Biometrika最新文献_第8页

A cross-validation-based statistical theory for point processes 基于交叉验证的点过程统计理论

IF 2.7 2区数学

Biometrika Pub Date : 2023-06-27 DOI: 10.1093/biomet/asad041

O. Cronie, M. Moradi, C. Biscio

{"title":"A cross-validation-based statistical theory for point processes","authors":"O. Cronie, M. Moradi, C. Biscio","doi":"10.1093/biomet/asad041","DOIUrl":"https://doi.org/10.1093/biomet/asad041","url":null,"abstract":"\u0000 Motivated by cross-validation’s general ability to reduce overfitting and mean square error, we develop a cross-validation-based statistical theory for general point processes. It is based on the combination of two novel concepts for general point processes: cross-validation and prediction errors. Our cross-validation approach uses thinning to split a point process/pattern into pairs of training and validation sets, while our prediction errors measure discrepancy between two point processes. The new statistical approach, which may be used to model different distributional characteristics, exploits the prediction errors to measure how well a given model predicts validation sets using associated training sets. Having indicated that our new framework generalizes many existing statistical approaches, we then establish different theoretical properties for it, including large sample properties. We further recognize that non-parametric intensity estimation is an instance of Papangelou conditional intensity estimation, which we exploit to apply our new statistical theory to kernel intensity estimation. Using independent thinning-based cross-validation, we numerically show that the new approach substantially outperforms the state of the art in bandwidth selection. Finally, we carry out intensity estimation for a dataset in forestry (Euclidean domain) and a dataset in neurology (linear network).","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45141737","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: 0

A mark-specific quantile regression model. 特定于标记的分位数回归模型

IF 2.4 2区数学

Biometrika Pub Date : 2023-06-20 eCollection Date: 2024-03-01 DOI: 10.1093/biomet/asad039

Lianqiang Qu, Liuquan Sun, Yanqing Sun

{"title":"A mark-specific quantile regression model.","authors":"Lianqiang Qu, Liuquan Sun, Yanqing Sun","doi":"10.1093/biomet/asad039","DOIUrl":"10.1093/biomet/asad039","url":null,"abstract":"Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is an extension of cause of failure in a classical competing risk model where cause of failure is replaced by a continuous mark only observed at uncensored failure times. An example of the continuous mark variable is the genetic distance that measures dissimilarity between the infecting virus and the virus contained in the vaccine construct. In this article, we propose a novel mark-specific quantile regression model. The proposed estimation method borrows strength from data in a neighbourhood of a mark and is based on an induced smoothed estimation equation, which is very different from the existing methods for competing risk data with discrete causes. The asymptotic properties of the resulting estimators are established across mark and quantile continuums. In addition, a mark-specific quantile-type vaccine efficacy is proposed and its statistical inference procedures are developed. Simulation studies are conducted to evaluate the finite sample performances of the proposed estimation and hypothesis testing procedures. An application to the first HIV vaccine efficacy trial is provided.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11212524/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45605391","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

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

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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":null,"pages":null},"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

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