Econometrics and Statistics最新文献_第7页

Exact Simulation of Max-Infinitely Divisible Processes 最大不可分割过程的精确模拟

IF 1.9

Econometrics and Statistics Pub Date : 2024-04-01 DOI: 10.1016/j.ecosta.2022.02.007

Peng Zhong , Raphaël Huser , Thomas Opitz

{"title":"Exact Simulation of Max-Infinitely Divisible Processes","authors":"Peng Zhong , Raphaël Huser , Thomas Opitz","doi":"10.1016/j.ecosta.2022.02.007","DOIUrl":"10.1016/j.ecosta.2022.02.007","url":null,"abstract":"<div>Max-infinitely divisible (max-id) processes play a central role in extreme-value theory and include the subclass of all max-stable processes. They allow for a constructive representation based on the pointwise maximum of random functions drawn from a Poisson point process defined on a suitable function space. Simulating from a max-id process is often difficult due to its complex stochastic structure, while calculating its joint density in high dimensions is often numerically infeasible. Therefore, exact and efficient simulation techniques for max-id processes are useful tools for studying the characteristics of the process and for drawing statistical inferences. Inspired by the simulation algorithms for max-stable processes, theory and algorithms to generalize simulation approaches tailored for certain flexible (existing or new) classes of max-id processes are presented. Efficient simulation for a large class of models can be achieved by implementing an adaptive rejection sampling scheme to sidestep a numerical integration step in the algorithm. The results of a simulation study highlight that our simulation algorithm works as expected and is highly accurate and efficient, such that it clearly outperforms customary approximate sampling schemes. As a by-product, new max-id models, which can be represented as pointwise maxima of general location-scale mixtures and possess flexible tail dependence structures capturing a wide range of asymptotic dependence scenarios, are also developed.</div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"30 ","pages":"Pages 96-109"},"PeriodicalIF":1.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85561550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A model specification test for semiparametric nonignorable missing data modeling 半参数非可变缺失数据建模的模型规范检验

IF 1.9

Econometrics and Statistics Pub Date : 2024-04-01 DOI: 10.1016/j.ecosta.2021.08.005

Cheng Yong Tang

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Tandem clustering with invariant coordinate selection 带有不变坐标选择的串联聚类

IF 1.9

Econometrics and Statistics Pub Date : 2024-03-16 DOI: 10.1016/j.ecosta.2024.03.002

Andreas Alfons, Aurore Archimbaud, Klaus Nordhausen, Anne Ruiz-Gazen

{"title":"Tandem clustering with invariant coordinate selection","authors":"Andreas Alfons, Aurore Archimbaud, Klaus Nordhausen, Anne Ruiz-Gazen","doi":"10.1016/j.ecosta.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.03.002","url":null,"abstract":"For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach using principal component analysis (PCA) has been criticized for focusing solely on inertia so that the first components do not necessarily retain the structure of interest for clustering. To address this limitation, a new tandem clustering approach based on invariant coordinate selection (ICS) is proposed. By jointly diagonalizing two scatter matrices, ICS is designed to find structure in the data while providing affine invariant components. Certain theoretical results have been previously derived and guarantee that under some elliptical mixture models, the group structure can be highlighted on a subset of the first and/or last components. However, ICS has garnered minimal attention within the context of clustering. Two challenges associated with ICS include choosing the pair of scatter matrices and selecting the components to retain. For effective clustering purposes, it is demonstrated that the best scatter pairs consist of one scatter matrix capturing the within-cluster structure and another capturing the global structure. For the former, local shape or pairwise scatters are of great interest, as is the minimum covariance determinant (MCD) estimator based on a carefully chosen subset size that is smaller than usual. The performance of ICS as a dimension reduction method is evaluated in terms of preserving the cluster structure in the data. In an extensive simulation study and empirical applications with benchmark data sets, various combinations of scatter matrices as well as component selection criteria are compared in situations with and without outliers. Overall, the new approach of tandem clustering with ICS shows promising results and clearly outperforms the PCA-based approach.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"24 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stein-Like Shrinkage Estimators for Coefficients of a Single-Equation in Simultaneous Equation Systems 同时方程系统中单项式系数的斯坦式收缩估计器

IF 1.9

Econometrics and Statistics Pub Date : 2024-03-08 DOI: 10.1016/j.ecosta.2024.03.001

A, l, i, , M, e, h, r, a, b, a, n, i

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Comments on “Challenges of cellwise outliers” by Jakob Raymaekers and Peter J. Rousseeuw 对 Jakob Raymaekers 和 Peter J. Rousseeuw 所作 "细胞离群值的挑战 "的评论

IF 1.9

Econometrics and Statistics Pub Date : 2024-02-24 DOI: 10.1016/j.ecosta.2024.02.003

Claudio Agostinelli

引用次数: 0

Challenges of cellwise outliers 单元离群值的挑战

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Econometrics and Statistics Pub Date : 2024-02-17 DOI: 10.1016/j.ecosta.2024.02.002

Jakob Raymaekers, Peter J. Rousseeuw

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Bayesian Nonparametric Multivariate Mixture of Autoregressive Processes with Application to Brain Signals 贝叶斯非参数多变量自回归过程混合物在脑信号中的应用

IF 1.9

Econometrics and Statistics Pub Date : 2024-02-17 DOI: 10.1016/j.ecosta.2024.01.004

Guillermo Granados-Garcia, Raquel Prado, Hernando Ombao

{"title":"Bayesian Nonparametric Multivariate Mixture of Autoregressive Processes with Application to Brain Signals","authors":"Guillermo Granados-Garcia, Raquel Prado, Hernando Ombao","doi":"10.1016/j.ecosta.2024.01.004","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.01.004","url":null,"abstract":"One of neuroscience’s goals is to study the interactions between different brain regions during rest and while performing specific cognitive tasks. Multivariate Bayesian autoregressive decomposition (MBMARD) is proposed as an intuitive and novel Bayesian non-parametric model to represent high-dimensional signals as a low-dimensional mixture of univariate uncorrelated latent oscillations. Each latent oscillation captures a specific underlying oscillatory activity and, hence, is modeled as a unique second-order autoregressive process due to a compelling property, namely, that its spectral density’s shape is characterized by a unique frequency peak and bandwidth, parameterized by a location and a scale parameter. The posterior distributions of the latent oscillation parameters are computed using a Metropolis-within-Gibbs algorithm. One of the advantages of the MBMARD model is its higher robustness against misspecification, compared with standard models. The main scientific questions addressed by the MBMARD model relate to the effects of long-term alcohol abuse on memory. These effects were examined by analyzing the electroencephalogram records of alcoholic and non-alcoholic subjects performing a visual recognition experiment. The MBMARD model yielded novel and interesting findings, including the identification of subject-specific clusters of low- and high-frequency oscillations in different brain regions.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"22 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139920274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rejoinder to the comment of Agostinelli 对阿戈斯蒂内利评论的反驳

IF 1.9

Econometrics and Statistics Pub Date : 2024-02-17 DOI: 10.1016/j.ecosta.2024.02.004

Jakob Raymaekers, Peter J. Rousseeuw

引用次数: 0

Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process 利用分层 Dirichlet 过程对单细胞 RNA 测序数据集进行共享差异聚类

IF 1.9

Econometrics and Statistics Pub Date : 2024-02-17 DOI: 10.1016/j.ecosta.2024.02.001

Jinlu Liu, Sara Wade, Natalia Bochkina

{"title":"Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process","authors":"Jinlu Liu, Sara Wade, Natalia Bochkina","doi":"10.1016/j.ecosta.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.02.001","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) is a powerful technology that allows researchers to understand gene expression patterns at the single-cell level and uncover the heterogeneous nature of cells. Clustering is an important tool in scRNA-seq analysis to discover groups of cells with similar gene expression patterns and identify potential cell types. Integration of multiple scRNA-seq datasets is a pressing challenge, and in this direction, a novel model is developed to extend clustering methods to appropriately combine inference across multiple datasets. The model simultaneously addresses normalization to deal with the inherent noise and uncertainty in scRNA-seq, infers cell types, and integrates multiple datasets for shared clustering in principled manner through a hierarchical Bayesian framework. A Gibbs sampler is developed that copes with the high-dimensionality of scRNA-seq through consensus clustering. The methodological developments are driven by experimental data from embryonic cells, with the aim of understanding the role of PAX6 in prenatal development, and more specifically how cell-subtypes and their proportions change when knocking out this factor.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"56 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139920378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariate Hermite polynomials and information matrix tests 多元赫米特多项式和信息矩阵检验

IF 1.9

Econometrics and Statistics Pub Date : 2024-02-06 DOI: 10.1016/j.ecosta.2024.01.005

Dante Amengual, Gabriele Fiorentini, Enrique Sentana

引用次数: 0