Statistical Modelling最新文献_第10页

Block models for generalized multipartite networks: Applications in ecology and ethnobiology 广义多方网络的块模型:在生态学和民族生物学中的应用

IF 1 4区数学

Statistical Modelling Pub Date : 2020-12-18 DOI: 10.1177/1471082X20963254

A. Bar-Hen, P. Barbillon, S. Donnet

{"title":"Block models for generalized multipartite networks: Applications in ecology and ethnobiology","authors":"A. Bar-Hen, P. Barbillon, S. Donnet","doi":"10.1177/1471082X20963254","DOIUrl":"https://doi.org/10.1177/1471082X20963254","url":null,"abstract":"Generalized multipartite networks consist in the joint observation of several networks implying some common pre-specified groups of individuals. Such complex networks arise commonly in social sciences, biology, ecology, etc. We propose a flexible probabilistic model named Multipartite Block Model (MBM) able to unravel the topology of multipartite networks by identifying clusters (blocks) of nodes sharing the same patterns of connectivity across the collection of networks they are involved in. The model parameters are estimated through a variational version of the Expectation–Maximization algorithm. The numbers of blocks are chosen using an Integrated Completed Likelihood criterion specifically designed for our model. A simulation study illustrates the robustness of the inference strategy. Finally, two datasets respectively issued from ecology and ethnobiology are analyzed with the MBM in order to illustrate its flexibility and its relevance for the analysis of real datasets. The inference procedure is implemented in an R-package GREMLIN, available on Github (https://github.com/Demiperimetre/GREMLINhttps://github.com/Demiperimetre/GREMLIN).","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"273 - 296"},"PeriodicalIF":1.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20963254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44560116","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: 10

Spatial survival modelling of business re-opening after Katrina: Survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months 卡特里娜飓风后企业重新开业的空间生存模型：3、6或12个月内重新开业的生存模型与空间概率模型的比较

IF 1 4区数学

Statistical Modelling Pub Date : 2020-12-15 DOI: 10.1177/1471082X20967158

R. Bivand, V. Gómez‐Rubio

{"title":"Spatial survival modelling of business re-opening after Katrina: Survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months","authors":"R. Bivand, V. Gómez‐Rubio","doi":"10.1177/1471082X20967158","DOIUrl":"https://doi.org/10.1177/1471082X20967158","url":null,"abstract":"Zhou and Hanson; Zhou and Hanson; Zhou and Hanson (2015, Nonparametric Bayesian Inference in Biostatistics, pages 215–46. Cham: Springer; 2018, Journal of the American Statistical Association, 113, 571–81; 2020, spBayesSurv: Bayesian Modeling and Analysis of Spatially Correlated Survival Data. R package version 1.1.4) and Zhou et al. (2020, Journal of Statistical Software, Articles, 92, 1–33) present methods for estimating spatial survival models using areal data. This article applies their methods to a dataset recording New Orleans business decisions to re-open after Hurricane Katrina; the data were included in LeSage et al. (2011b, Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27). In two articles (LeSage etal., 2011a, Significance, 8, 160—63; 2011b, Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27), spatial probit models are used to model spatial dependence in this dataset, with decisions to re-open aggregated to the first 90, 180 and 360 days. We re-cast the problem as one of examining the time-to-event records in the data, right-censored as observations ceased before 175 businesses had re-opened; we omit businesses already re-opened when observations began on Day 41. We are interested in checking whether the conclusions about the covariates using aspatial and spatial probit models are modified when applying survival and spatial survival models estimated using MCMC and INLA. In general, we find that the same covariates are associated with re-opening decisions in both modelling approaches. We do however find that data collected from three streets differ substantially, and that the streets are probably better handled separately or that the street effect should be included explicitly.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 1","pages":"137 - 160"},"PeriodicalIF":1.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20967158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45849449","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

A copula-based approach to joint modelling of multiple longitudinal responses with multimodal structures 基于copula的多模态结构多重纵向响应联合建模方法

IF 1 4区数学

Statistical Modelling Pub Date : 2020-12-13 DOI: 10.1177/1471082X20967168

Zahra Mahdiyeh, I. Kazemi, G. Verbeke

{"title":"A copula-based approach to joint modelling of multiple longitudinal responses with multimodal structures","authors":"Zahra Mahdiyeh, I. Kazemi, G. Verbeke","doi":"10.1177/1471082X20967168","DOIUrl":"https://doi.org/10.1177/1471082X20967168","url":null,"abstract":"This article introduces a flexible modelling strategy to extend the familiar mixed-effects models for analysing longitudinal responses in the multivariate setting. By initiating a flexible multivariate multimodal distribution, this strategy relaxes the imposed normality assumption of related random-effects. We use copulas to construct a multimodal form of elliptical distributions. It can deal with the multimodality of responses and the non-linearity of dependence structure. Moreover, the proposed model can flexibly accommodate clustered subject-effects for multiple longitudinal measurements. It is much useful when several subpopulations exist but cannot be directly identifiable. Since the implied marginal distribution is not in the closed form, to approximate the associated likelihood functions, we suggest a computational methodology based on the Gauss–Hermite quadrature that consequently enables us to implement standard optimization techniques. We conduct a simulation study to highlight the main properties of the theoretical part and make a comparison with regular mixture distributions. Results confirm that the new strategy deserves to receive attention in practice. We illustrate the usefulness of our model by the analysis of a real-life dataset taken from a low back pain study.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"327 - 348"},"PeriodicalIF":1.0,"publicationDate":"2020-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20967168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42995318","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

Response transformations for random effect and variance component models 随机效应和方差分量模型的响应变换

IF 1 4区数学

Statistical Modelling Pub Date : 2020-12-13 DOI: 10.1177/1471082X20966919

Amani Almohaimeed, J. Einbeck

引用次数: 3

Kernel-based estimation of individual location densities from smartphone data 基于核的智能手机数据个体位置密度估计

IF 1 4区数学

Statistical Modelling Pub Date : 2020-12-01 DOI: 10.1177/1471082X19870331

F. Finazzi, L. Paci

引用次数: 1

Maximum approximate likelihood estimation of general continuous-time state-space models 一般连续时间状态空间模型的最大近似似然估计

IF 1 4区数学

Statistical Modelling Pub Date : 2020-10-28 DOI: 10.1177/1471082x211065785

S. Mews, R. Langrock, Marius Otting, Houda Yaqine, Jost Reinecke

{"title":"Maximum approximate likelihood estimation of general continuous-time state-space models","authors":"S. Mews, R. Langrock, Marius Otting, Houda Yaqine, Jost Reinecke","doi":"10.1177/1471082x211065785","DOIUrl":"https://doi.org/10.1177/1471082x211065785","url":null,"abstract":"Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time SSM framework, allowing both the observation and the state process to be non-linear and non-Gaussian. Statistical inference is carried out by maximum approximate likelihood estimation, where multiple numerical integration within the likelihood evaluation is performed via a fine discretization of the state process. The corresponding reframing of the SSM as a continuous-time hidden Markov model, with structured state transitions, enables us to apply the associated efficient algorithms for parameter estimation and state decoding. We illustrate the modelling approach in a case study using data from a longitudinal study on delinquent behaviour of adolescents in Germany, revealing temporal persistence in the deviation of an individual's delinquency level from the population mean.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45949267","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: 9

Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach 半连续纵向数据的两部分分位数回归模型:有限混合方法

IF 1 4区数学

Statistical Modelling Pub Date : 2020-10-23 DOI: 10.1177/1471082X21993603

Luca Merlo, A. Maruotti, L. Petrella

引用次数: 3

Multiple imputation and selection of ordinal level 2 predictors in multilevel models: An analysis of the relationship between student ratings and teacher practices and attitudes 多层次模型中有序水平2预测因子的多重归算和选择:学生评分与教师实践和态度之间关系的分析

IF 1 4区数学

Statistical Modelling Pub Date : 2020-10-22 DOI: 10.1177/1471082X20949710

L. Grilli, Maria Francesca Marino, O. Paccagnella, C. Rampichini

{"title":"Multiple imputation and selection of ordinal level 2 predictors in multilevel models: An analysis of the relationship between student ratings and teacher practices and attitudes","authors":"L. Grilli, Maria Francesca Marino, O. Paccagnella, C. Rampichini","doi":"10.1177/1471082X20949710","DOIUrl":"https://doi.org/10.1177/1471082X20949710","url":null,"abstract":"The article is motivated by the analysis of the relationship between university student ratings and teacher practices and attitudes, which are measured via a set of binary and ordinal items collected by an innovative survey. The analysis is conducted through a two-level random intercept model, where student ratings are nested within teachers. The analysis must face two issues about the items measuring teacher practices and attitudes, which are level 2 predictors: (a) the items are severely affected by missingness due to teacher non-response and (b) there is redundancy in both the number of items and the number of categories of their measurement scale. We tackle the missing data issue by considering a multiple imputation strategy exploiting information at both student and teacher levels. For the redundancy issue, we rely on regularization techniques for ordinal predictors, also accounting for the multilevel data structure. The proposed solution addresses the problem at hand in an original way, and it can be applied whenever it is required to select level 2 predictors affected by missing values. The results obtained with the final model indicate that ratings on teacher ability to motivate students are related to certain teacher practices and attitudes.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"221 - 238"},"PeriodicalIF":1.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20949710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47914577","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 mixture modelling of the high-energy photon counts collected by the Fermi Large Area Telescope 费米大面积望远镜收集的高能光子计数的贝叶斯混合模型

IF 1 4区数学

Statistical Modelling Pub Date : 2020-09-28 DOI: 10.1177/1471082X20947222

D. Costantin, Andrea Sottosanti, A. Brazzale, D. Bastieri, J. Fan

{"title":"Bayesian mixture modelling of the high-energy photon counts collected by the Fermi Large Area Telescope","authors":"D. Costantin, Andrea Sottosanti, A. Brazzale, D. Bastieri, J. Fan","doi":"10.1177/1471082X20947222","DOIUrl":"https://doi.org/10.1177/1471082X20947222","url":null,"abstract":"Identifying as yet undetected high-energy sources in the γ -ray sky is one of the declared objectives of the Fermi Large Area Telescope (LAT) Collaboration. We develop a Bayesian mixture model which is capable of disentangling the high-energy extra-galactic sources present in a given sky region from the pervasive background radiation. We achieve this by combining two model components. The first component models the emission activity of the single sources and incorporates the instrument response function of the Fermi γ -ray space telescope. The second component reliably reflects the current knowledge of the physical phenomena which underlie the γ -ray background. The model parameters are estimated using a reversible jump MCMC algorithm, which simultaneously returns the number of detected sources, their locations and relative intensities, and the background component. Our proposal is illustrated using a sample of the Fermi LAT data. In the analysed sky region, our model correctly identifies 116 sources out of the 132 present. The detection rate and the estimated directions and intensities of the identified sources are largely unaffected by the number of detected sources.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"175 - 198"},"PeriodicalIF":1.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20947222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47939426","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

Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model 贝叶斯环境下多变量纵向结果的成对估计及其对联合模型的扩展

IF 1 4区数学

Statistical Modelling Pub Date : 2020-09-28 DOI: 10.1177/1471082X20945069

K. Mauff, N. Erler, I. Kardys, D. Rizopoulos

{"title":"Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model","authors":"K. Mauff, N. Erler, I. Kardys, D. Rizopoulos","doi":"10.1177/1471082X20945069","DOIUrl":"https://doi.org/10.1177/1471082X20945069","url":null,"abstract":"Multiple longitudinal outcomes are theoretically easily modelled via extension of the generalized linear mixed effects model. However, due to computational limitations in high dimensions, in practice these models are applied only in situations with relatively few outcomes. We adapt the solution proposed by Fieuws and Verbeke (2006) to the Bayesian setting: fitting all pairwise bivariate models instead of a single multivariate model, and combining the Markov Chain Monte Carlo (MCMC) realizations obtained for each pairwise bivariate model for the relevant parameters. We explore importance sampling as a method to more closely approximate the correct multivariate posterior distribution. Simulation studies show satisfactory results in terms of bias, RMSE and coverage of the 95% credible intervals for multiple longitudinal outcomes, even in scenarios with more limited information and non-continuous outcomes, although the use of importance sampling is not successful. We further examine the incorporation of a time-to-event outcome, proposing the use of Bayesian pairwise estimation of a multivariate GLMM in an adaptation of the corrected two-stage estimation procedure for the joint model for multiple longitudinal outcomes and a time-to-event outcome (Mauff et al., 2020, Statistics and Computing). The method does not work as well in the case of the corrected two-stage joint model; however, the results are promising and should be explored further.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 1","pages":"115 - 136"},"PeriodicalIF":1.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20945069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45650265","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