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Estimation for vector autoregressive model under multivariate skew-t-normal innovations 多元倾斜-t-正态创新下的向量自回归模型估计
IF 1 4区 数学
Statistical Modelling Pub Date : 2024-02-15 DOI: 10.1177/1471082x231224910
U. Nduka, E. O. Ossai, M. Madukaife, T. E. Ugah
{"title":"Estimation for vector autoregressive model under multivariate skew-t-normal innovations","authors":"U. Nduka, E. O. Ossai, M. Madukaife, T. E. Ugah","doi":"10.1177/1471082x231224910","DOIUrl":"https://doi.org/10.1177/1471082x231224910","url":null,"abstract":"Current procedures for estimating the parameters of [Formula: see text]th order vector autoregressive (VAR [Formula: see text]) model are usually based on assuming that the ensuing error distribution is multivariate normal. But there exists large body of evidence that several data encountered in real life are skewed; thereby making estimators derived based on normality assumption not suitable in such scenarios. This prompts for the search of appropriate methods for skewed distributions. Therefore, this article proposes estimators for the mean and covariance matrices of the [Formula: see text] model under multivariate skew- [Formula: see text]-normal (MSTN) distribution. Also, estimators for the shape and skewness parameters are provided. The expectation conditional maximization (ECM) and its extension the expectation conditional maximization either (ECME) algorithms are the tools used to derive the estimators. The performance of the estimators were examined through extensive simulations, and results show that they compete favourably with other numerical methods especially when the underlying distribution is skewed. The usefulness of our estimators was illustrated using a real data set on some US economic indicators. The VAR [Formula: see text] model under MSTN distribution provides a good fit, better than [Formula: see text] model under the assumption of normality.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139833961","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: 0
Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients 整合联合潜类混合模型和贝叶斯网络揭示 COVID-19 患者的临床亚群
IF 1 4区 数学
Statistical Modelling Pub Date : 2024-02-08 DOI: 10.1177/1471082x231222746
Federica Cugnata, C. Brombin, Pietro E. Cippà, Alessandro Ceschi, P. Ferrari, C. Di Serio
{"title":"Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients","authors":"Federica Cugnata, C. Brombin, Pietro E. Cippà, Alessandro Ceschi, P. Ferrari, C. Di Serio","doi":"10.1177/1471082x231222746","DOIUrl":"https://doi.org/10.1177/1471082x231222746","url":null,"abstract":"When modelling the dynamics of biomarkers in biomedical studies, it is essential to identify homogeneous clusters of patients and analyse them from a precision medicine perspective. This need has emerged as crucial and urgent during the COVID-19 pandemic: early understanding of symptoms and patient heterogeneity has significant implications for prevention, early diagnosis, effective management, and treatment. Additionally, biomarker progression may be associated with clinically relevant time-toevent data. Therefore, statistical models are necessary to gain insight into complex disease mechanisms by properly accounting for unobservable heterogeneity in patients while jointly modelling longitudinal and time-to-event data. In this study, we leverage the key features of Latent Class modelling and Bayesian Network approaches and propose a unified framework to (a) uncover homogeneous subgroups of patients concerning their longitudinal and survival data and (b) describe patient subgroups within a multivariate framework.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139853563","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: 0
Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients 整合联合潜类混合模型和贝叶斯网络揭示 COVID-19 患者的临床亚群
IF 1 4区 数学
Statistical Modelling Pub Date : 2024-02-08 DOI: 10.1177/1471082x231222746
Federica Cugnata, C. Brombin, Pietro E. Cippà, Alessandro Ceschi, P. Ferrari, C. Di Serio
{"title":"Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients","authors":"Federica Cugnata, C. Brombin, Pietro E. Cippà, Alessandro Ceschi, P. Ferrari, C. Di Serio","doi":"10.1177/1471082x231222746","DOIUrl":"https://doi.org/10.1177/1471082x231222746","url":null,"abstract":"When modelling the dynamics of biomarkers in biomedical studies, it is essential to identify homogeneous clusters of patients and analyse them from a precision medicine perspective. This need has emerged as crucial and urgent during the COVID-19 pandemic: early understanding of symptoms and patient heterogeneity has significant implications for prevention, early diagnosis, effective management, and treatment. Additionally, biomarker progression may be associated with clinically relevant time-toevent data. Therefore, statistical models are necessary to gain insight into complex disease mechanisms by properly accounting for unobservable heterogeneity in patients while jointly modelling longitudinal and time-to-event data. In this study, we leverage the key features of Latent Class modelling and Bayesian Network approaches and propose a unified framework to (a) uncover homogeneous subgroups of patients concerning their longitudinal and survival data and (b) describe patient subgroups within a multivariate framework.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793820","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: 0
Impact of jittering on raster- and distance-based geostatistical analyses of DHS data 抖动对基于栅格和距离的人口与健康调查数据地理统计分析的影响
IF 1 4区 数学
Statistical Modelling Pub Date : 2024-02-07 DOI: 10.1177/1471082x231219847
Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad
{"title":"Impact of jittering on raster- and distance-based geostatistical analyses of DHS data","authors":"Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad","doi":"10.1177/1471082x231219847","DOIUrl":"https://doi.org/10.1177/1471082x231219847","url":null,"abstract":"Fine-scale covariate rasters are routinely used in geostatistical models for mapping demographic and health indicators based on household surveys from the Demographic and Health Surveys (DHS) program. However, the geostatistical analyses ignore the fact that GPS coordinates in DHS surveys are jittered for privacy purposes. We demonstrate the need to account for this jittering, and we propose a computationally efficient approach that can be routinely applied. We use the new method to analyse the prevalence of completion of secondary education for 20-49 year old women in Nigeria in 2018 based on the 2018 DHS survey. The analysis demonstrates substantial changes in the estimates of spatial range and fixed effects compared to when we ignore jittering. Through a simulation study that mimics the dataset, we demonstrate that accounting for jittering reduces attenuation in the estimated coefficients for covariates and improves predictions. The results also show that the common approach of averaging covariate values in windows around the observed locations does not lead to the same improvements as accounting for jittering.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949298","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: 0
A combined overdispersed longitudinal model for nominal data 名义数据的综合过度分散纵向模型
IF 1 4区 数学
Statistical Modelling Pub Date : 2023-12-21 DOI: 10.1177/1471082x231209361
R. K. Sercundes, G. Molenberghs, G. Verbeke, Clarice G.B. Demétrio, Sila C. da Silva, Rafael A. Moral
{"title":"A combined overdispersed longitudinal model for nominal data","authors":"R. K. Sercundes, G. Molenberghs, G. Verbeke, Clarice G.B. Demétrio, Sila C. da Silva, Rafael A. Moral","doi":"10.1177/1471082x231209361","DOIUrl":"https://doi.org/10.1177/1471082x231209361","url":null,"abstract":"Longitudinal studies involving nominal outcomes are carried out in various scientific areas. These outcomes are frequently modelled using a generalized linear mixed modelling (GLMM) framework. This widely used approach allows for the modelling of the hierarchy in the data to accommodate different degrees of overdispersion. In this article, a combined model (CM) that takes into account overdispersion and clustering through two separate sets of random effects is formulated. Maximum likelihood estimation with analytic-numerical integration is used to estimate the model parameters. To examine the relative performance of the CM and the GLMM, simulation studies were carried out, exploring scenarios with different sample sizes, types of random effects, and overdispersion. Both models were applied to a real dataset obtained from an experiment in agriculture. We also provide an implementation of these models through SAS code.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138948041","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: 0
A flexible Bayesian hierarchical quantile spatial model for areal data 用于地形数据的灵活贝叶斯分层量化空间模型
IF 1 4区 数学
Statistical Modelling Pub Date : 2023-12-21 DOI: 10.1177/1471082x231204930
Rafael Cabral Fernandez, Kelly Cristina Mota Gonçalves, João Batista de Morais Pereira
{"title":"A flexible Bayesian hierarchical quantile spatial model for areal data","authors":"Rafael Cabral Fernandez, Kelly Cristina Mota Gonçalves, João Batista de Morais Pereira","doi":"10.1177/1471082x231204930","DOIUrl":"https://doi.org/10.1177/1471082x231204930","url":null,"abstract":"This article introduces a new class of nested models that extends the literature standard combination of spatial autoregressive model for areal data with parametric quantile regression by considering an asymmetric Laplace distribution for the random errors. In addition to being more flexible, the new proposed model can incorporate a hierarchical structure, allowing it to deal with clustered data. Such an approach produces a robust statistical method for modeling the quantiles of areal data distributed in a geographically hierarchical setting. The proposed non-hierarchical model is evaluated using a wellknown house pricing dataset and a simulation study. In addition, its hierarchical version is applied to a real dataset of math scores related to public high schools within the metropolitan area of Rio de Janeiro, Brazil.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138950479","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: 0
A joint normal-binary (probit) model for high-dimensional longitudinal data 高维纵向数据的正态-二元（probit）联合模型
IF 1 4区 数学
Statistical Modelling Pub Date : 2023-12-08 DOI: 10.1177/1471082x231202341
Margaux Delporte, Steffen Fieuws, G. Molenberghs, G. Verbeke, D. De Coninck, Vera Hoorens
{"title":"A joint normal-binary (probit) model for high-dimensional longitudinal data","authors":"Margaux Delporte, Steffen Fieuws, G. Molenberghs, G. Verbeke, D. De Coninck, Vera Hoorens","doi":"10.1177/1471082x231202341","DOIUrl":"https://doi.org/10.1177/1471082x231202341","url":null,"abstract":"In many biomedical studies multiple responses are collected over time, which results in highdimensional longitudinal data. It is often of interest to model the continuous and binary responses jointly, which can be done with joint generalized mixed models in which the association is modelled through random effects. Investigating the association between the responses is often limited to scrutinizing the correlations between the latent random effects. In this article, this approach is extended by deriving closed-form formulas for the manifest correlations (and corresponding standard errors), which reflects the correlation between the observed responses as observed. In addition, the marginal joint model is constructed, from which predictions of subvectors of one response conditional on subvectors of other response(s) and potentially a subvector of the history of the response can be derived. Corresponding prediction and confidence intervals are constructed. Two case studies are discussed, in which further pseudo-likelihood methodology is applied to reduce the computational complexity.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589253","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: 0
Robust function-on-function interaction regression 鲁棒函数对函数交互回归
4区 数学
Statistical Modelling Pub Date : 2023-10-23 DOI: 10.1177/1471082x231198907
Ufuk Beyaztas, Han Lin Shang, Abhijit Mandal
{"title":"Robust function-on-function interaction regression","authors":"Ufuk Beyaztas, Han Lin Shang, Abhijit Mandal","doi":"10.1177/1471082x231198907","DOIUrl":"https://doi.org/10.1177/1471082x231198907","url":null,"abstract":"A function-on-function regression model with quadratic and interaction effects of the covariates provides a more flexible model. Despite several attempts to estimate the model’s parameters, almost all existing estimation strategies are non-robust against outliers. Outliers in the quadratic and interaction effects may deteriorate the model structure more severely than their effects in the main effect. We propose a robust estimation strategy based on the robust functional principal component decomposition of the function-valued variables and [Formula: see text]-estimator. The performance of the proposed method relies on the truncation parameters in the robust functional principal component decomposition of the function-valued variables. A robust Bayesian information criterion is used to determine the optimum truncation constants. A forward stepwise variable selection procedure is employed to determine relevant main, quadratic, and interaction effects to address a possible model misspecification. The finite-sample performance of the proposed method is investigated via a series of Monte-Carlo experiments. The proposed method’s asymptotic consistency and influence function are also studied in the supplement, and its empirical performance is further investigated using a U.S. COVID-19 dataset.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367709","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: 0
Ordinal compositional data and time series 有序成分数据和时间序列
4区 数学
Statistical Modelling Pub Date : 2023-10-05 DOI: 10.1177/1471082x231190971
Christian H. Weiß
{"title":"Ordinal compositional data and time series","authors":"Christian H. Weiß","doi":"10.1177/1471082x231190971","DOIUrl":"https://doi.org/10.1177/1471082x231190971","url":null,"abstract":"There are several real applications where the categories behind compositional data (CoDa) exhibit a natural order, which, however, is not accounted for by existing CoDa methods. For various application areas, it is demonstrated that appropriately developed methods for ordinal CoDa provide valuable additional insights and are, thus, recommended to complement existing CoDa methods. The potential benefits are demonstrated for the (visual) descriptive analysis of ordinal CoDa, for statistical inference based on CoDa samples, for the monitoring of CoDa processes by means of control charts, and for the analysis and modelling of compositional time series. The novel methods are illustrated by a couple of real-world data examples.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134977792","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: 0
Editorial to the Special Issue “Applications of P-Splines” in Memory of Brian D. Marx 纪念布赖恩·d·马克思的特刊“p样条的应用”社论
4区 数学
Statistical Modelling Pub Date : 2023-10-01 DOI: 10.1177/1471082x231201705
Paul H.C. Eilers, Thomas Kneib
{"title":"Editorial to the Special Issue “Applications of P-Splines” in Memory of Brian D. Marx","authors":"Paul H.C. Eilers, Thomas Kneib","doi":"10.1177/1471082x231201705","DOIUrl":"https://doi.org/10.1177/1471082x231201705","url":null,"abstract":"","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135965350","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: 0
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