Statistical Modelling最新文献_第9页

Quantile regression for longitudinal data via the multivariate generalized hyperbolic distribution 基于多元广义双曲分布的纵向数据的分位数回归

IF 1 4区数学

Statistical Modelling Pub Date : 2021-06-07 DOI: 10.1177/1471082X211015454

Alvaro J. Flórez, I. Van Keilegom, G. Molenberghs, A. Verhasselt

引用次数: 0

Interactively visualizing distributional regression models with distreg.vis 分布式回归模型的交互式可视化

IF 1 4区数学

Statistical Modelling Pub Date : 2021-05-27 DOI: 10.1177/1471082X211007308

Stanislaus Stadlmann, T. Kneib

{"title":"Interactively visualizing distributional regression models with distreg.vis","authors":"Stanislaus Stadlmann, T. Kneib","doi":"10.1177/1471082X211007308","DOIUrl":"https://doi.org/10.1177/1471082X211007308","url":null,"abstract":"A newly emerging field in statistics is distributional regression, where not only the mean but each parameter of a parametric response distribution can be modelled using a set of predictors. As an extension of generalized additive models, distributional regression utilizes the known link functions (log, logit, etc.), model terms (fixed, random, spatial, smooth, etc.) and available types of distributions but allows us to go well beyond the exponential family and to model potentially all distributional parameters. Due to this increase in model flexibility, the interpretation of covariate effects on the shape of the conditional response distribution, its moments and other features derived from this distribution is more challenging than with traditional mean-based methods. In particular, such quantities of interest often do not directly equate the modelled parameters but are rather a (potentially complex) combination of them. To ease the post-estimation model analysis, we propose a framework and subsequently feature an implementation in R for the visualization of Bayesian and frequentist distributional regression models fitted using the bamlss, gamlss and betareg R packages.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X211007308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43253762","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: 4

Bayesian adjustment for measurement error in an offset variable in a Poisson regression model 泊松回归模型中偏移变量测量误差的贝叶斯平差

IF 1 4区数学

Statistical Modelling Pub Date : 2021-05-24 DOI: 10.1177/1471082X211008011

Kangjie Zhang, Juxin Liu, Yang Liu, Peng Zhang, R. Carroll

{"title":"Bayesian adjustment for measurement error in an offset variable in a Poisson regression model","authors":"Kangjie Zhang, Juxin Liu, Yang Liu, Peng Zhang, R. Carroll","doi":"10.1177/1471082X211008011","DOIUrl":"https://doi.org/10.1177/1471082X211008011","url":null,"abstract":"Fatal car crashes are the leading cause of death among teenagers in the USA. The Graduated Driver Licensing (GDL) programme is one effective policy for reducing the number of teen fatal car crashes. Our study focuses on the number of fatal car crashes in Michigan during 1990–2004 excluding 1997, when the GDL started. We use Poisson regression with spatially dependent random effects to model the county level teen car crash counts. We develop a measurement error model to account for the fact that the total teenage population in the county level is used as a proxy for the teenage driver population. To the best of our knowledge, there is no existing literature that considers adjustment for measurement error in an offset variable. Furthermore, limited work has addressed the measurement errors in the context of spatial data. In our modelling, a Berkson measurement error model with spatial random effects is applied to adjust for the error-prone offset variable in a Bayesian paradigm. The Bayesian Markov chain Monte Carlo (MCMC) sampling is implemented in rstan. To assess the consequence of adjusting for measurement error, we compared two models with and without adjustment for measurement error. We found the effect of a time indicator becomes less significant with the measurement-error adjustment. It leads to our conclusion that the reduced number of teen drivers can help explain, to some extent, the effectiveness of GDL.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X211008011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44696177","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: 1

Mixed effect modelling and variable selection for quantile regression 分位数回归的混合效应建模和变量选择

IF 1 4区数学

Statistical Modelling Pub Date : 2021-04-17 DOI: 10.1177/1471082X211033490

H. Bar, J. Booth, M. Wells

引用次数: 0

Modelling changes over time in a multivariate paired comparison: An application to window display design 在多变量配对比较中建模随时间的变化:在窗口显示设计中的应用

IF 1 4区数学

Statistical Modelling Pub Date : 2021-03-31 DOI: 10.1177/1471082X21995675

A. Grand, R. Dittrich

{"title":"Modelling changes over time in a multivariate paired comparison: An application to window display design","authors":"A. Grand, R. Dittrich","doi":"10.1177/1471082X21995675","DOIUrl":"https://doi.org/10.1177/1471082X21995675","url":null,"abstract":"This article proposes an alternative method of making comparative judgements in multivariate paired comparisons (PCs) where judgements about change are made directly by comparing an object at two time points for each of a series of attributes. The application deals with the design of shop window displays where products should be arranged by teams of vocational students according to aesthetic principles (attributes). The photos of the students’ window displays at time 1 (before feedback) and at time 2 (after feedback) were compared by judging each attribute as to whether it was fulfilled better at time 1 or at time 2. An advantage of this PC approach over an alternative of a scoring system is the possibility to assess even subtle changes of various aspects of attractiveness, which cannot easily be measured using a score. To analyse these data, we used earlier work which developed both a multivariate PC pattern model for multi-attribute data and a PC model over time and defined a multivariate PC model of changes (MPCC). The model can be fitted as a non-standard Poisson log-linear model and provides estimates of change for the three attributes for time 2 and we were able to check for possible interaction effects between these attributes.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X21995675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42838067","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

Multivariate functional additive mixed models 多元函数加性混合模型

IF 1 4区数学

Statistical Modelling Pub Date : 2021-03-11 DOI: 10.1177/1471082X211056158

A. Volkmann, Almond Stöcker, F. Scheipl, S. Greven

{"title":"Multivariate functional additive mixed models","authors":"A. Volkmann, Almond Stöcker, F. Scheipl, S. Greven","doi":"10.1177/1471082X211056158","DOIUrl":"https://doi.org/10.1177/1471082X211056158","url":null,"abstract":"Multivariate functional data can be intrinsically multivariate like movement trajectories in 2D or complementary such as precipitation, temperature and wind speeds over time at a given weather station. We propose a multivariate functional additive mixed model (multiFAMM) and show its application to both data situations using examples from sports science (movement trajectories of snooker players) and phonetic science (acoustic signals and articulation of consonants). The approach includes linear and nonlinear covariate effects and models the dependency structure between the dimensions of the responses using multivariate functional principal component analysis. Multivariate functional random intercepts capture both the auto-correlation within a given function and cross-correlations between the multivariate functional dimensions. They also allow us to model between-function correlations as induced by, for example, repeated measurements or crossed study designs. Modelling the dependency structure between the dimensions can generate additional insight into the properties of the multivariate functional process, improves the estimation of random effects, and yields corrected confidence bands for covariate effects. Extensive simulation studies indicate that a multivariate modelling approach is more parsimonious than fitting independent univariate models to the data while maintaining or improving model fit.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48199296","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: 8

Semi-supervised clustering of time-dependent categorical sequences with application to discovering education-based life patterns 时间依赖分类序列的半监督聚类及其在基于教育生活模式发现中的应用

IF 1 4区数学

Statistical Modelling Pub Date : 2021-03-08 DOI: 10.1177/1471082X21989170

Yingying Zhang, Volodymyr Melnykov, Igor Melnykov

引用次数: 0

Reflections on Murray Aitkin's contributions to nonparametric mixture models and Bayes factors Murray Aitkin对非参数混合模型和贝叶斯因子贡献的思考

IF 1 4区数学

Statistical Modelling Pub Date : 2021-02-08 DOI: 10.1177/1471082X20981312

A. Agresti, F. Bartolucci, A. Mira

引用次数: 0

Editorial 社论

IF 1 4区数学

Statistical Modelling Pub Date : 2021-02-01 DOI: 10.1177/1471082x20943971

B. Marx, Komárek Arnošt, V. Núñez-Antón

引用次数: 0

Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks. 评估生物标志物的重要性:具有半竞争风险的纵向和生存数据的贝叶斯联合建模方法。

IF 1 4区数学

Statistical Modelling Pub Date : 2021-02-01 Epub Date: 2020-07-27 DOI: 10.1177/1471082x20933363

Fan Zhang, Ming-Hui Chen, Xiuyu Julie Cong, Qingxia Chen

{"title":"Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks.","authors":"Fan Zhang, Ming-Hui Chen, Xiuyu Julie Cong, Qingxia Chen","doi":"10.1177/1471082x20933363","DOIUrl":"https://doi.org/10.1177/1471082x20933363","url":null,"abstract":"Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modeling of PRO/QOL and survival data can provide a comparative assessment of patient-reported changes in specific symptoms or global measures that correspond to changes in survival. Motivated by a head and neck cancer clinical trial, we develop a class of trajectory-based models for longitudinal and survival data with disease progression. Specifically, we propose a class of mixed effects regression models for longitudinal measures, a cure rate model for the disease progression time (T P ), and a Cox proportional hazards model with time-varying covariates for the overall survival time (T D ) to account for T P and treatment switching. Under the semi-competing risks framework, the disease progression is the nonterminal event, the occurrence of which is subject to a terminal event of death. The properties of the proposed models are examined in detail. Within the Bayesian paradigm, we derive the decompositions of the deviance information criterion (DIC) and the logarithm of the pseudo marginal likelihood (LPML) to assess the fit of the longitudinal component of the model and the fit of each survival component, separately. We further develop ΔDIC as well as ΔLPML to determine the importance and contribution of the longitudinal data to the model fit of the T P and T D data.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082x20933363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39258068","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: 7