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Optimal regimes for algorithm-assisted human decision-making 算法辅助人类决策的最佳机制
IF 2.7 2区 数学
Biometrika Pub Date : 2024-03-19 DOI: 10.1093/biomet/asae016
M J Stensrud, J D Laurendeau, A L Sarvet
{"title":"Optimal regimes for algorithm-assisted human decision-making","authors":"M J Stensrud, J D Laurendeau, A L Sarvet","doi":"10.1093/biomet/asae016","DOIUrl":"https://doi.org/10.1093/biomet/asae016","url":null,"abstract":"Summary We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are identical to conventional optimal regimes. Furthermore, identification of the expected outcome under superoptimal regimes in non-experimental studies requires the same assumptions as identification of value functions under conventional optimal regimes when the treatment is binary. To illustrate the utility of superoptimal regimes, we derive identification and estimation results in a common instrumental variable setting. We use these derivations to analyse examples from the optimal regimes literature, including a case study of the effect of prompt intensive care treatment on survival.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199831","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
Inference of partial correlations of a multivariate Gaussian time series 多变量高斯时间序列的局部相关性推理
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-26 DOI: 10.1093/biomet/asae012
A S DiLernia, M Fiecas, L Zhang
{"title":"Inference of partial correlations of a multivariate Gaussian time series","authors":"A S DiLernia, M Fiecas, L Zhang","doi":"10.1093/biomet/asae012","DOIUrl":"https://doi.org/10.1093/biomet/asae012","url":null,"abstract":"We derive an asymptotic joint distribution and novel covariance estimator for the partial correlations of a multivariate Gaussian time series given mild regularity conditions. Using our derived asymptotic distribution, we develop a Wald confidence interval and testing procedure for inference of individual partial correlations for time series data. Through simulation we demonstrate that our proposed confidence interval attains higher coverage rates, and our testing procedure attains false positive rates closer to the nominal levels than approaches that assume independent observations when autocorrelation is present.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977684","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
Network-adjusted covariates for community detection 用于群落探测的网络调整协变量
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-24 DOI: 10.1093/biomet/asae011
Y Hu, W Wang
{"title":"Network-adjusted covariates for community detection","authors":"Y Hu, W Wang","doi":"10.1093/biomet/asae011","DOIUrl":"https://doi.org/10.1093/biomet/asae011","url":null,"abstract":"Community detection is a crucial task in network analysis that can be significantly improved by incorporating subject-level information, i.e., covariates. Existing methods have shown the effectiveness of using covariates on the low-degree nodes, but rarely discuss the case where communities have significantly different density levels, i.e. multiscale networks. In this paper, we introduce a novel method that addresses this challenge by constructing network-adjusted covariates, which leverage the network connections and covariates with a node-specific weight for each node. This weight can be calculated without tuning parameters. We present novel theoretical results on the strong consistency of our method under degree-corrected stochastic blockmodels with covariates, even in the presence of misspecification and multiple sparse communities. Additionally, we establish a general lower bound for the community detection problem when both network and covariates are present, and it shows our method is optimal for connection intensity up to a constant factor. Our method outperforms existing approaches in simulations and a LastFM app user network. We then compare our method with others on a statistics publication citation network where 30% of nodes are isolated, and our method produces reasonable and balanced results.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950512","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
Selective conformal inference with false coverage-statement rate control 带错误覆盖率控制的选择性保形推理
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-19 DOI: 10.1093/biomet/asae010
Yajie Bao, Yuyang Huo, Haojie Ren, Changliang Zou
{"title":"Selective conformal inference with false coverage-statement rate control","authors":"Yajie Bao, Yuyang Huo, Haojie Ren, Changliang Zou","doi":"10.1093/biomet/asae010","DOIUrl":"https://doi.org/10.1093/biomet/asae010","url":null,"abstract":"Conformal inference is a popular tool for constructing prediction intervals. We consider here the scenario of post-selection/selective conformal inference, that is prediction intervals are reported only for individuals selected from unlabelled test data. To account for multiplicity, we develop a general split conformal framework to construct selective prediction intervals with the false coverage-statement rate control. We first investigate benjamini2005false's false coverage rate-adjusted method in the present setting, and show that it is able to achieve false coverage-statement rate control but yields uniformly inflated prediction intervals. We then propose a novel solution to the problem called selective conditional conformal prediction. Our method performs selection procedures on both the calibration set and test set, and then constructs conformal prediction intervals for the selected test candidates with the aid of conditional empirical distribution obtained by the post-selection calibration set. When the selection rule is exchangeable, we show that our proposed method can exactly control the false coverage-statement rate in a model-free and distribution-free guarantee. For non-exchangeable selection procedures involving the calibration set, we provide non-asymptotic bounds for the false coverage-statement rate under mild distributional assumptions. Numerical results confirm the effectiveness and robustness of our method in false coverage-statement rate control and show that it achieves more narrowed prediction intervals over existing methods across various settings.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950365","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
The Promises of Parallel Outcomes 并行成果的承诺
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-17 DOI: 10.1093/biomet/asae008
Ying Zhou, Dingke Tang, Dehan Kong, Linbo Wang
{"title":"The Promises of Parallel Outcomes","authors":"Ying Zhou, Dingke Tang, Dehan Kong, Linbo Wang","doi":"10.1093/biomet/asae008","DOIUrl":"https://doi.org/10.1093/biomet/asae008","url":null,"abstract":"A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that leverages information in multiple outcomes to deal with unmeasured confounding. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in the conditional independence assumption are symmetric, hence the name parallel outcomes. We show nonparametric identifiability with at least three parallel outcomes and provide parametric estimation tools under a set of linear structural equation models. Our proposal is evaluated through a set of synthetic and real data analyses.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139924069","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
Doubly robust estimation under covariate-induced dependent left truncation 协变量诱发的依存左截断条件下的双稳健估计
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-11 DOI: 10.1093/biomet/asae005
Yuyao Wang, Andrew Ying, Ronghui Xu
{"title":"Doubly robust estimation under covariate-induced dependent left truncation","authors":"Yuyao Wang, Andrew Ying, Ronghui Xu","doi":"10.1093/biomet/asae005","DOIUrl":"https://doi.org/10.1093/biomet/asae005","url":null,"abstract":"Summary In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of time-to-event, conventional methods adjusting for left truncation tend to rely on the quasi-independence assumption that the truncation time and the event time are independent on the observed region. This assumption is violated when there is dependence between the truncation time and the event time possibly induced by measured covariates. Inverse probability of truncation weighting can be used in this case, but it is sensitive to misspecification of the truncation model. In this work, we apply semiparametric theory to find the efficient influence curve of the expectation of an arbitrarily transformed survival time in the presence of covariate-induced dependent left truncation. We then use it to construct estimators that are shown to enjoy double-robustness properties. Our work represents the first attempt to construct doubly robust estimators in the presence of left truncation, which does not fall under the established framework of coarsened data where doubly robust approaches were developed. We provide technical conditions for the asymptotic properties that appear to not have been carefully examined in the literature for time-to-event data, and study the estimators via extensive simulation. We apply the estimators to two datasets from practice, with different right-censoring patterns.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769979","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
Regression analysis of group-tested current status data 对分组测试的现状数据进行回归分析
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-08 DOI: 10.1093/biomet/asae006
Shuwei Li, Tao Hu, Lianming Wang, Christopher S McMahan, Joshua M Tebbs
{"title":"Regression analysis of group-tested current status data","authors":"Shuwei Li, Tao Hu, Lianming Wang, Christopher S McMahan, Joshua M Tebbs","doi":"10.1093/biomet/asae006","DOIUrl":"https://doi.org/10.1093/biomet/asae006","url":null,"abstract":"Summary Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested current status data (?). To analyse these complex data, we propose methods which estimate a proportional hazards regression model based on test outcomes from measuring the pools. A sieve maximum likelihood estimation approach is developed that approximates the cumulative baseline hazard function with a piecewise constant function. To identify the sieve estimator, a computationally efficient expectation-maximization algorithm is derived by using data augmentation. Asymptotic properties of both the parametric and nonparametric components of the sieve estimator are then established by applying modern empirical process theory. Numerical results from simulation studies show that our proposed method performs nominally and has advantages over the corresponding estimation method based on individual testing results. We illustrate our work by analysing a chlamydia dataset collected by the State Hygienic Laboratory at the University of Iowa.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139770356","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
Explicit solutions for the asymptotically-optimal bandwidth in cross-validation 交叉验证中渐近最优带宽的显式解法
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-08 DOI: 10.1093/biomet/asae007
Karim M Abadir, Michel Lubrano
{"title":"Explicit solutions for the asymptotically-optimal bandwidth in cross-validation","authors":"Karim M Abadir, Michel Lubrano","doi":"10.1093/biomet/asae007","DOIUrl":"https://doi.org/10.1093/biomet/asae007","url":null,"abstract":"Summary We show that least squares cross-validation methods share a common structure which has an explicit asymptotic solution, when the chosen kernel is asymptotically separable in bandwidth and data. For density estimation with a multivariate Student t(ν) kernel, the cross-validation criterion becomes asymptotically equivalent to a polynomial of only three terms. Our bandwidth formulae are simple and noniterative thus leading to very fast computations, their integrated squared-error dominates traditional cross-validation implementations, they alleviate the notorious sample variability of cross-validation, and overcome its breakdown in the case of repeated observations. We illustrate our method with univariate and bivariate applications, of density estimation and nonparametric regressions, to a large dataset of Michigan State University academic wages and experience.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139770360","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
On the failure of the bootstrap for Chatterjee's rank correlation 关于查特吉秩相关自举法的失败
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-04 DOI: 10.1093/biomet/asae004
Zhexiao Lin, Fang Han
{"title":"On the failure of the bootstrap for Chatterjee's rank correlation","authors":"Zhexiao Lin, Fang Han","doi":"10.1093/biomet/asae004","DOIUrl":"https://doi.org/10.1093/biomet/asae004","url":null,"abstract":"Summary While researchers commonly use the bootstrap to quantify the uncertainty of an estimator, it has been noticed that the standard bootstrap, in general, does not work for Chatterjee's rank correlation. In this paper, we provide proof of this issue under an additional independence assumption, and complement our theory with simulation evidence for general settings. Chatterjee's rank correlation thus falls into a category of statistics that are asymptotically normal but bootstrap inconsistent. Valid inferential methods in this case are Chatterjee's original proposal for testing independence and Lin & Han (2022) 's analytic asymptotic variance estimator for more general purposes.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139770362","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
Asymptotically constant risk estimator of the time-average variance constant 时间平均方差常数的渐近恒定风险估计器
IF 2.7 2区 数学
Biometrika Pub Date : 2024-02-03 DOI: 10.1093/biomet/asae003
K W Chan, C Y Yau
{"title":"Asymptotically constant risk estimator of the time-average variance constant","authors":"K W Chan, C Y Yau","doi":"10.1093/biomet/asae003","DOIUrl":"https://doi.org/10.1093/biomet/asae003","url":null,"abstract":"Summary Estimation of the time-average variance constant is important for statistical analyses involving dependent data. This problem is difficult as it relies on a bandwidth parameter. Specifically, the optimal choices of the bandwidths of all existing estimators depend on the estimand itself and another unknown parameter which is very difficult to estimate. Thus, optimal variance estimation is unachievable. In this paper, we introduce a concept of converging flat-top kernels for constructing variance estimators whose optimal bandwidths are free of unknown parameters asymptotically and hence can be computed easily. We prove that the new estimator has an asymptotically constant risk and is locally asymptotically minimax.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139678925","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
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