Journal of the Royal Statistical Society Series B-Statistical Methodology最新文献_第7页

Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects 超过尖锐零值的随机化推断:有界零假设和个体治疗效果的分位数

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-08-19 DOI: 10.1093/jrsssb/qkad080

Devin Caughey, Allan Dafoe, Xinran Li, Luke Miratrix

{"title":"Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects","authors":"Devin Caughey, Allan Dafoe, Xinran Li, Luke Miratrix","doi":"10.1093/jrsssb/qkad080","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad080","url":null,"abstract":"Abstract Randomisation inference (RI) is typically interpreted as testing Fisher’s ‘sharp’ null hypothesis that all unit-level effects are exactly zero. This hypothesis is often criticised as restrictive and implausible, making its rejection scientifically uninteresting. We show, however, that many randomisation tests are also valid for a ‘bounded’ null hypothesis under which the unit-level effects are all non-positive (or all non-negative) but are otherwise heterogeneous. In addition to being more plausible a priori, bounded nulls are closely related to substantively important concepts such as monotonicity and Pareto efficiency. Reinterpreting RI in this way expands the range of inferences possible in this framework. We show that exact confidence intervals for the maximum (or minimum) unit-level effect can be obtained by inverting tests for a sequence of bounded nulls. We also generalise RI to cover inference for quantiles of the individual effect distribution as well as for the proportion of individual effects larger (or smaller) than a given threshold. The proposed confidence intervals for all effect quantiles are simultaneously valid, in the sense that no correction for multiple analyses is required. In sum, our reinterpretation and generalisation provide a broader justification for randomisation tests and a basis for exact non-parametric inference for effect quantiles.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135936658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Maxway CRT: improving the robustness of the model-X inference Maxway CRT:提高模型- x推理的鲁棒性

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-08-17 DOI: 10.1093/jrsssb/qkad081

Shuangning Li, Molei Liu

{"title":"Maxway CRT: improving the robustness of the model-X inference","authors":"Shuangning Li, Molei Liu","doi":"10.1093/jrsssb/qkad081","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad081","url":null,"abstract":"Abstract The model-X conditional randomisation test (CRT) is a flexible and powerful testing procedure for testing the hypothesis X⫫Y∣Z. However, it requires perfect knowledge of X∣Z and may lose its validity when there is an error in modelling X∣Z. This problem is even more severe when Z is of high dimensionality. In response to this, we propose the Maxway CRT, which learns the distribution of Y∣Z and uses it to calibrate the resampling distribution of X to gain robustness to the error in modelling X. We prove that the type-I error inflation of the Maxway CRT can be controlled by the learning error for a low-dimensional adjusting model plus the product of learning errors for X∣Z and Y∣Z, interpreted as an ‘almost doubly robust’ property. Based on this, we develop implementing algorithms of the Maxway CRT in practical scenarios including (surrogate-assisted) semi-supervised learning (SA-SSL) and transfer learning (TL). Through simulations, we demonstrate that the Maxway CRT achieves significantly better type-I error control than existing model-X inference approaches while preserving similar powers. Finally, we apply our methodology to two real examples of SA-SSL and TL.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136336395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Debiased inference on heterogeneous quantile treatment effects with regression rank scores 用回归等级评分对异质性分位数治疗效果的去偏推断

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-08-08 DOI: 10.1093/jrsssb/qkad075

Alexander Giessing, Jingshen Wang

引用次数: 1

Correction to: Sensitivity Analysis for Inverse Probability Weighting Estimators via the Percentile Bootstrap 修正:通过百分位Bootstrap对逆概率加权估计的敏感性分析

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-08-05 DOI: 10.1093/jrsssb/qkad079

引用次数: 0

Autoregressive optimal transport models. 自回归最优运输模型。

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-07-01 DOI: 10.1093/jrsssb/qkad051

Changbo Zhu, Hans-Georg Müller

{"title":"Autoregressive optimal transport models.","authors":"Changbo Zhu, Hans-Georg Müller","doi":"10.1093/jrsssb/qkad051","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad051","url":null,"abstract":"Series of univariate distributions indexed by equally spaced time points are ubiquitous in applications and their analysis constitutes one of the challenges of the emerging field of distributional data analysis. To quantify such distributional time series, we propose a class of intrinsic autoregressive models that operate in the space of optimal transport maps. The autoregressive transport models that we introduce here are based on regressing optimal transport maps on each other, where predictors can be transport maps from an overall barycenter to a current distribution or transport maps between past consecutive distributions of the distributional time series. Autoregressive transport models and their associated distributional regression models specify the link between predictor and response transport maps by moving along geodesics in Wasserstein space. These models emerge as natural extensions of the classical autoregressive models in Euclidean space. Unique stationary solutions of autoregressive transport models are shown to exist under a geometric moment contraction condition of Wu & Shao [(2004) Limit theorems for iterated random functions. Journal of Applied Probability 41, 425-436)], using properties of iterated random functions. We also discuss an extension to a varying coefficient model for first-order autoregressive transport models. In addition to simulations, the proposed models are illustrated with distributional time series of house prices across U.S. counties and annual summer temperature distributions.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"85 3","pages":"1012-1033"},"PeriodicalIF":5.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/69/84/qkad051.PMC10376456.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9910306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 12

Testing homogeneity: the trouble with sparse functional data. 测试同质性:稀疏函数数据的麻烦。

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-07-01 DOI: 10.1093/jrsssb/qkad021

Changbo Zhu, Jane-Ling Wang

引用次数: 0

On the causal interpretation of randomised interventional indirect effects 关于随机介入间接效应的因果解释

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-06-28 DOI: 10.1093/jrsssb/qkad066

Caleb H Miles

引用次数: 3

Two-stage estimation and bias-corrected empirical likelihood in a partially linear single-index varying-coefficient model 部分线性单指标变系数模型的两阶段估计和偏差校正经验似然

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-06-27 DOI: 10.1093/jrsssb/qkad060

L. Xue

引用次数: 0

Testing for the Markov property in time series via deep conditional generative learning. 通过深度条件生成学习测试时间序列中的马尔可夫性质。

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-06-23 eCollection Date: 2023-09-01 DOI: 10.1093/jrsssb/qkad064

Yunzhe Zhou, Chengchun Shi, Lexin Li, Qiwei Yao

{"title":"Testing for the Markov property in time series via deep conditional generative learning.","authors":"Yunzhe Zhou, Chengchun Shi, Lexin Li, Qiwei Yao","doi":"10.1093/jrsssb/qkad064","DOIUrl":"10.1093/jrsssb/qkad064","url":null,"abstract":"The Markov property is widely imposed in analysis of time series data. Correspondingly, testing the Markov property, and relatedly, inferring the order of a Markov model, are of paramount importance. In this article, we propose a nonparametric test for the Markov property in high-dimensional time series via deep conditional generative learning. We also apply the test sequentially to determine the order of the Markov model. We show that the test controls the type-I error asymptotically, and has the power approaching one. Our proposal makes novel contributions in several ways. We utilise and extend state-of-the-art deep generative learning to estimate the conditional density functions, and establish a sharp upper bound on the approximation error of the estimators. We derive a doubly robust test statistic, which employs a nonparametric estimation but achieves a parametric convergence rate. We further adopt sample splitting and cross-fitting to minimise the conditions required to ensure the consistency of the test. We demonstrate the efficacy of the test through both simulations and the three data applications.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"85 4","pages":"1204-1222"},"PeriodicalIF":5.8,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10541293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41140853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Normalised latent measure factor models 归一化潜在测量因子模型

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-06-23 DOI: 10.1093/jrsssb/qkad062

Mario Beraha, Jim E Griffin

引用次数: 0