Biometrika最新文献_第8页

Correction to: Ancestor regression in linear structural equation models 修正：线性结构方程模型中的祖先回归

IF 2.7 2区数学

Biometrika Pub Date : 2023-06-10 DOI: 10.1093/biomet/asad028

引用次数: 0

Interpolating discriminant functions in high-dimensional Gaussian latent mixtures 高维高斯潜混合中判别函数的插值

2区数学

Biometrika Pub Date : 2023-06-08 DOI: 10.1093/biomet/asad037

Xin Bing, Marten Wegkamp

引用次数: 1

Sample-constrained partial identification with application to selection bias. 应用于选择偏差的样本约束部分识别。

IF 2.7 2区数学

Biometrika Pub Date : 2023-06-01 DOI: 10.1093/biomet/asac042

Matthew J Tudball, Rachael A Hughes, Kate Tilling, Jack Bowden, Qingyuan Zhao

{"title":"Sample-constrained partial identification with application to selection bias.","authors":"Matthew J Tudball, Rachael A Hughes, Kate Tilling, Jack Bowden, Qingyuan Zhao","doi":"10.1093/biomet/asac042","DOIUrl":"https://doi.org/10.1093/biomet/asac042","url":null,"abstract":"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. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to evaluate the finite sample performance of our inference procedure, and conclude with a substantive motivating example on the causal effect of education on income in the highly selected UK Biobank cohort. We demonstrate that our method can produce informative bounds using plausible population-level auxiliary constraints. We implement this method in the [Formula: see text] package [Formula: see text].","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"110 2","pages":"485-498"},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9914105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Bayesian learning of network structures from interventional experimental data 基于介入实验数据的网络结构贝叶斯学习

IF 2.7 2区数学

Biometrika Pub Date : 2023-05-11 DOI: 10.1093/biomet/asad032

F. Castelletti, S. Peluso

{"title":"Bayesian learning of network structures from interventional experimental data","authors":"F. Castelletti, S. Peluso","doi":"10.1093/biomet/asad032","DOIUrl":"https://doi.org/10.1093/biomet/asad032","url":null,"abstract":"\u0000 Directed Acyclic Graphs (DAGs) provide an effective framework for learning causal relationships among variables given multivariate observations. Under pure observational data, DAGs encoding the same conditional independencies cannot be distinguished and are collected into Markov equivalence classes. In many contexts however, observational measurements are supplemented by interventional data that improve DAG identifiability and enhance causal effect estimation. We propose a Bayesian framework for multivariate data partially generated after stochastic interventions. To this end, we introduce an effective prior elicitation procedure leading to a closed-form expression for the DAG marginal likelihood and guaranteeing score equivalence among DAGs that are Markov equivalent post intervention. Under the Gaussian setting we show, in terms of posterior ratio consistency, that the true network will be asymptotically recovered, regardless of the specific distribution of the intervened variables and of the relative asymptotic dominance between observational and interventional measurements. We validate our theoretical results in simulation and we implement on both synthetic and biological protein expression data a Markov chain Monte Carlo sampler for posterior inference on the space of DAGs.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43958916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Populations of Unlabelled Networks: Graph Space Geometry and Generalized Geodesic Principal Components 未标记网络的总体:图空间几何和广义测地线主成分

IF 2.7 2区数学

Biometrika Pub Date : 2023-04-04 DOI: 10.1093/biomet/asad024

Anna Calissano, Aasa Feragen, S. Vantini

引用次数: 3

Development and initial validation of the Daily Goal Realization Scale. 每日目标实现量表的开发和初步验证

IF 1 2区数学

Biometrika Pub Date : 2023-03-23 eCollection Date: 2023-01-01 DOI: 10.5114/cipp/155945

Mariola Łaguna, Emilia Mielniczuk, Wiktor Razmus

{"title":"Development and initial validation of the Daily Goal Realization Scale.","authors":"Mariola Łaguna, Emilia Mielniczuk, Wiktor Razmus","doi":"10.5114/cipp/155945","DOIUrl":"10.5114/cipp/155945","url":null,"abstract":"Background: This paper presents the results of three studies allowing the design and initial validation of the Daily Goal Realization Scale (DGRS). Goal realization refers to the engagement in goal-directed behavior that leads to progress in personal goal attainment; it is considered one of the adaptive personal characteristics.Participants and procedure: Three studies, including an initial study to develop and select the items (Study 1), an intensive longitudinal study (Study 2), and a multiple goal evaluation study (Study 3), tested factorial structure, reliability and validity of the measure.Results: Multilevel confirmatory factor analysis confirmed the unidimensional structure of the DGRS (obtained in Study 1) both at the individual and goal level, captured as daily goal realization (Study 2) and as multiple goal realization (Study 3). The validity of the DGRS was supported by meaningful associations with other goal evaluations (Study 3). As expected, the DGRS was positively related to evaluations of progress in goal achievement, engagement, likelihood of success, and goal importance. The DGRS also demonstrated measurement invariance allowing for meaningful comparisons of scores between men and women.Conclusions: The findings indicate that the DGRS is a brief and reliable idiographic measure of daily goal realization. The scale has excellent internal consistency and good criterion validity.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"51 1","pages":"240-250"},"PeriodicalIF":1.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82684058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable subsampling: computation, aggregation and inference 可伸缩子抽样:计算、聚合和推理

2区数学

Biometrika Pub Date : 2023-03-21 DOI: 10.1093/biomet/asad021

Dimitris N Politis

引用次数: 0

Causal inference with misspecified exposure mappings: separating definitions and assumptions 具有错误指定暴露映射的因果推断：分离定义和假设

IF 2.7 2区数学

Biometrika Pub Date : 2023-03-16 DOI: 10.1093/biomet/asad019

F. Sävje

引用次数: 1

√2-Estimation for Smooth Eigenvectors of Matrix-Valued Functions √2-矩阵值函数光滑特征向量的估计

IF 2.7 2区数学

Biometrika Pub Date : 2023-03-15 DOI: 10.1093/biomet/asad018

Giovanni Motta, W. Wu, M. Pourahmadi

引用次数: 0

Testing generalized linear models with high-dimensional nuisance parameter. 测试具有高维滋扰参数的广义线性模型

IF 2.7 2区数学

Biometrika Pub Date : 2023-03-01 Epub Date: 2022-04-05 DOI: 10.1093/biomet/asac021

Jinsong Chen, Quefeng Li, Hua Yun Chen

{"title":"Testing generalized linear models with high-dimensional nuisance parameter.","authors":"Jinsong Chen, Quefeng Li, Hua Yun Chen","doi":"10.1093/biomet/asac021","DOIUrl":"10.1093/biomet/asac021","url":null,"abstract":"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. We show that under certain regularity conditions, the type I error of the proposed method is asymptotically correct, and we establish its power under high-dimensional alternatives. Extensive simulations demonstrate the good performance of the proposed test and its robustness when certain sparsity assumptions are violated. We also apply the proposed method to Chinese famine sample data in order to show its performance when testing the significance of gene-environment interactions.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"110 1","pages":"83-99"},"PeriodicalIF":2.7,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10800040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0