journal
https://read.qxmd.com/read/38352626/statistical-summaries-of-unlabelled-evolutionary-trees
#1
JOURNAL ARTICLE
Rajanala Samyak, Julia A Palacios
Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, relatively less literature exists for unlabelled trees that are increasingly useful, for example when one seeks to summarize samples of trees obtained with different methods, or from different samples and environments, and wishes to assess the stability and generalizability of these summaries...
March 2024: Biometrika
https://read.qxmd.com/read/37982010/efficient-estimation-under-data-fusion
#2
JOURNAL ARTICLE
Sijia Li, Alex Luedtke
We aim to make inferences about a smooth, finite-dimensional parameter by fusing data from multiple sources together. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including in the estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions, and rewards and one data source of the same covariates. In this work, we consider the general case where one or more data sources align with each part of the distribution of the target population, for example, the conditional distribution of the reward given actions and covariates...
December 2023: Biometrika
https://read.qxmd.com/read/37981957/discussion-of-statistical-inference-for-streamed-longitudinal-data
#3
JOURNAL ARTICLE
Yang Ning, Jingyi Duan
No abstract text is available yet for this article.
December 2023: Biometrika
https://read.qxmd.com/read/38500847/spectral-adjustment-for-spatial-confounding
#4
JOURNAL ARTICLE
Yawen Guan, Garritt L Page, Brian J Reich, Massimo Ventrucci, Shu Yang
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures identifiability is that confounding present at global scales dissipates at local scales...
September 2023: Biometrika
https://read.qxmd.com/read/37711671/assessing-time-varying-causal-effect-moderation-in-the-presence-of-cluster-level-treatment-effect-heterogeneity-and-interference
#5
JOURNAL ARTICLE
Jieru Shi, Zhenke Wu, Walter Dempsey
The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points. MRTs have motivated a new class of causal estimands, termed "causal excursion effects", for which semiparametric inference can be conducted via a weighted, centered least squares criterion (Boruvka et al., 2018). Existing methods assume between-subject independence and non-interference...
September 2023: Biometrika
https://read.qxmd.com/read/37601305/marginal-proportional-hazards-models-for-multivariate-interval-censored-data
#6
JOURNAL ARTICLE
Yangjianchen Xu, Donglin Zeng, D Y Lin
Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm...
September 2023: Biometrika
https://read.qxmd.com/read/37197742/a-multiplicative-structural-nested-mean-model-for-zero-inflated-outcomes
#7
JOURNAL ARTICLE
Miao Yu, Wenbin Lu, Shu Yang, Pulak Ghosh
Zero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated parametrically or nonparametrically...
June 2023: Biometrika
https://read.qxmd.com/read/37197741/sample-constrained-partial-identification-with-application-to-selection-bias
#8
JOURNAL ARTICLE
Matthew J Tudball, Rachael A Hughes, Kate Tilling, Jack Bowden, Qingyuan Zhao
Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies...
June 2023: Biometrika
https://read.qxmd.com/read/37197740/gradient-based-sparse-principal-component-analysis-with-extensions-to-online-learning
#9
JOURNAL ARTICLE
Yixuan Qiu, Jing Lei, Kathryn Roeder
Sparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of multipliers, and can be more efficiently implemented with the rich toolbox developed for gradient methods from the deep learning literature...
June 2023: Biometrika
https://read.qxmd.com/read/37197739/multi-stage-optimal-dynamic-treatment-regimes-for-survival-outcomes-with-dependent-censoring
#10
JOURNAL ARTICLE
Hunyong Cho, Shannon T Holloway, David J Couper, Michael R Kosorok
We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time-point. The estimator is constructed using generalized random survival forests and can have polynomial rates of convergence...
June 2023: Biometrika
https://read.qxmd.com/read/36816791/testing-generalized-linear-models-with-high-dimensional-nuisance-parameter
#11
JOURNAL ARTICLE
Jinsong Chen, Quefeng Li, Hua Yun Chen
Generalized linear models often have a high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional sub-vector of the model's coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and thus are computationally expensive. Here, we propose a computationally efficient test with a closed-form limiting distribution, which allows the parameter being tested to be either sparse or dense...
March 2023: Biometrika
https://read.qxmd.com/read/36798841/instrumental-variable-estimation-of-the-marginal-structural-cox-model-for-time-varying-treatments
#12
JOURNAL ARTICLE
Y Cui, H Michael, F Tanser, E Tchetgen Tchetgen
Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural models for evaluating the causal effect of time-varying treatments on a censored failure time outcome...
March 2023: Biometrika
https://read.qxmd.com/read/36798840/data-integration-exploiting-ratios-of-parameter-estimates-from-a-reduced-external-model
#13
JOURNAL ARTICLE
Jeremy M G Taylor, Kyuseong Choi, Peisong Han
We consider the situation of estimating the parameters in a generalized linear prediction model, from an internal dataset, where the outcome variable [Formula: see text] is binary and there are two sets of covariates, [Formula: see text] and [Formula: see text]. We have information from an external study that provides parameter estimates for a generalized linear model of [Formula: see text] on [Formula: see text]. We propose a method that makes limited assumptions about the similarity of the distributions in the two study populations...
March 2023: Biometrika
https://read.qxmd.com/read/38094986/a-proximal-distance-algorithm-for-likelihood-based-sparse-covariance-estimation
#14
JOURNAL ARTICLE
Jason Xu, Kenneth Lange
This paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems...
December 2022: Biometrika
https://read.qxmd.com/read/36685139/significance-testing-for-canonical-correlation-analysis-in-high-dimensions
#15
JOURNAL ARTICLE
Ian W McKeague, Xin Zhang
We consider the problem of testing for the presence of linear relationships between large sets of random variables based on a post-selection inference approach to canonical correlation analysis. The challenge is to adjust for the selection of subsets of variables having linear combinations with maximal sample correlation. To this end, we construct a stabilized one-step estimator of the euclidean-norm of the canonical correlations maximized over subsets of variables of pre-specified cardinality. This estimator is shown to be consistent for its target parameter and asymptotically normal, provided the dimensions of the variables do not grow too quickly with sample size...
December 2022: Biometrika
https://read.qxmd.com/read/36643962/graphical-gaussian-process-models-for-highly-multivariate-spatial-data
#16
JOURNAL ARTICLE
Debangan Dey, Abhirup Datta, Sudipto Banerjee
For multivariate spatial Gaussian process (GP) models, customary specifications of cross-covariance functions do not exploit relational inter-variable graphs to ensure process-level conditional independence among the variables. This is undesirable, especially for highly multivariate settings, where popular cross-covariance functions such as the multivariate Matérn suffer from a "curse of dimensionality" as the number of parameters and floating point operations scale up in quadratic and cubic order, respectively, in the number of variables...
December 2022: Biometrika
https://read.qxmd.com/read/36531154/functional-hybrid-factor-regression-model-for-handling-heterogeneity-in-imaging-studies
#17
JOURNAL ARTICLE
C Huang, H Zhu
This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer's disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data collected from multicentres or multistudies. We propose both estimation and inference procedures for estimating unknown parameters and detecting unknown factors under our new model...
December 2022: Biometrika
https://read.qxmd.com/read/36381997/multi-scale-fisher-s-independence-test-for-multivariate-dependence
#18
JOURNAL ARTICLE
S Gorsky, L Ma
Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them in the presence of massive sample sizes. Moreover, resampling is usually necessary to evaluate the statistical significance of the resulting test statistics at finite sample sizes, further worsening the computational burden. We introduce a scalable, resampling-free approach to testing the independence between two random vectors by breaking down the task into simple univariate tests of independence on a collection of 2 × 2 contingency tables constructed through sequential coarse-to-fine discretization of the sample space, transforming the inference task into a multiple testing problem that can be completed with almost linear complexity with respect to the sample size...
September 2022: Biometrika
https://read.qxmd.com/read/36105175/generalized-infinite-factorization-models
#19
JOURNAL ARTICLE
L Schiavon, A Canale, D B Dunson
Factorization models express a statistical object of interest in terms of a collection of simpler objects. For example, a matrix or tensor can be expressed as a sum of rank-one components. However, in practice, it can be challenging to infer the relative impact of the different components as well as the number of components. A popular idea is to include infinitely many components having impact decreasing with the component index. This article is motivated by two limitations of existing methods: (1) lack of careful consideration of the within component sparsity structure; and (2) no accommodation for grouped variables and other non-exchangeable structures...
September 2022: Biometrika
https://read.qxmd.com/read/36035896/on-the-relative-efficiency-of-the-intent-to-treat-wilcoxon-mann-whitney-test-in-the-presence-of-noncompliance
#20
JOURNAL ARTICLE
Lu Mao
A general framework is set up to study the asymptotic properties of the intent-to-treat Wilcoxon-Mann-Whitney test in randomized experiments with nonignorable noncompliance. Under location-shift alternatives, the Pitman efficiencies of the intent-to-treat Wilcoxon-Mann-Whitney and [Formula: see text] tests are derived. It is shown that the former is superior if the compliers are more likely to be found in high-density regions of the outcome distribution or, equivalently, if the noncompliers tend to reside in the tails...
September 2022: Biometrika
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