Econometrics and Statistics最新文献_第3页

Modeling Turning Points in the Global Equity Market 模拟全球股票市场的转折点

IF 1.9

Econometrics and Statistics Pub Date : 2024-04-01 DOI: 10.1016/j.ecosta.2021.10.004

Daniel Felix Ahelegbey , Monica Billio , Roberto Casarin

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Data segmentation algorithms: Univariate mean change and beyond 数据分割算法：单变量均值变化及其他

IF 1.9

Econometrics and Statistics Pub Date : 2024-04-01 DOI: 10.1016/j.ecosta.2021.10.008

Haeran Cho , Claudia Kirch

{"title":"Data segmentation algorithms: Univariate mean change and beyond","authors":"Haeran Cho , Claudia Kirch","doi":"10.1016/j.ecosta.2021.10.008","DOIUrl":"10.1016/j.ecosta.2021.10.008","url":null,"abstract":"<div>Data segmentation a.k.a. multiple change point analysis has received considerable attention due to its importance in time series analysis and signal processing, with applications in a variety of fields including natural and social sciences, medicine, engineering and finance. The first part reviews the existing literature on the canonical data segmentation problem which aims at detecting and localising multiple change points in the mean of univariate time series. An overview of popular methodologies is provided on their computational complexity and theoretical properties. In particular, the theoretical discussion focuses on the separation rate relating to which change points are detectable by a given procedure, and the localisation rate quantifying the precision of corresponding change point estimators, and a distinction is made whether a homogeneous or multiscale viewpoint has been adopted in their derivation. It is further highlighted that the latter viewpoint provides the most general setting for investigating the optimality of data segmentation algorithms.Arguably, the canonical segmentation problem has been the most popular framework to propose new data segmentation algorithms and study their efficiency in the last decades. The second part of this survey motivates the importance of attaining an in-depth understanding of strengths and weaknesses of methodologies for the change point problem in a simpler, univariate setting, as a stepping stone for the development of methodologies for more complex problems. This point is illustrated with a range of examples showcasing the connections between complex distributional changes and those in the mean. Extensions towards high-dimensional change point problems are also discussed where it is demonstrated that the challenges arising from high dimensionality are orthogonal to those in dealing with multiple change points.</div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"30 ","pages":"Pages 76-95"},"PeriodicalIF":1.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76584108","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

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}

引用次数: 0

Partially one-sided semiparametric inference for trending persistent and antipersistent processes 持续和反持续过程趋势的部分单边半参数推理

IF 1.9

Econometrics and Statistics Pub Date : 2024-04-01 DOI: 10.1016/j.ecosta.2021.12.007

Karim M. Abadir , Walter Distaso , Liudas Giraitis

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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

引用次数: 0

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

引用次数: 0

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