Statistics and Its Interface最新文献_第8页

Weakly informative priors and prior-data conflict checking for likelihood-free inference 用于无似然推理的弱信息先验和先验数据冲突检查

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2022-02-21 DOI: 10.4310/22-sii733

Atlanta Chakraborty, D. Nott, Michael Evans

{"title":"Weakly informative priors and prior-data conflict checking for likelihood-free inference","authors":"Atlanta Chakraborty, D. Nott, Michael Evans","doi":"10.4310/22-sii733","DOIUrl":"https://doi.org/10.4310/22-sii733","url":null,"abstract":"Bayesian likelihood-free inference, which is used to perform Bayesian inference when the likelihood is intractable, enjoys an increasing number of important scientific applications. However, many aspects of a Bayesian analysis become more challenging in the likelihood-free setting. One example of this is prior-data conflict checking, where the goal is to assess whether the information in the data and the prior are inconsistent. Conflicts of this kind are important to detect, since they may reveal problems in an investigator's understanding of what are relevant values of the parameters, and can result in sensitivity of Bayesian inferences to the prior. Here we consider methods for prior-data conflict checking which are applicable regardless of whether the likelihood is tractable or not. In constructing our checks, we consider checking statistics based on prior-to-posterior Kullback-Leibler divergences. The checks are implemented using mixture approximations to the posterior distribution and closed-form approximations to Kullback-Leibler divergences for mixtures, which make Monte Carlo approximation of reference distributions for calibration computationally feasible. When prior-data conflicts occur, it is useful to consider weakly informative prior specifications in alternative analyses as part of a sensitivity analysis. As a main application of our methodology, we develop a technique for searching for weakly informative priors in likelihood-free inference, where the notion of a weakly informative prior is formalized using prior-data conflict checks. The methods are demonstrated in three examples.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43377766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Causal measures using generalized difference-in-difference approach with nonlinear models 非线性模型下广义差中差法的因果度量

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2022-01-01 DOI: 10.4310/21-sii704

Marcelo M. Taddeo, Leila D. Amorim, R. Aquino

引用次数: 4

Sufficient dimension reduction for spatial point processes using weighted principal support vector machines 基于加权主支持向量机的空间点过程充分降维

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2022-01-01 DOI: 10.4310/21-sii705

Subha Datta, J. Loh

引用次数: 0

Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data. 区域参考功能脑电数据的协变量调整混合主成分分析。

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2022-01-01 DOI: 10.4310/21-sii712

A. Scheffler, A. Dickinson, Charlotte DiStefano, S. Jeste, D. Şentürk

{"title":"Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data.","authors":"A. Scheffler, A. Dickinson, Charlotte DiStefano, S. Jeste, D. Şentürk","doi":"10.4310/21-sii712","DOIUrl":"https://doi.org/10.4310/21-sii712","url":null,"abstract":"Electroencephalography (EEG) studies produce region-referenced functional data via EEG signals recorded across scalp electrodes. The high-dimensional data can be used to contrast neurodevelopmental trajectories between diagnostic groups, for example between typically developing (TD) children and children with autism spectrum disorder (ASD). Valid inference requires characterization of the complex EEG dependency structure as well as covariate-dependent heteroscedasticity, such as changes in variation over developmental age. In our motivating study, EEG data is collected on TD and ASD children aged two to twelve years old. The peak alpha frequency, a prominent peak in the alpha spectrum, is a biomarker linked to neurodevelopment that shifts as children age. To retain information, we model patterns of alpha spectral variation, rather than just the peak location, regionally across the scalp and chronologically across development. We propose a covariate-adjusted hybrid principal components analysis (CA-HPCA) for EEG data, which utilizes both vector and functional principal components analysis while simultaneously adjusting for covariate-dependent heteroscedasticity. CA-HPCA assumes the covariance process is weakly separable conditional on observed covariates, allowing for covariate-adjustments to be made on the marginal covariances rather than the full covariance leading to stable and computationally efficient estimation. The proposed methodology provides novel insights into neurodevelopmental differences between TD and ASD children.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"15 2 1","pages":"209-223"},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71151916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

The more data, the better? Demystifying deletion-based methods in linear regression with missing data. 数据越多越好?揭示缺失数据线性回归中基于删除的方法。

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2022-01-01 DOI: 10.4310/21-sii717

Tianchen Xu, Kun Chen, Gen Li

{"title":"The more data, the better? Demystifying deletion-based methods in linear regression with missing data.","authors":"Tianchen Xu, Kun Chen, Gen Li","doi":"10.4310/21-sii717","DOIUrl":"https://doi.org/10.4310/21-sii717","url":null,"abstract":"<p><p>We compare two deletion-based methods for dealing with the problem of missing observations in linear regression analysis. One is the complete-case analysis (CC, or listwise deletion) that discards all incomplete observations and only uses common samples for ordinary least-squares estimation. The other is the available-case analysis (AC, or pairwise deletion) that utilizes all available data to estimate the covariance matrices and applies these matrices to construct the normal equation. We show that the estimates from both methods are asymptotically unbiased under missing completely at random (MCAR) and further compare their asymptotic variances in some typical situations. Surprisingly, using more data (i.e., AC) does not necessarily lead to better asymptotic efficiency in many scenarios. Missing patterns, covariance structure and true regression coefficient values all play a role in determining which is better. We further conduct simulation studies to corroborate the findings and demystify what has been missed or misinterpreted in the literature. Some detailed proofs and simulation results are available in the online supplemental materials.</p>","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"15 4","pages":"515-526"},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762692/pdf/nihms-1855165.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10780629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Estimation of Hilbertian varying coefficient models 希尔伯特变系数模型的估计

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2022-01-01 DOI: 10.4310/20-sii651

H. Hong, Dongwoo Kim, Young K. Lee, Byeong-U Park

引用次数: 1

Sparsity-restricted estimation for the accelerated failure time model 加速失效时间模型的稀疏性约束估计

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2022-01-01 DOI: 10.4310/21-SII669

Xiaoyu Zhang, Yunpeng Zhou, Jinfeng Xu, K. Yuen

引用次数: 0

Discussion of “Estimation of Hilbertian varying coefficient models” 关于“希尔伯特变系数模型估计”的讨论