Statistical Modelling最新文献_第4页

Editorial 社论

IF 1 4区数学

Statistical Modelling Pub Date : 2023-02-01 DOI: 10.1177/1471082x231152966

Vicente Núñez-Antón, Andrea Mayr, F. Bartolucci

引用次数: 0

Soft-clipping INGARCH models for time series of bounded counts 有界计数时间序列的软裁剪INGARCH模型

IF 1 4区数学

Statistical Modelling Pub Date : 2022-12-20 DOI: 10.1177/1471082x221121223

C. Weiß, Malte Jahn

引用次数: 5

Covariate-modulated rectangular latent Markov models with an unknown number of regime profiles 具有未知数量的状态曲线的协变量调制的矩形潜马尔可夫模型

IF 1 4区数学

Statistical Modelling Pub Date : 2022-11-16 DOI: 10.1177/1471082x221127732

Alfonso Russo, Alessio Farcomeni, Maria Grazia Pittau, Roberto Zelli

引用次数: 0

Covariate-modulated rectangular latent Markov models with an unknown number of regime profiles 具有未知数量的状态曲线的协变量调制的矩形潜马尔可夫模型

IF 1 4区数学

Statistical Modelling Pub Date : 2022-11-16 DOI: 10.1177/1471082x221127732

Alfonso Russo, Alessio Farcomeni, Maria Grazia Pittau, Roberto Zelli

引用次数: 0

Semi-parametric hidden Markov model for large-scale multiple testing under dependency 依赖条件下大规模多重测试的半参数隐马尔可夫模型

IF 1 4区数学

Statistical Modelling Pub Date : 2022-09-27 DOI: 10.1177/1471082x221121235

Joungyoun Kim, Johan Lim, Jong Soo Lee

{"title":"Semi-parametric hidden Markov model for large-scale multiple testing under dependency","authors":"Joungyoun Kim, Johan Lim, Jong Soo Lee","doi":"10.1177/1471082x221121235","DOIUrl":"https://doi.org/10.1177/1471082x221121235","url":null,"abstract":"In this article, we propose a new semiparametric hidden Markov model (HMM) for use in the simultaneous hypothesis testing with dependency. The semi- or non-parametric HMM in the literature requires two conditions for its model identifiability, (a) the latent Markov chain (MC) is ergodic and its transition probability is full rank and (b) the observational distributions of different hidden states are disjoint or linearly independent. Unlike the existing models, our semiparametric HMM with two hidden states makes no assumption on the transition probability of the latent MC but assumes that observational distributions are extremal for the set of all stationary distributions of the model. To estimate the model, we propose a modified expectation-maximization algorithm, whose M-step has an additional purification step to make the observational distribution be extremal one. We numerically investigate the performance of the proposed procedure in the estimation of the model and compare it to two recent existing methods in various multiple testing error settings. In addition, we apply our procedure to analyzing two real data examples, the gas chromatography/mass spectrometry experiment to differentiate the origin of herbal medicine and the epidemiologic surveillance of an influenza-like illness.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45709451","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

Recurrent events analysis with piece-wise exponential additive mixed models 用分段指数加性混合模型分析循环事件

IF 1 4区数学

Statistical Modelling Pub Date : 2022-09-08 DOI: 10.1177/1471082x221117612

Jordache Ramjith, Andreas Bender, Kit C. B. Roes, Marianne A. Jonker

引用次数: 0

Recurrent events analysis with piece-wise exponential additive mixed models 用分段指数加性混合模型分析循环事件

IF 1 4区数学

Statistical Modelling Pub Date : 2022-09-08 DOI: 10.1177/1471082x221117612

Jordache Ramjith, Andreas Bender, Kit C. B. Roes, Marianne A. Jonker

引用次数: 0

Response mixture models based on supervised components: Clustering floristic taxa 基于监督分量的响应混合模型:聚类区系分类群

IF 1 4区数学

Statistical Modelling Pub Date : 2022-09-05 DOI: 10.1177/1471082x221115525

Julien Gibaud, X. Bry, C. Trottier, F. Mortier, M. Réjou‐Méchain

{"title":"Response mixture models based on supervised components: Clustering floristic taxa","authors":"Julien Gibaud, X. Bry, C. Trottier, F. Mortier, M. Réjou‐Méchain","doi":"10.1177/1471082x221115525","DOIUrl":"https://doi.org/10.1177/1471082x221115525","url":null,"abstract":"In this article, we propose to cluster responses in order to identify groups predicted by specific explanatory components. A response matrix is assumed to depend on a set of explanatory variables and a set of additional covariates. Explanatory variables are supposed many and redundant, which implies some dimension reduction and regularization. By contrast, additional covariates contain few selected variables which are forced into the regression model, as they demand no regularization. The response matrix is assumed partitioned into several unknown groups of responses. We suppose that the responses in each group are predictable from an appropriate number of specific orthogonal supervised components of explanatory variables. The classification is based on a mixture model of the responses. To estimate the model, we propose a criterion extending that of Supervised Component-based Generalized Linear Regression, a Partial Least Squares-type method, and develop an algorithm combining component-based model and Expectation Maximization estimation. This new methodology is tested on simulated data and then applied to a floristic ecology dataset.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41967369","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 model for space-time threshold exceedances with an application to extreme rainfall 时空阈值超越模型及其在极端降雨中的应用

IF 1 4区数学

Statistical Modelling Pub Date : 2022-05-27 DOI: 10.1177/1471082x221098224

P. Bortot, C. Gaetan

{"title":"A model for space-time threshold exceedances with an application to extreme rainfall","authors":"P. Bortot, C. Gaetan","doi":"10.1177/1471082x221098224","DOIUrl":"https://doi.org/10.1177/1471082x221098224","url":null,"abstract":"In extreme value studies, models for observations exceeding a fixed high threshold have the advantage of exploiting the available extremal information while avoiding bias from low values. In the context of space-time data, the challenge is to develop models for threshold exceedances that account for both spatial and temporal dependence. We address this issue through a modelling approach that embeds spatial dependence within a time series formulation. The model allows for different forms of limiting dependence in the spatial and temporal domains as the threshold level increases. In particular, temporal asymptotic independence is assumed, as this is often supported by empirical evidence, especially in environmental applications, while both asymptotic dependence and asymptotic independence are considered for the spatial domain. Inference from the observed exceedances is carried out through a combination of pairwise likelihood and a censoring mechanism. For those model specifications for which direct maximization of the censored pairwise likelihood is unfeasible, we propose an indirect inference procedure which leads to satisfactory results in a simulation study. The approach is applied to a dataset of rainfall amounts recorded over a set of weather stations in the North Brabant province of the Netherlands. The application shows that the range of extremal patterns that the model can cover is wide and that it has a competitive performance with respect to an alternative existing model for space-time threshold exceedances.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46124461","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

Self-exciting point process modelling of crimes on linear networks 线性网络犯罪的自激点过程建模

IF 1 4区数学

Statistical Modelling Pub Date : 2022-05-19 DOI: 10.1177/1471082x221094146

Nicoletta D’Angelo, David Payares, G. Adelfio, J. Mateu

引用次数: 5