Biometrika最新文献_第5页

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":"15 2","pages":""},"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":"5 3","pages":""},"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

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

Maximum Likelihood Estimation for Semiparametric Regression Models with Interval-Censored Multistate Data 区间截尾多态数据半参数回归模型的极大似然估计

IF 2.7 2区数学

Biometrika Pub Date : 2023-11-24 DOI: 10.1093/biomet/asad073

Yu Gu, Donglin Zeng, Gerardo Heiss, D Y Lin

引用次数: 0

On varimax asymptotics in network models and spectral methods for dimensionality reduction 网络模型的变极大渐近性及降维的谱方法

IF 2.7 2区数学

Biometrika Pub Date : 2023-11-20 DOI: 10.1093/biomet/asad061

J Cape

{"title":"On varimax asymptotics in network models and spectral methods for dimensionality reduction","authors":"J Cape","doi":"10.1093/biomet/asad061","DOIUrl":"https://doi.org/10.1093/biomet/asad061","url":null,"abstract":"Summary Varimax factor rotations, while popular among practitioners in psychology and statistics since being introduced by H.Kaiser, have historically been viewed with skepticism and suspicion by some theoreticians and mathematical statisticians. Now, work by K. Rohe and M. Zeng provides new, fundamental insight: varimax rotations provably perform statistical estimation in certain classes of latent variable models when paired with spectral-based matrix truncations for dimensionality reduction. We build on this new-found understanding of varimax rotations by developing further connections to network analysis and spectral methods rooted in entrywise matrix perturbation analysis. Concretely, this paper establishes the asymptotic multivariate normality of vectors in varimax-transformed Euclidean point clouds that represent low-dimensional node embeddings in certain latent space random graph models. We address related concepts including network sparsity, data denoising, and the role of matrix rank in latent variable parameterizations. Collectively, these findings, at the confluence of classical and contemporary multivariate analysis, reinforce methodology and inference procedures grounded in matrix factorization-based techniques. Numerical examples illustrate our findings and supplement our discussion.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"19 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508101","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

Second term improvement to generalised linear mixed model asymptotics 广义线性混合模型渐近性的二阶改进

IF 2.7 2区数学

Biometrika Pub Date : 2023-11-16 DOI: 10.1093/biomet/asad072

Luca Maestrini, Aishwarya Bhaskaran, Matt P Wand

引用次数: 0

Discussion of 'Statistical inference for streamed longitudinal data'. 关于“纵向流数据的统计推断”的讨论。

IF 2.4 2区数学

Biometrika Pub Date : 2023-11-15 eCollection Date: 2023-12-01 DOI: 10.1093/biomet/asad043

Yang Ning, Jingyi Duan

引用次数: 0

Projective Independence Tests in High Dimensions: the Curses and the Cures 高维投射独立性检验:弊与弊

IF 2.7 2区数学

Biometrika Pub Date : 2023-11-15 DOI: 10.1093/biomet/asad070

Yaowu Zhang, Liping Zhu

{"title":"Projective Independence Tests in High Dimensions: the Curses and the Cures","authors":"Yaowu Zhang, Liping Zhu","doi":"10.1093/biomet/asad070","DOIUrl":"https://doi.org/10.1093/biomet/asad070","url":null,"abstract":"Summary Testing independence between high dimensional random vectors is fundamentally different from testing independence between univariate random variables. Take the projection correlation as an example. It suffers from at least three issues. First, it has a high computational complexity of O{n3 (p + q)}, where n, p and q are the respective sample size and dimensions of the random vectors. This limits its usefulness substantially when n is extremely large. Second, the asymptotic null distribution of the projection correlation test is rarely tractable. Therefore, random permutations are often suggested to approximate the asymptotic null distribution. This further increases the complexity of implementing independence tests. Last, the power performance of the projection correlation test deteriorates in high dimensions. To address these issues, we improve the projection correlation through a modified weight function, which reduces the complexity to O{n2 (p + q)}. We estimate the improved projection correlation with U-statistic theory. More importantly, its asymptotic null distribution is standard normal, thanks to the high dimensions of random vectors. This expedites the implementation of independence tests substantially. To enhance power performance in high dimensions, we introduce a cross-validation procedure which incorporates feature screening with the projection correlation test. The implementation efficacy and power enhancement are confirmed through extensive numerical studies.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"11 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508102","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: 2

Discussion of ‘Statistical inference for streamed longitudinal data’ 关于 "流式纵向数据的统计推断 "的讨论

IF 2.7 2区数学

Biometrika Pub Date : 2023-11-15 DOI: 10.1093/biomet/asad035

J. Wang, H. Wang, K. Chen

引用次数: 0

Discussion of ‘Statistical inference for streamed longitudinal data’ 关于 "流式纵向数据的统计推断 "的讨论

IF 2.7 2区数学

Biometrika Pub Date : 2023-11-15 DOI: 10.1093/biomet/asad034

Peter X-K Song, Ling Zhou

引用次数: 0