set [Formula: see text], where it is reasonable to assume the function has no more than local extrema interior to [Formula: see text]. Following a Bayesian approach we develop a nonparametric prior over a novel class of local extremum splines. This approach is shown to be consistent when modeling any continuously differentiable function within the class considered, and is used to develop methods for testing hypotheses on the shape of the curve. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798493PMC
December 2017

Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees.

Biometrika 2017 Dec 27;104(4):901-922. Epub 2017 Sep 27.

Department of Operations Research, Naval Postgraduate School, Monterey, California 93943,

Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi-dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high-dimensional data to a low-dimensional representation that preserves much of the sample's structure. Read More

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http://dx.doi.org/10.1093/biomet/asx047DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793493PMC
December 2017
1 Read

Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data.

Biometrika 2017 Sep 12;104(3):505-525. Epub 2017 Jul 12.

Department of Biostatistics, CB#7420, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.

Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring schemes. We show that the proposed estimators for the finite-dimensional parameters are consistent and asymptotically normal, with a limiting covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. Read More

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http://dx.doi.org/10.1093/biomet/asx029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787874PMC
September 2017
13 Reads

Miscellanea Dependent generalized functional linear models.

Biometrika 2017 Dec 2;104(4):987-994. Epub 2017 Sep 2.

Department of Epidemiology & Biostatistics, Michigan State University, B601West Fee Hall, 909 Fee Road, East Lansing, Michigan 48824, U.S.A.

This paper considers testing for no effect of functional covariates on response variables in multivariate regression. We use generalized estimating equations to determine the underlying parameters and establish their joint asymptotic normality. This is then used to test the significance of the effect of predictors on the vector of response variables. Read More

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http://dx.doi.org/10.1093/biomet/asx044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5771479PMC
December 2017

Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics.

Biometrika 2016 09;103(3):493-511

Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.

Gaussian graphical models are widely used to represent conditional dependence among random variables. In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent. A motivating example is that of modeling gene expression collected on multiple tissues from the same individual: here the multivariate outcome is affected by dependencies acting not only at the level of the specific tissues, but also at the level of the whole body; existing methods that assume independence among graphs are not applicable in this case. Read More

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http://dx.doi.org/10.1093/biomet/asw035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5640885PMC
September 2016
17 Reads

Instrumental variables as bias amplifiers with general outcome and confounding.

Biometrika 2017 Jun 17;104(2):291-302. Epub 2017 Apr 17.

Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.

Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. Read More

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http://dx.doi.org/10.1093/biomet/asx009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5636691PMC

Case-cohort studies with interval-censored failure time data.

Authors:
Q Zhou H Zhou J Cai

Biometrika 2017 Mar 3;104(1):17-29. Epub 2017 Feb 3.

Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.

The case-cohort design has been widely used as a means of cost reduction in assembling or measuring expensive covariates in large cohort studies. The existing literature on the case-cohort design is mainly focused on right-censored data. In practice, however, the failure time is often subject to interval-censoring; it is known only to fall within some random time interval. Read More

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http://dx.doi.org/10.1093/biomet/asw067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608290PMC

An adaptive two-sample test for high-dimensional means.

Biometrika 2016 09 18;103(3):609-624. Epub 2017 Mar 18.

Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, U.S.A. 55455.

Several two-sample tests for high-dimensional data have been proposed recently, but they are powerful only against certain limited alternative hypotheses. In practice, since the true alternative hypothesis is unknown, it is unclear how to choose a powerful test. We propose an adaptive test that maintains high power across a wide range of situations, and study its asymptotic properties. Read More

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http://dx.doi.org/10.1093/biomet/asw029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549874PMC
September 2016
28 Reads

Data integration with high dimensionality.

Biometrika 2017 Jun 9;104(2):251-272. Epub 2017 May 9.

Department of Statistics, 447 Blocker Building, Texas A&M University, College Station, Texas 77843, U.S.A.

We consider situations where the data consist of a number of responses for each individual, which may include a mix of discrete and continuous variables. The data also include a class of predictors, where the same predictor may have different physical measurements across different experiments depending on how the predictor is measured. The goal is to select which predictors affect any of the responses, where the number of such informative predictors tends to infinity as the sample size increases. Read More

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http://dx.doi.org/10.1093/biomet/asx023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5532816PMC
June 2017
2 Reads

Replicates in high dimensions, with applications to latent variable graphical models.

Biometrika 2016 12 8;103(4):761-777. Epub 2016 Dec 8.

Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544, U.S.A.,

In classical statistics, much thought has been put into experimental design and data collection. In the high-dimensional setting, however, experimental design has been less of a focus. In this paper, we stress the importance of collecting multiple replicates for each subject in this setting. Read More

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http://dx.doi.org/10.1093/biomet/asw050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5520622PMC
December 2016
31 Reads

Ignorability for general longitudinal data.

Biometrika 2017 Jun 8;104(2):317-326. Epub 2017 May 8.

Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, 28359 Bremen, Germany.

Likelihood factors that can be disregarded for inference are termed ignorable. We demonstrate that close ties exist between ignorability and identification of causal effects by covariate adjustment. A graphical condition, stability, plays a role analogous to that of missingness at random, but is applicable to general longitudinal data. Read More

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http://dx.doi.org/10.1093/biomet/asx020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5496665PMC

Variable selection for case-cohort studies with failure time outcome.

Biometrika 2016 09 10;103(3):547-562. Epub 2016 Aug 10.

3101 McGavran-Greenberg Hall, CB 7420, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.

Case-cohort designs are widely used in large cohort studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large, so an efficient variable selection method is necessary. In this paper, we study the properties of a variable selection procedure using the smoothly clipped absolute deviation penalty in a case-cohort design with a diverging number of parameters. Read More

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http://dx.doi.org/10.1093/biomet/asw027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436496PMC
September 2016
35 Reads

Fast sampling with Gaussian scale-mixture priors in high-dimensional regression.

Biometrika 2016 12 27;103(4):985-991. Epub 2016 Oct 27.

Department of Statistics, Texas A&M University, College Station, Texas, 77843, USA.

We propose an efficient way to sample from a class of structured multivariate Gaussian distributions. The proposed algorithm only requires matrix multiplications and linear system solutions. Its computational complexity grows linearly with the dimension, unlike existing algorithms that rely on Cholesky factorizations with cubic complexity. Read More

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http://dx.doi.org/10.1093/biomet/asw042DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5400369PMC
December 2016
1 Read

A goodness-of-fit test for structural nested mean models.

Authors:
S Yang J J Lok

Biometrika 2016 09 25;103(3):734-741. Epub 2016 Jul 25.

Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, U.S.A.

Coarse structural nested mean models are tools to estimate treatment effects from longitudinal observational data with time-dependent confounding. There is, however, no guidance on how to specify the treatment effect model, and model misspecification can lead to bias. We derive a goodness-of-fit test based on modified overidentification restrictions tests for evaluating a treatment effect model, and show that our test statistic is doubly-robust in the sense that, with a correct treatment effect model, the test has the correct type-I error if either the treatment initiation model or a nuisance regression outcome model is correctly specified. Read More

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http://dx.doi.org/10.1093/biomet/asw031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5152627PMC
September 2016
1 Read

Bayesian inference on quasi-sparse count data.

Biometrika 2016 12 8;103(4):971-983. Epub 2016 Dec 8.

Department of Statistical Science, Duke University, Durham, North Carolina 27708,

There is growing interest in analysing high-dimensional count data, which often exhibit quasi-sparsity corresponding to an overabundance of zeros and small nonzero counts. Existing methods for analysing multivariate count data via Poisson or negative binomial log-linear hierarchical models with zero-inflation cannot flexibly adapt to quasi-sparse settings. We develop a new class of continuous local-global shrinkage priors tailored to quasi-sparse counts. Read More

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http://dx.doi.org/10.1093/biomet/asw053DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793680PMC
December 2016

On inverse probability-weighted estimators in the presence of interference.

Biometrika 2016 12 8;103(4):829-842. Epub 2016 Dec 8.

Department of Family Medicine, University of North Carolina, CB #7595, Chapel Hill, North Carolina 27599,

We consider inference about the causal effect of a treatment or exposure in the presence of interference, i.e., when one individual's treatment affects the outcome of another individual. Read More

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http://dx.doi.org/10.1093/biomet/asw047DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793685PMC
December 2016