Statistical Modelling最新文献_第10页

Bayesian modelling of nonlinear negative binomial integer-valued GARCHX models 非线性负二项整数值GARCHX模型的贝叶斯建模

IF 1 4区数学

Statistical Modelling Pub Date : 2019-07-08 DOI: 10.1177/1471082X19845541

Cathy W. S. Chen, K. Khamthong

引用次数: 10

Flexible parametric multistate modelling of employment history 就业历史的柔性参数多状态建模

IF 1 4区数学

Statistical Modelling Pub Date : 2019-06-01 DOI: 10.1177/1471082X19836299

A. Hout, Wen. Tan

引用次数: 0

Guest Editorial: Statistical Modelling in Demographics 客座评论:人口统计学中的统计建模

IF 1 4区数学

Statistical Modelling Pub Date : 2019-06-01 DOI: 10.1177/1471082X19852227

J. Gampe, C. Camarda

引用次数: 0

A Bayesian approach for some zero-modified Poisson mixture models 一些零修正泊松混合模型的贝叶斯方法

IF 1 4区数学

Statistical Modelling Pub Date : 2019-05-21 DOI: 10.1177/1471082X19841984

Wesley Bertoli, K. S. Conceição, M. Andrade, F. Louzada

引用次数: 5

A Bayesian approach for the segmentation of series with a functional effect 具有函数效应的序列分割的贝叶斯方法

IF 1 4区数学

Statistical Modelling Pub Date : 2019-04-01 DOI: 10.1177/1471082X18755539

M. Baragatti, K. Bertin, É. Lebarbier, C. Meza

引用次数: 2

A Bayesian two-stage regression approach of analysing longitudinal outcomes with endogeneity and incompleteness 一种贝叶斯两阶段回归方法分析纵向结果的内生性和不完全性

IF 1 4区数学

Statistical Modelling Pub Date : 2019-04-01 DOI: 10.1177/1471082X17747806

P. Bhuyan, J. Biswas, Pulak Ghosh, Kiranmoy Das

{"title":"A Bayesian two-stage regression approach of analysing longitudinal outcomes with endogeneity and incompleteness","authors":"P. Bhuyan, J. Biswas, Pulak Ghosh, Kiranmoy Das","doi":"10.1177/1471082X17747806","DOIUrl":"https://doi.org/10.1177/1471082X17747806","url":null,"abstract":"Abstract: Two-stage regression methods are typically used for handling endogeneity in the simultaneous equations models in economics and other social sciences. However, the problem is challenging in the presence of incomplete response and/or incomplete endogenous covariate(s). We propose a Bayesian approach for the joint modelling of incomplete longitudinal continuous response and an incomplete count endogenous covariate, where the incompleteness is caused by the censorship through a selection mechanism. We define latent continuous variables which are left-censored at zero and develop a Gibbs sampling algorithm for the simultaneous estimation of the model parameters. We consider partially varying coefficients regression models containing covariates with fixed and time-varying effects on the response. Our work is motivated by a sample dataset from the Health and Retirement Study (HRS) for modelling the out-of-pocket medical cost, where the number of hospital admissions is considered as an endogenous covariate. Our analysis addresses some of the previously unanswered questions on the physical and financial health of the older population based on HRS data. Simulation studies are performed for assessing the usefulness of the proposed method compared to its competitors.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"19 1","pages":"157 - 173"},"PeriodicalIF":1.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X17747806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41822036","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: 11

Estimating transition coefficients for reconstructing coherent series of mortality by cause of Death 估计过渡系数重建连贯系列的死亡原因

IF 1 4区数学

Statistical Modelling Pub Date : 2019-03-19 DOI: 10.1177/1471082X19832398

C. Camarda

引用次数: 0

Random scaling factors in Bayesian distributional regression models with an application to real estate data 贝叶斯分布回归模型中的随机标度因子及其在房地产数据中的应用

IF 1 4区数学

Statistical Modelling Pub Date : 2019-03-19 DOI: 10.1177/1471082X18823099

Alexander Razen, S. Lang

{"title":"Random scaling factors in Bayesian distributional regression models with an application to real estate data","authors":"Alexander Razen, S. Lang","doi":"10.1177/1471082X18823099","DOIUrl":"https://doi.org/10.1177/1471082X18823099","url":null,"abstract":"Distributional structured additive regression provides a flexible framework for modelling each parameter of a potentially complex response distribution in dependence of covariates. Structured additive predictors allow for an additive decomposition of covariate effects with non-linear effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. Within this framework, we present a simultaneous estimation approach for multiplicative random effects that allow for cluster-specific heterogeneity with respect to the scaling of a covariate′s effect. More specifically, a possibly non-linear function f(z) of a covariate z may be scaled by a multiplicative and possibly spatially correlated cluster-specific random effect (1+αc). Inference is fully Bayesian and is based on highly efficient Markov Chain Monte Carlo (MCMC) algorithms. We investigate the statistical properties of our approach within extensive simulation experiments for different response distributions. Furthermore, we apply the methodology to German real estate data where we identify significant district-specific scaling factors. According to the deviance information criterion, the models incorporating these factors perform significantly better than standard models without (spatially correlated) random scaling factors.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"20 1","pages":"347 - 368"},"PeriodicalIF":1.0,"publicationDate":"2019-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X18823099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45220294","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: 19

Editorial 社论

IF 1 4区数学

Statistical Modelling Pub Date : 2019-02-01 DOI: 10.1177/1471082x18794049

B. Marx, A. Komárek, V. Núñez-Antón

引用次数: 0

Exploring and modelling team performances of the Kaggle European Soccer database 探索和模拟Kaggle欧洲足球数据库的球队表现

IF 1 4区数学

Statistical Modelling Pub Date : 2019-01-10 DOI: 10.1177/1471082X18810971

M. Carpita, E. Ciavolino, Paola Pasca

{"title":"Exploring and modelling team performances of the Kaggle European Soccer database","authors":"M. Carpita, E. Ciavolino, Paola Pasca","doi":"10.1177/1471082X18810971","DOIUrl":"https://doi.org/10.1177/1471082X18810971","url":null,"abstract":"This study explores a big and open database of soccer leagues in 10 European countries. Data related to players, teams and matches covering seven seasons (from 2009/2010 to 2015/2016) were retrieved from Kaggle, an online platform in which big data are available for predictive modelling and analytics competition among data scientists. Based on both preliminary data analysis, experts’ evaluation and players’ position on the football pitch, role-based indicators of teams’ performance have been built and used to estimate the win probability of the home team with the binomial logistic regression (BLR) model that has been extended including the ELO rating predictor and two random effects due to the hierarchical structure of the dataset. The predictive power of the BLR model and its extensions has been compared with the one of other statistical modelling approaches (Random Forest, Neural Network, k-NN, Naïve Bayes). Results showed that role-based indicators substantially improved the performance of all the models used in both this work and in previous works available on Kaggle. The base BLR model increased prediction accuracy by 10 percentage points, and showed the importance of defence performances, especially in the last seasons. Inclusion of both ELO rating predictor and the random effects did not substantially improve prediction, as the simpler BLR model performed equally good. With respect to the other models, only Naïve Bayes showed more balanced results in predicting both win and no-win of the home team.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"19 1","pages":"101 - 74"},"PeriodicalIF":1.0,"publicationDate":"2019-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X18810971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47485726","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: 32