Statistical Modelling最新文献_第6页

A time-varying GARCH mixed-effects model for isolating high- and low- frequency volatility and co-volatility 一种时变GARCH混合效应模型，用于隔离高、低频波动和共波动

IF 1 4区数学

Statistical Modelling Pub Date : 2022-03-14 DOI: 10.1177/1471082x221080488

Zeynab Aghabazaz, I. Kazemi, A. Nematollahi

{"title":"A time-varying GARCH mixed-effects model for isolating high- and low- frequency volatility and co-volatility","authors":"Zeynab Aghabazaz, I. Kazemi, A. Nematollahi","doi":"10.1177/1471082x221080488","DOIUrl":"https://doi.org/10.1177/1471082x221080488","url":null,"abstract":"This article studies long-term, short-term volatility and co-volatility in stock markets by introducing modelling strategies to the multivariate data analysis that deal with serially correlated innovations and cross-section dependence. In particular, it presents an innovative mixed-effects model through a GARCH process, allowing for heterogeneity effects and time-series dynamics. We propose a non-parametric regression model of the penalized low-rank smoothing spline to present time trends into the variance and covariance equations. The strategy provides flexible modelling of the low-frequency volatility and co-volatility in equity markets. The decomposed low-frequency matrix was modelled using the modified Cholesky factorization. The Hamiltonian Monte Carlo technique is implemented as a Bayesian computing process for estimating parameters and latent factors. The advantage of our modelling strategy in empirical studies is highlighted by examining the effect of latent financial factors on a panel across 10 equities over 110 weekly series. The model can differentiate non-parametrically dynamic patterns of high and low frequencies of variance–covariance structural equations and incorporate economic features to predict variabilities in stock markets regarding time-series evidence.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42631773","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

Bayesian modelling of integer-valued transfer function models 整值传递函数模型的贝叶斯建模

IF 1 4区数学

Statistical Modelling Pub Date : 2022-03-01 DOI: 10.1177/1471082x221075477

Aljo Clair Pingal, Cathy W. S. Chen

{"title":"Bayesian modelling of integer-valued transfer function models","authors":"Aljo Clair Pingal, Cathy W. S. Chen","doi":"10.1177/1471082x221075477","DOIUrl":"https://doi.org/10.1177/1471082x221075477","url":null,"abstract":"External events are commonly known as interventions that often affect times series of counts. This research introduces a class of transfer function models that include four different types of interventions on integer-valued time series: abrupt start and abrupt decay (additive outlier), abrupt start and gradual decay (transient shift), abrupt start and permanent effect (level shift) and gradual start and permanent effect. We propose integer-valued transfer function models incorporating a generalized Poisson, log-linear generalized Poisson or negative binomial to estimate and detect these four types of interventions in a time series of counts. Utilizing Bayesian methods, which are adaptive Markov chain Monte Carlo (MCMC) algorithms to obtain the estimation, we further employ deviance information criterion (DIC), posterior odd ratios and mean squared standardized residual for model comparisons. As an illustration, this study evaluates the effectiveness of our methods through a simulation study and application to crime data in Albury City, New South Wales (NSW) Australia. Simulation results show that the MCMC procedure is reasonably effective. The empirical outcome also reveals that the proposed models are able to successfully detect the locations and type of interventions.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45000599","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: 3

Power logit regression for modeling bounded data 建模有界数据的幂logit回归

IF 1 4区数学

Statistical Modelling Pub Date : 2022-02-03 DOI: 10.1177/1471082x221140157

Francisco F. Queiroz, S. Ferrari

引用次数: 0

Editorial 编辑

IF 1 4区数学

Statistical Modelling Pub Date : 2022-02-01 DOI: 10.1177/1471082x211051203

Vicente Núñez-Antón, Andrea Mayr, F. Bartolucci

引用次数: 0

Guest Editorial: Special Issue in Honour of Murray Aitkin 客座编辑：纪念Murray Aitkin特刊

IF 1 4区数学

Statistical Modelling Pub Date : 2022-02-01 DOI: 10.1177/1471082X221076884

B. Francis, J. Hinde

引用次数: 0

Obituary: Brian Douglas Marx 讣告:布莱恩·道格拉斯·马克思

IF 1 4区数学

Statistical Modelling Pub Date : 2022-02-01 DOI: 10.1177/1471082x211072584

Paul H. C. Eilers, E. Lesaffre

引用次数: 0

Statistical modelling of COVID-19 data: Putting generalized additive models to work 新冠肺炎数据的统计建模：使广义加性模型发挥作用

IF 1 4区数学

Statistical Modelling Pub Date : 2022-01-04 DOI: 10.1177/1471082X221124628

Cornelius Fritz, Giacomo De Nicola, Martje Rave, M. Weigert, Yegane Khazaei, U. Berger, Helmut Kuchenhoff, G. Kauermann

{"title":"Statistical modelling of COVID-19 data: Putting generalized additive models to work","authors":"Cornelius Fritz, Giacomo De Nicola, Martje Rave, M. Weigert, Yegane Khazaei, U. Berger, Helmut Kuchenhoff, G. Kauermann","doi":"10.1177/1471082X221124628","DOIUrl":"https://doi.org/10.1177/1471082X221124628","url":null,"abstract":"Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter estimates are independent of the (unknown) case-detection ratio, which plays an important role in COVID-19 surveillance data. Second, we model the incidence of hospitalizations, for which data is only available with a temporal delay. We illustrate how correcting for this reporting delay through a nowcasting procedure can be naturally incorporated into the GAM framework as an offset term. Third, we propose a multinomial model for the weekly occupancy of intensive care units (ICU), where we distinguish between the number of COVID-19 patients, other patients and vacant beds. With these three examples, we aim to showcase the practical and ‘off-the-shelf’ applicability of GAMs to gain new insights from real-world data.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48853286","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

Dynamic modelling of corporate credit ratings and defaults 企业信用评级和违约的动态建模

IF 1 4区数学

Statistical Modelling Pub Date : 2021-12-17 DOI: 10.1177/1471082X211057610

Laura Vana, K. Hornik

引用次数: 0

Nonlinear discrete-time hazard models for women's entry into marriage 女性结婚的非线性离散时间风险模型

IF 1 4区数学

Statistical Modelling Pub Date : 2021-12-14 DOI: 10.1177/1471082X211062651

H. Turner, A. Batchelor, D. Firth

引用次数: 0

Smoothing spatio-temporal data with complex missing data patterns 用复杂的缺失数据模式平滑时空数据

IF 1 4区数学

Statistical Modelling Pub Date : 2021-12-02 DOI: 10.1177/1471082X211057959

Eleonora Arnone, L. Sangalli, A. Vicini

{"title":"Smoothing spatio-temporal data with complex missing data patterns","authors":"Eleonora Arnone, L. Sangalli, A. Vicini","doi":"10.1177/1471082X211057959","DOIUrl":"https://doi.org/10.1177/1471082X211057959","url":null,"abstract":"We consider spatio-temporal data and functional data with spatial dependence, characterized by complicated missing data patterns. We propose a new method capable to efficiently handle these data structures, including the case where data are missing over large portions of the spatio-temporal domain. The method is based on regression with partial differential equation regularization. The proposed model can accurately deal with data scattered over domains with irregular shapes and can accurately estimate fields exhibiting complicated local features. We demonstrate the consistency and asymptotic normality of the estimators. Moreover, we illustrate the good performances of the method in simulations studies, considering different missing data scenarios, from sparse data to more challenging scenarios where the data are missing over large portions of the spatial and temporal domains and the missing data are clustered in space and/or in time. The proposed method is compared to competing techniques, considering predictive accuracy and uncertainty quantification measures. Finally, we show an application to the analysis of lake surface water temperature data, that further illustrates the ability of the method to handle data featuring complicated patterns of missingness and highlights its potentiality for environmental studies.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48209900","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