Biometrika最新文献_第4页

A note on minimax robustness of designs against correlated or heteroscedastic responses 关于针对相关或异方差响应的最小稳健性设计的说明

IF 2.7 2区数学

Biometrika Pub Date : 2024-01-20 DOI: 10.1093/biomet/asae001

D P Wiens

引用次数: 0

Efficient nonparametric estimation of Toeplitz covariance matrices 托普利兹协方差矩阵的高效非参数估计

IF 2.7 2区数学

Biometrika Pub Date : 2024-01-17 DOI: 10.1093/biomet/asae002

K Klockmann, T Krivobokova

引用次数: 0

On Selecting and Conditioning in Multiple Testing and Selective Inference 论多重测试和选择性推理中的选择和条件限制

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-22 DOI: 10.1093/biomet/asad078

Jelle J Goeman, Aldo Solari

{"title":"On Selecting and Conditioning in Multiple Testing and Selective Inference","authors":"Jelle J Goeman, Aldo Solari","doi":"10.1093/biomet/asad078","DOIUrl":"https://doi.org/10.1093/biomet/asad078","url":null,"abstract":"We investigate a class of methods for selective inference that condition on a selection event. Such methods follow a two-stage process. First, a data-driven collection of hypotheses is chosen from some large universe of hypotheses. Subsequently, inference takes place within this data-driven collection, conditioned on the information that was used for the selection. Examples of such methods include basic data splitting, as well as modern data carving methods and post-selection inference methods for lasso coefficients based on the polyhedral lemma. In this paper, we adopt a holistic view on such methods, considering the selection, conditioning, and final error control steps together as a single method. From this perspective, we demonstrate that multiple testing methods defined directly on the full universe of hypotheses are always at least as powerful as selective inference methods based on selection and conditioning. This result holds true even when the universe is potentially infinite and only implicitly defined, such as in the case of data splitting. We give general theory and intuitions before investigating in detail several case studies where a shift to a non-selective or unconditional perspective can yield a power gain.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139051164","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

Central limit theorems for local network statistics 本地网络统计的中心极限定理

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-22 DOI: 10.1093/biomet/asad080

P A Maugis

引用次数: 0

The state of cumulative sum sequential change point testing seventy years after Page 累积和顺序变化点测试七十年后的状况 Page

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-21 DOI: 10.1093/biomet/asad079

Alexander Aue, Claudia Kirch

{"title":"The state of cumulative sum sequential change point testing seventy years after Page","authors":"Alexander Aue, Claudia Kirch","doi":"10.1093/biomet/asad079","DOIUrl":"https://doi.org/10.1093/biomet/asad079","url":null,"abstract":"\u0000 Quality control charts aim at raising an alarm as soon as sequentially obtained observations of an underlying random process no longer seem to be within stochastic fluctuations prescribed by an ‘in-control’ scenario. Such random processes can often be modelled using the concept of stationarity, or even independence as in most classical works. An important out-of-control scenario is the changepoint alternative, for which the distribution of the process changes at an unknown point in time. In his seminal 1954 Biometrika paper, E. S. Page introduced the famous cumulative sum control charts for changepoint monitoring. Innovatively, decision rules based on cumulative sum procedures took the full history of the process into account, whereas previous procedures were based only on a fixed and typically small number of the most recent observations. The extreme case of using only the most recent observation, often referred to as the Shewhart chart, is more akin to serial outlier than changepoint detection. Page’s cumulative sum approach, introduced seven decades ago, is ubiquitous in modern changepoint analysis, and his original paper has led to a multitude of follow-up papers in different research communities. This review is focused on a particular subfield of this research, namely nonparametric sequential, or online, changepoint tests which are constructed to maintain a desired Type 1 error as opposed to the more traditional approach seeking to minimize the average run length of the procedures. Such tests have originated at the intersection of econometrics and statistics. We trace the development of these tests and highlight their properties, mostly using a simple location model for clarity of exposition, but also review more complex situations such as regression and time series models.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951837","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

Correction to: ‘A cross-validation-based statistical theory for point processes’ 更正：基于交叉验证的点过程统计理论

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-20 DOI: 10.1093/biomet/asad077

引用次数: 0

Phylogenetic Association Analysis with Conditional Rank Correlation 基于条件秩相关的系统发育关联分析

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-01 DOI: 10.1093/biomet/asad075

Shulei Wang, Bo Yuan, T Tony Cai, Hongzhe Li

{"title":"Phylogenetic Association Analysis with Conditional Rank Correlation","authors":"Shulei Wang, Bo Yuan, T Tony Cai, Hongzhe Li","doi":"10.1093/biomet/asad075","DOIUrl":"https://doi.org/10.1093/biomet/asad075","url":null,"abstract":"Summary Phylogenetic association analysis plays a crucial role in investigating the correlation between microbial compositions and specific outcomes of interest in microbiome studies. However, existing methods for testing such associations have limitations related to the assumption of a linear association in high-dimensional settings and the handling of confounding effects. Therefore, there is a need for methods capable of characterizing complex associations, including nonmonotonic relationships. This paper introduces a novel phylogenetic association analysis framework and associated tests to address these challenges by employing conditional rank correlation as a measure of association. These tests account for confounders in a fully nonparametric manner, ensuring robustness against outliers and the ability to detect diverse dependencies. The proposed framework aggregates conditional rank correlations for subtrees using a weighted sum and maximum approach to capture both dense and sparse signals. The significance level of the test statistics is determined by calibrating through a nearest neighbour bootstrapping method, which is straightforward to implement and can accommodate additional datasets when available. The practical advantages of the proposed framework are demonstrated through numerical experiments utilizing both simulated and real microbiome datasets.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508105","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

Conformalized survival analysis with adaptive cutoffs 具有自适应截断的符合化生存分析

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-01 DOI: 10.1093/biomet/asad076

Yu Gui, Rohan Hore, Zhimei Ren, Rina Foygel Barber

{"title":"Conformalized survival analysis with adaptive cutoffs","authors":"Yu Gui, Rohan Hore, Zhimei Ren, Rina Foygel Barber","doi":"10.1093/biomet/asad076","DOIUrl":"https://doi.org/10.1093/biomet/asad076","url":null,"abstract":"Summary This paper introduces an assumption-lean method that constructs valid and efficient lower predictive bounds (LPBs) for survival times with censored data.We build on recent work by Candès et al. (2021), whose approach first subsets the data to discard any data points with early censoring times, and then uses a reweighting technique (namely, weighted conformal inference (Tibshirani et al., 2019)) to correct for the distribution shift introduced by this subsetting procedure. For our new method, instead of constraining to a fixed threshold for the censoring time when subsetting the data, we allow for a covariate-dependent and data-adaptive subsetting step, which is better able to capture the heterogeneity of the censoring mechanism. As a result, our method can lead to LPBs that are less conservative and give more accurate information. We show that in the Type I right-censoring setting, if either of the censoring mechanism or the conditional quantile of survival time is well estimated, our proposed procedure achieves nearly exact marginal coverage, where in the latter case we additionally have approximate conditional coverage. We evaluate the validity and efficiency of our proposed algorithm in numerical experiments, illustrating its advantage when compared with other competing methods. Finally, our method is applied to a real dataset to generate LPBs for users’ active times on a mobile app.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508131","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

Efficient Estimation under Data Fusion. 数据融合下的有效估计

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-01 Epub Date: 2023-02-06 DOI: 10.1093/biomet/asad007

Sijia Li, Alex Luedtke

{"title":"Efficient Estimation under Data Fusion.","authors":"Sijia Li, Alex Luedtke","doi":"10.1093/biomet/asad007","DOIUrl":"10.1093/biomet/asad007","url":null,"abstract":"<p><p>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. We describe potential gains in efficiency that can arise from fusing these data sources together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. We also provide a general means to construct estimators that achieve these bounds. In numerical simulations, we illustrate marked improvements in efficiency from using our proposed estimators rather than their natural alternatives. Finally, we illustrate the magnitude of efficiency gains that can be realized in vaccine immunogenicity studies by fusing data from two HIV vaccine trials.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44309457","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: 7

Familial inference: Tests for hypotheses on a family of centres 家族推理:对一个中心家族的假设进行检验

IF 2.7 2区数学

Biometrika Pub Date : 2023-11-28 DOI: 10.1093/biomet/asad074

Ryan Thompson, Catherine S Forbes, Steven N Maceachern, Mario Peruggia

引用次数: 0