Statistical Modelling最新文献_第7页

A Bayesian framework for modelling the preferential selection process in respondent-driven sampling 一个贝叶斯框架建模的优先选择过程在受访者驱动的抽样

IF 1 4区数学

Statistical Modelling Pub Date : 2021-09-22 DOI: 10.1177/1471082X211043945

Katherine R. McLaughlin

{"title":"A Bayesian framework for modelling the preferential selection process in respondent-driven sampling","authors":"Katherine R. McLaughlin","doi":"10.1177/1471082X211043945","DOIUrl":"https://doi.org/10.1177/1471082X211043945","url":null,"abstract":"In sampling designs that utilize peer recruitment, the sampling process is partially unknown and must be modelled to make inference about the population and estimate standard outcomes like prevalence. We develop a Bayesian model for the recruitment process for respondent-driven sampling (RDS), a network sampling methodology used worldwide to sample hidden populations that are not reachable by conventional sampling techniques, including those at high risk for HIV/AIDS. Current models for the RDS sampling process typically assume that recruitment occurs randomly given the population social network, but this is likely untrue in practice. To model preferential selection on covariates, we develop a sequential two-sided rational choice framework, which allows generative probabilistic network models to be created for the RDS sampling process. In the rational choice framework, members of the population make recruitment and participation choices based on observable nodal and dyadic covariates to maximize their utility given constraints. Inference is made about recruitment preferences given the observed recruitment chain in a Bayesian framework by incorporating the latent utilities and sampling from the joint posterior distribution via Markov chain Monte Carlo. We present simulation results and apply the model to an RDS study of Francophone migrants in Rabat, Morocco.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"153 - 174"},"PeriodicalIF":1.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46175653","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

Mixture models and networks: The stochastic blockmodel 混合模型和网络:随机块模型

IF 1 4区数学

Statistical Modelling Pub Date : 2021-09-04 DOI: 10.1177/1471082X211033169

G. De Nicola, Benjamin Sischka, G. Kauermann

引用次数: 7

Modelling agreement for binary intensive longitudinal data 二元密集纵向数据的建模协议

IF 1 4区数学

Statistical Modelling Pub Date : 2021-09-03 DOI: 10.1177/1471082X211034002

S. Vanbelle, E. Lesaffre

引用次数: 0

Parametric estimation of non-crossing quantile functions 非交叉分位数函数的参数估计

IF 1 4区数学

Statistical Modelling Pub Date : 2021-09-01 DOI: 10.1177/1471082X211036517

G. Sottile, P. Frumento

{"title":"Parametric estimation of non-crossing quantile functions","authors":"G. Sottile, P. Frumento","doi":"10.1177/1471082X211036517","DOIUrl":"https://doi.org/10.1177/1471082X211036517","url":null,"abstract":"Quantile regression (QR) has gained popularity during the last decades, and is now considered a standard method by applied statisticians and practitioners in various fields. In this work, we applied QR to investigate climate change by analysing historical temperatures in the Arctic Circle. This approach proved very flexible and allowed to investigate the tails of the distribution, that correspond to extreme events. The presence of quantile crossing, however, prevented using the fitted model for prediction and extrapolation. In search of a possible solution, we first considered a different version of QR, in which the QR coefficients were described by parametric functions. This alleviated the crossing problem, but did not eliminate it completely. Finally, we exploited the imposed parametric structure to formulate a constrained optimization algorithm that enforced monotonicity. The proposed example showed how the relatively unexplored field of parametric quantile functions could offer new solutions to the long-standing problem of quantile crossing. Our approach is particularly convenient in situations, like the analysis of time series, in which the fitted model may be used to predict extreme quantiles or to perform extrapolation. The described estimator has been implemented in the R package qrcm.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"23 1","pages":"173 - 195"},"PeriodicalIF":1.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45294117","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

Random effect models for multivariate mixed data: A Parafac-based finite mixture approach 多元混合数据的随机效应模型:基于parafac的有限混合方法

IF 1 4区数学

Statistical Modelling Pub Date : 2021-09-01 DOI: 10.1177/1471082X211037405

M. Alfò, P. Giordani

引用次数: 0

On Bayesian model selection for INGARCH models viatrans-dimensional Markov chain Monte Carlo methods 基于跨维马尔可夫链蒙特卡罗方法的INGARCH模型贝叶斯模型选择

IF 1 4区数学

Statistical Modelling Pub Date : 2021-08-30 DOI: 10.1177/1471082X211034705

Panagiota Tsamtsakiri, D. Karlis

引用次数: 1

Corrigendum to Bayesian modelling of nonlinear negative binomial integer-valued GARCHX models 非线性负二项整数值GARCHX模型贝叶斯建模的勘误表

IF 1 4区数学

Statistical Modelling Pub Date : 2021-08-01 DOI: 10.1177/1471082x19874190

引用次数: 0

Vertex Exchange Method for non-parametric estimation of mixing distributions in logistic mixed models logistic混合模型中混合分布非参数估计的顶点交换方法

IF 1 4区数学

Statistical Modelling Pub Date : 2021-08-01 DOI: 10.1177/1471082X19889143

L. Marquart, G. Verbeke

{"title":"Vertex Exchange Method for non-parametric estimation of mixing distributions in logistic mixed models","authors":"L. Marquart, G. Verbeke","doi":"10.1177/1471082X19889143","DOIUrl":"https://doi.org/10.1177/1471082X19889143","url":null,"abstract":"The conventional normality assumption for the random effects distribution in logistic mixed models can be too restrictive in some applications. In our data example of a longitudinal study modelling employment participation of Australian women, the random effects exhibit non-normality due to a potential mover–stayer scenario. In such a scenario, the women observed to remain in the same initial response state over the study period may consist of two subgroups: latent stayers—those with extremely small probability of transitioning response states—and latent movers, those with a probability of transitioning response states. The similarities between estimating the random effects using non-parametric approaches and mover–stayer models have previously been highlighted. We explore non-parametric approaches to model univariate and bivariate random effects in a potential mover–stayer scenario. As there are limited approaches available to fit the non-parametric maximum likelihood estimate for bivariate random effects in logistic mixed models, we implement the Vertex Exchange Method (VEM) to estimate the random effects in logistic mixed models. The approximation of the non-parametric maximum likelihood estimate derived by the VEM algorithm induces more flexibility of the random effects, identifying regions corresponding to potential latent stayers in the non-employment category in our data example.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 1","pages":"359 - 377"},"PeriodicalIF":1.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X19889143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47681941","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

Withdrawal: Administrative Duplicate Publication 撤回:行政副本出版物

IF 1 4区数学

Statistical Modelling Pub Date : 2021-07-12 DOI: 10.1177/1471082x20979104

引用次数: 0

Withdrawal: Administrative Duplicate Publication 撤回:行政副本出版物

IF 1 4区数学

Statistical Modelling Pub Date : 2021-07-12 DOI: 10.1177/1471082x20979104

引用次数: 0