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Similar Journals
 BiometrikaJournal Prestige (SJR): 3.485 Citation Impact (citeScore): 2Number of Followers: 21      Hybrid journal (It can contain Open Access articles) ISSN (Print) 0006-3444 - ISSN (Online) 1464-3510 Published by Oxford University Press  [419 journals]
• Discussion of ‘Event history and topological data analysis’

Authors: Chung M; Ombao H.
Pages: 775 - 778
PubDate: Mon, 15 Nov 2021 00:00:00 GMT
DOI: 10.1093/biomet/asab023
Issue No: Vol. 108, No. 4 (2021)

• Discussion of ‘Event history and topological data analysis’

Authors: Biscio C; Møller J.
Pages: 779 - 783
Abstract: Danish Council for Independent Research – Natural Sciences
PubDate: Mon, 15 Nov 2021 00:00:00 GMT
DOI: 10.1093/biomet/asab032
Issue No: Vol. 108, No. 4 (2021)

• Discussion of ‘Event history and topological data analysis’

Authors: Bubenik P.
Pages: 785 - 788
Abstract: SummaryGarside et al. (2021) use event history methods to analyse topological data. We provide additional background on persistent homology to contrast the hazard estimators used in Garside et al. (2021) with standard approaches in topological data analysis. In particular, Garside et al.’s approach is a local method, which has advantages and disadvantages, whereas homology is global. We also provide more details on persistence landscapes and show how a more complete use of this statistic improves its performance.
PubDate: Mon, 15 Nov 2021 00:00:00 GMT
DOI: 10.1093/biomet/asab022
Issue No: Vol. 108, No. 4 (2021)

• Rejoinder: ‘Event history and topological data analysis’

Authors: Garside K; Gjoka A, Henderson R, et al.
Pages: 789 - 793
PubDate: Mon, 25 Oct 2021 00:00:00 GMT
DOI: 10.1093/biomet/asab040
Issue No: Vol. 108, No. 4 (2021)

• Consistency guarantees for greedy permutation-based causal inference
algorithms

Authors: Solus L; Wang Y, Uhler C.
Pages: 795 - 814
Abstract: SummaryDirected acyclic graphical models are widely used to represent complex causal systems. Since the basic task of learning such a model from data is NP-hard, a standard approach is greedy search over the space of directed acyclic graphs or Markov equivalence classes of directed acyclic graphs. As the space of directed acyclic graphs on $p$ nodes and the associated space of Markov equivalence classes are both much larger than the space of permutations, it is desirable to consider permutation-based greedy searches. Here, we provide the first consistency guarantees, both uniform and high dimensional, of a greedy permutation-based search. This search corresponds to a simplex-like algorithm operating over the edge-graph of a subpolytope of the permutohedron, called a directed acyclic graph associahedron. Every vertex in this polytope is associated with a directed acyclic graph, and hence with a collection of permutations that are consistent with the directed acyclic graph ordering. A walk is performed on the edges of the polytope maximizing the sparsity of the associated directed acyclic graphs. We show via simulated and real data that this permutation search is competitive with current approaches.
PubDate: Mon, 04 Jan 2021 00:00:00 GMT
DOI: 10.1093/biomet/asaa104
Issue No: Vol. 108, No. 4 (2021)

• Estimation of local treatment effects under the binary instrumental
variable model

Authors: Wang L; Zhang Y, Richardson T, et al.
Pages: 881 - 894
Abstract: SummaryInstrumental 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 discuss the challenges of causal estimation with a binary outcome and show that, surprisingly, it can be more difficult than in the case with a continuous outcome. We propose novel modelling and estimation procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness and efficiency. Our approach is illustrated via simulation studies and a real data analysis.
PubDate: Thu, 04 Feb 2021 00:00:00 GMT
DOI: 10.1093/biomet/asab003
Issue No: Vol. 108, No. 4 (2021)

• Event history and topological data analysis

Authors: Garside K; Gjoka A, Henderson R, et al.
Pages: 757 - 773
Abstract: SummaryPersistent homology is used to track the appearance and disappearance of features as we move through a nested sequence of topological spaces. Equating the nested sequence to a filtration and the appearance and disappearance of features to events, we show that simple event history methods can be used for the analysis of topological data. We propose a version of the well-known Nelson–Aalen cumulative hazard estimator for the comparison of topological features of random fields and for testing parametric assumptions. We suggest a Cox proportional hazards approach for the analysis of embedded metric trees. The Nelson–Aalen method is illustrated on globally distributed climate data and on neutral hydrogen distribution in the Milky Way. The Cox method is used to compare vascular patterns in fundus images of the eyes of healthy and diabetic retinopathy patients.
PubDate: Mon, 16 Nov 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa097
Issue No: Vol. 108, No. 4 (2020)

• Regression adjustment in completely randomized experiments with a
diverging number of covariates

Authors: Lei L; Ding P.
Pages: 815 - 828
Abstract: SummaryRandomized experiments have become important tools in empirical research. In a completely randomized treatment-control experiment, the simple difference in means of the outcome is un- biased 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 covariates, motivating the need for a theory of covariate adjustment under the asymptotic regime with a diverging number of covariates. We study the asymptotic properties of covariate adjustment under the potential outcomes model and propose a bias-corrected estimator that is consistent and asymptotically normal under weaker conditions. Our theory is based purely on randomization without imposing any parametric outcome model assumptions. To prove the theoretical results, we develop novel vector and matrix concentration inequalities for sampling without replacement.
PubDate: Sat, 19 Dec 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa103
Issue No: Vol. 108, No. 4 (2020)

• Changepoint inference in the presence of missing covariates for principal
surrogate evaluation in vaccine trials

Authors: Yang T; Huang Y, Fong Y.
Pages: 829 - 843
Abstract: SummaryWe consider the use of threshold-based regression models to evaluate immune response biomarkers as principal surrogate markers of a vaccine’s protective effect. Threshold-based regression models, which allow the relationship between a clinical outcome and a covariate to change dramatically across a threshold value in the covariate, have been studied by various authors under fully observed data. Limited research, however, has examined these models in the presence of missing covariates, such as the counterfactual potential immune responses of a participant in the placebo arm of a standard vaccine trial had they been assigned to the vaccine arm instead. Based on a hinge model for a threshold effect of the principal surrogate on vaccine efficacy, we develop a regression method that consists of two components: (i) an estimated likelihood method for handling missing potential outcomes, and (ii) a penalty imposed on the estimated likelihood to ensure satisfactory finite-sample performance. We develop a method that allows joint estimation of all model parameters, as well as a two-step method that separates estimation of the threshold parameter from the rest of the parameters. Stable iterative algorithms are developed to implement the two methods, and the asymptotic properties of the proposed estimators are established. In simulation studies, the proposed estimators are shown to have satisfactory finite-sample performance. The proposed methods are applied to real data collected from dengue vaccine efficacy trials to predict how vaccine efficacy varies with an individual’s potential immune response if receiving the vaccine.
PubDate: Thu, 10 Dec 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa100
Issue No: Vol. 108, No. 4 (2020)

• A method of constructing maximin distance designs

Authors: Li W; Liu M, Tang B.
Pages: 845 - 855
Abstract: SummaryAn attractive type of space-filling design for computer experiments is the class of maximin distance designs. Algorithmic search is commonly used for finding such designs, but this approach becomes ineffective for large problems. Theoretical construction of maximin distance designs is challenging; some results have been obtained recently, often using highly specialized techniques. This article presents an easy-to-use method for constructing maximin distance designs. The method is versatile as it works with any distance measure. The basic idea is to construct large designs from small designs, and the method is effective because the quality of large designs is guaranteed by that of small designs, as evaluated by the maximin distance criterion.
PubDate: Thu, 29 Oct 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa089
Issue No: Vol. 108, No. 4 (2020)

• Elicitation complexity of statistical properties

Authors: Frongillo R; Kash I.
Pages: 857 - 879
Abstract: SummaryA property, or statistical functional, is said to be elicitable if it minimizes the expected loss for some loss function. The study of which properties are elicitable sheds light on the capabilities and limitations of point estimation and empirical risk minimization. While recent work has sought to identify which properties are elicitable, here we investigate a more nuanced question: how many dimensions are required to indirectly elicit a given property' This number is called the elicitation complexity of the property. We lay the foundation for a general theory of elicitation complexity, which includes several basic results on how elicitation complexity behaves and the complexity of standard properties of interest. Building on this foundation, our main result gives tight complexity bounds for the broad class of Bayes risks. We apply these results to several properties of interest, including variance, entropy, norms and several classes of financial risk measures. The article concludes with a discussion and open questions.
PubDate: Wed, 04 Nov 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa093
Issue No: Vol. 108, No. 4 (2020)

• Bio-equivalence tests in functional data by maximum deviation

Authors: Dette H; Kokot K.
Pages: 895 - 913
Abstract: SummaryWe study the problem of testing equivalence of functional parameters, such as the mean or the variance function, in the two-sample functional data setting. In contrast to previous work where the functional problem is reduced to a multiple testing problem for the equivalence of scalar data by comparing the functions at each point, our approach is based on an estimate of a distance measuring the maximum deviation between the two functional parameters. Equivalence is claimed if the estimate for the maximum deviation does not exceed a given threshold. We propose a bootstrap procedure for obtaining quantiles of the distribution of the test statistic, and we prove consistency of the corresponding test in the large-sample scenario. As the methods proposed here avoid the use of the intersection-union principle, they are less conservative and more powerful than currently available approaches.
PubDate: Fri, 06 Nov 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa096
Issue No: Vol. 108, No. 4 (2020)

• Covariate adaptive familywise error rate control for genome-wide
association studies

Authors: Zhou H; Zhang X, Chen J.
Pages: 915 - 931
Abstract: SummaryThe familywise error rate has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focused on false discovery rate control, while covariate-adaptive procedures controlling the familywise error rate remain underdeveloped. Here, we propose a novel covariate-adaptive procedure to control the familywise error rate that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyse 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.
PubDate: Fri, 27 Nov 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa098
Issue No: Vol. 108, No. 4 (2020)

• Learning block structures in U-statistic-based matrices

Authors: Zhang W; Jin B, Bai Z.
Pages: 933 - 946
Abstract: SummaryWe introduce a conceptually simple, efficient and easily implemented approach for learning the block structure in a large matrix. Using the properties of U-statistics and large-dimensional random matrix theory, the group structure of many variables can be directly identified based on the eigenvalues and eigenvectors of the scaled sample matrix. We also establish the asymptotic properties of the proposed approach under mild conditions. The finite-sample performance of the approach is examined by extensive simulations and data examples.
PubDate: Fri, 27 Nov 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa099
Issue No: Vol. 108, No. 4 (2020)

• Maximum likelihood estimation for semiparametric regression models with
panel count data

Authors: Zeng D; Lin D.
Pages: 947 - 963
Abstract: SummaryPanel count data, in which the observation for each study subject consists of the number of recurrent events between successive examinations, are commonly encountered in industrial reliability testing, medical research and other scientific investigations. We formulate the effects of potentially time-dependent covariates on one or more types of recurrent events through nonhomogeneous Poisson processes with random effects. We employ nonparametric maximum likelihood estimation under arbitrary examination schemes, and develop a simple and stable EM algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that achieves the semiparametric efficiency bound and can be estimated using profile likelihood. We evaluate the performance of the proposed methods through simulation studies and analysis of data from a skin cancer clinical trial.
PubDate: Fri, 06 Nov 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa091
Issue No: Vol. 108, No. 4 (2020)

• On semiparametric modelling, estimation and inference for survival data
subject to dependent censoring

Authors: Deresa N; Van Keilegom I.
Pages: 965 - 979
Abstract: SummaryWhen modelling survival data, it is common to assume that the survival time $T$ is conditionally independent of the censoring time $C$ given a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model which assumes that, after a proper nonparametric monotone transformation, the vector $(T, C)$ follows a linear model, and the vector of errors in this bivariate linear model follows a standard bivariate normal distribution with a possibly nondiagonal covariance matrix. We show that this semiparametric model is identifiable, and propose estimators of the nonparametric transformation, the regression coefficients and the correlation between the error terms. It is shown that the estimators of the model parameters and the transformation are consistent and asymptotically normal. We also assess the finite-sample performance of the proposed method by comparing it with an estimation method under a fully parametric model. Finally, our method is illustrated using data from the AIDS Clinical Trial Group 175 study.
PubDate: Fri, 06 Nov 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa095
Issue No: Vol. 108, No. 4 (2020)

• Bagging cross-validated bandwidths with application to big data

Authors: Barreiro-Ures D; Cao R, Francisco-Fernández M, et al.
Pages: 981 - 988
Abstract: SummaryHall & Robinson (2009) proposed and analysed the use of bagged cross-validation to choose the bandwidth of a kernel density estimator. They established that bagging greatly reduces the noise inherent in ordinary cross-validation, and hence leads to a more efficient bandwidth selector. The asymptotic theory of Hall & Robinson (2009) assumes that $N$, the number of bagged subsamples, is $\infty$. We expand upon their theoretical results by allowing $N$ to be finite, as it is in practice. Our results indicate an important difference in the rate of convergence of the bagged cross-validation bandwidth for the cases $N=\infty$ and $N<\infty$. Simulations quantify the improvement in statistical efficiency and computational speed that can result from using bagged cross-validation as opposed to a binned implementation of ordinary cross-validation. The performance of the bagged bandwidth is also illustrated on a real, very large, dataset. Finally, a byproduct of our study is the correction of errors appearing in the Hall & Robinson (2009) expression for the asymptotic mean squared error of the bagging selector.
PubDate: Wed, 04 Nov 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa092
Issue No: Vol. 108, No. 4 (2020)

• Nontestability of instrument validity under continuous treatments

Authors: Gunsilius F.
Pages: 989 - 995
Abstract: SummaryThis note presents a proof of the conjecture in Pearl (1995) about testing the validity of an instrumental variable in hidden variable models. It implies that instrument validity cannot be tested in the case where the endogenous treatment is continuously distributed. This stands in contrast to the classical testability results for instrument validity when the treatment is discrete. However, imposing weak structural assumptions on the model, such as continuity between the observable variables, can re-establish theoretical testability in the continuous setting.
PubDate: Tue, 15 Dec 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa101
Issue No: Vol. 108, No. 4 (2020)

• Admissible estimators of a multivariate normal mean vector when the scale
is unknown

Authors: Maruyama Y; Strawderman W.
Pages: 997 - 1003
Abstract: SummaryWe study admissibility of a subclass of generalized Bayes estimators of a multivariate normal vector in the case where the variance is unknown, under scaled quadratic loss. Minimaxity is established for some of these estimators.
PubDate: Thu, 17 Dec 2020 00:00:00 GMT
DOI: 10.1093/biomet/asaa102
Issue No: Vol. 108, No. 4 (2020)

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