Biometrika最新文献_第5页

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

Generalized kernel two-sample tests 广义核双样本测试

2区数学

Biometrika Pub Date : 2023-11-14 DOI: 10.1093/biomet/asad068

Hoseung Song, Hao Chen

引用次数: 9

Testing Serial Independence of Object-Valued Time Series 对象值时间序列序列独立性的检验

2区数学

Biometrika Pub Date : 2023-11-11 DOI: 10.1093/biomet/asad069

Feiyu Jiang, Hanjia Gao, Xiaofeng Shao

引用次数: 0

On the optimality of score-driven models 分数驱动模型的最优性

2区数学

Biometrika Pub Date : 2023-11-09 DOI: 10.1093/biomet/asad067

P Gorgi, C S A Lauria, A Luati

{"title":"On the optimality of score-driven models","authors":"P Gorgi, C S A Lauria, A Luati","doi":"10.1093/biomet/asad067","DOIUrl":"https://doi.org/10.1093/biomet/asad067","url":null,"abstract":"Summary Score-driven models have been recently introduced as a general framework to specify time-varying parameters of conditional densities. %The underlying idea is to specify a time-varying parameter as an autoregressive process with innovation given by the score of the associated log-likelihood. The score enjoys stochastic properties that make these models easy to implement and convenient to apply in several contexts, ranging from biostatistics to finance. Score-driven parameter updates have been shown to be optimal in terms of locally reducing a local version of the Kullback–Leibler divergence between the true conditional density and the postulated density of the model. A key limitation of such an optimality property is that it holds only locally both in the parameter space and sample space, yielding to a definition of local Kullback–Leibler divergence that is in fact not a divergence measure. The current paper shows that score-driven updates satisfy stronger optimality properties that are based on a global definition of Kullback–Leibler divergence. In particular, it is shown that score-driven updates reduce the distance between the expected updated parameter and the pseudo-true parameter. Furthermore, depending on the conditional density and the scaling of the score, the optimality result can hold globally over the parameter space, which can be viewed as a generalization of the monotonicity property of the stochastic gradient descent scheme. Several examples illustrate how the results derived in the paper apply to specific models under different easy-to-check assumptions, and provide a formal method to select the link-function and the scaling of the score.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135291655","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