Statistical Modelling最新文献

{"title":"Dynamic modelling of corporate credit ratings and defaults","authors":"Laura Vana, K. Hornik","doi":"10.1177/1471082X211057610","DOIUrl":"https://doi.org/10.1177/1471082X211057610","url":null,"abstract":"In this article, we propose a longitudinal multivariate model for binary and ordinal outcomes to describe the dynamic relationship among firm defaults and credit ratings from various raters. The latent probability of default is modelled as a dynamic process which contains additive firm-specific effects, a latent systematic factor representing the business cycle and idiosyncratic observed and unobserved factors. The joint set-up also facilitates the estimation of a bias for each rater which captures changes in the rating standards of the rating agencies. Bayesian estimation techniques are employed to estimate the parameters of interest. Several models are compared based on their out-of-sample prediction ability and we find that the proposed model outperforms simpler specifications. The joint framework is illustrated on a sample of publicly traded US corporates which are rated by at least one of the credit rating agencies S&P, Moody's and Fitch during the period 1995–2014.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"23 1","pages":"357 - 375"},"PeriodicalIF":1.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48669974","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}

{"title":"Nonlinear discrete-time hazard models for women's entry into marriage","authors":"H. Turner, A. Batchelor, D. Firth","doi":"10.1177/1471082X211062651","DOIUrl":"https://doi.org/10.1177/1471082X211062651","url":null,"abstract":"We propose a hazard model for entry into marriage, based on a bell-shaped function to model the dependence on age. We demonstrate near-aliasing in an extension that estimates the support of the hazard and mitigate this via re-parameterization. Our proposed model parameterizes the maximum hazard and corresponding age, thereby facilitating more general models where these features depend on covariates. For data on women's marriages from the Living in Ireland Surveys 1994–2001, this approach captures a reduced propensity to marry over successive cohorts and an increasing delay in the timing of marriage with increasing education.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"107 - 126"},"PeriodicalIF":1.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47182063","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}

{"title":"Smoothing spatio-temporal data with complex missing data patterns","authors":"Eleonora Arnone, L. Sangalli, A. Vicini","doi":"10.1177/1471082X211057959","DOIUrl":"https://doi.org/10.1177/1471082X211057959","url":null,"abstract":"We consider spatio-temporal data and functional data with spatial dependence, characterized by complicated missing data patterns. We propose a new method capable to efficiently handle these data structures, including the case where data are missing over large portions of the spatio-temporal domain. The method is based on regression with partial differential equation regularization. The proposed model can accurately deal with data scattered over domains with irregular shapes and can accurately estimate fields exhibiting complicated local features. We demonstrate the consistency and asymptotic normality of the estimators. Moreover, we illustrate the good performances of the method in simulations studies, considering different missing data scenarios, from sparse data to more challenging scenarios where the data are missing over large portions of the spatial and temporal domains and the missing data are clustered in space and/or in time. The proposed method is compared to competing techniques, considering predictive accuracy and uncertainty quantification measures. Finally, we show an application to the analysis of lake surface water temperature data, that further illustrates the ability of the method to handle data featuring complicated patterns of missingness and highlights its potentiality for environmental studies.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"23 1","pages":"327 - 356"},"PeriodicalIF":1.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48209900","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}

{"title":"Streamlined variational inference for higher level group-specific curve models.","authors":"M Menictas,&nbsp;T H Nolan,&nbsp;D G Simpson,&nbsp;M P Wand","doi":"10.1177/1471082x20930894","DOIUrl":"https://doi.org/10.1177/1471082x20930894","url":null,"abstract":"<p><p>A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another one, and higher level extensions are analogous. Streamlined variational inference for higher level group-specific curve models is a challenging problem. We confront it by systematically working through two-level and then three-level cases and making use of the higher level sparse matrix infrastructure laid down in Nolan and Wand (2019). A motivation is analysis of data from ultrasound technology for which three-level group-specific curve models are appropriate. Whilst extension to the number of levels exceeding three is not covered explicitly, the pattern established by our systematic approach sheds light on what is required for even higher level group-specific curve models.</p>","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 6","pages":"479-519"},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082x20930894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39913169","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}

{"title":"Reflections on statistical modelling: A conversation with Murray Aitkin","authors":"M. Aitkin, J. Hinde, Brian Francis","doi":"10.1177/1471082X211060560","DOIUrl":"https://doi.org/10.1177/1471082X211060560","url":null,"abstract":"A virtual interview with Murray Aitkin by Brian Francis and John Hinde, two of the original members of the Centre for Applied Statistics that Murray created at Lancaster University. The talk ranges over Murray's reflections of a career in statistical modelling and the many different collaborations across the world that have been such a significant part of it.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"13 - 32"},"PeriodicalIF":1.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48389187","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}

{"title":"Bayesian analysis of two-part nonlinear latent variable model: Semiparametric method","authors":"Jianwei Gou, Ye-mao Xia, De-Peng Jiang","doi":"10.1177/1471082X211059233","DOIUrl":"https://doi.org/10.1177/1471082X211059233","url":null,"abstract":"Two-part model (TPM) is a widely appreciated statistical method for analyzing semi-continuous data. Semi-continuous data can be viewed as arising from two distinct stochastic processes: one governs the occurrence or binary part of data and the other determines the intensity or continuous part. In the regression setting with the semi-continuous outcome as functions of covariates, the binary part is commonly modelled via logistic regression and the continuous component via a log-normal model. The conventional TPM, still imposes assumptions such as log-normal distribution of the continuous part, with no unobserved heterogeneity among the response, and no collinearity among covariates, which are quite often unrealistic in practical applications. In this article, we develop a two-part nonlinear latent variable model (TPNLVM) with mixed multiple semi-continuous and continuous variables. The semi-continuous variables are treated as indicators of the latent factor analysis along with other manifest variables. This reduces the dimensionality of the regression model and alleviates the potential multicollinearity problems. Our TPNLVM can accommodate the nonlinear relationships among latent variables extracted from the factor analysis. To downweight the influence of distribution deviations and extreme observations, we develop a Bayesian semiparametric analysis procedure. The conventional parametric assumptions on the related distributions are relaxed and the Dirichlet process (DP) prior is used to improve model fitting. By taking advantage of the discreteness of DP, our method is effective in capturing the heterogeneity underlying population. Within the Bayesian paradigm, posterior inferences including parameters estimates and model assessment are carried out through Markov Chains Monte Carlo (MCMC) sampling method. To facilitate posterior sampling, we adapt the Polya-Gamma stochastic representation for the logistic model. Using simulation studies, we examine properties and merits of our proposed methods and illustrate our approach by evaluating the effect of treatment on cocaine use and examining whether the treatment effect is moderated by psychiatric problems.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"23 1","pages":"376 - 399"},"PeriodicalIF":1.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47521798","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}

{"title":"Multi-parameter regression survival modelling with random effects","authors":"Fatima-Zahra Jaouimaa, I. Ha, Kevin Burke","doi":"10.1177/1471082x221117377","DOIUrl":"https://doi.org/10.1177/1471082x221117377","url":null,"abstract":"We consider a parametric modelling approach for survival data where covariates are allowed to enter the model through multiple distributional parameters (i.e., scale and shape). This is in contrast with the standard convention of having a single covariate-dependent parameter, typically the scale. Taking what is referred to as a multi-parameter regression (MPR) approach to modelling has been shown to produce flexible and robust models with relatively low model complexity cost. However, it is very common to have clustered data arising from survival analysis studies, and this is something that is under developed in the MPR context. The purpose of this article is to extend MPR models to handle multivariate survival data by introducing random effects in both the scale and the shape regression components. We consider a variety of possible dependence structures for these random effects (independent, shared and correlated), and estimation proceeds using a h-likelihood approach. The performance of our estimation procedure is investigated by a way of an extensive simulation study, and the merits of our modelling approach are illustrated through applications to two real data examples, a lung cancer dataset and a bladder cancer dataset.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48196771","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}

{"title":"A joint transition model for evaluating eGFR as biomarker for rejection after kidney transplantation","authors":"M. Coemans, G. Verbeke, M. Naesens","doi":"10.1177/1471082X211048695","DOIUrl":"https://doi.org/10.1177/1471082X211048695","url":null,"abstract":"The estimated glomerular filtration rate (eGFR) quantifies kidney graft function and is measured repeatedly after transplantation. Kidney graft rejection is diagnosed by performing biopsies on a regular basis (protocol biopsies at time of stable eGFR) or by performing biopsies due to clinical cause (indication biopsies at time of declining eGFR). The diagnostic value of the eGFR evolution as biomarker for rejection is not well established. To this end, we built a joint model which combines characteristics of transition models and shared parameter models to carry over information from one biopsy to the next, taking into account the longitudinal information of eGFR collected in between. From our model, applied to data of University Hospitals Leuven (870 transplantations, 2 635 biopsies), we conclude that a negative deviation from the mean eGFR slope increases the probability of rejection in indication biopsies, but that, on top of the biopsy history, there is little benefit in using the eGFR profile for diagnosing rejection. Methodologically, our model fills a gap in the biomarker literature by relating a frequently (repeatedly) measured continuous outcome with a less frequently (repeatedly) measured binary indicator. The developed joint transition model is flexible and applicable to multiple other research settings.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"23 1","pages":"228 - 246"},"PeriodicalIF":1.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46812969","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}

{"title":"Corrigendum to Poisson–Tweedie mixed-effects model: A flexible approach for the analysis of longitudinal RNA-seq data","authors":"","doi":"10.1177/1471082x211014368","DOIUrl":"https://doi.org/10.1177/1471082x211014368","url":null,"abstract":"“Poisson–Tweedie mixed-effects model: A flexible approach for the analysis of longitudinal RNA-seq data” by Mirko Signorelli, Pietro Spitali and Roula Tsonaka was published in Statistical Modelling, Onlinefirst 24 August 2020, DOI: 10.1177/1471082X20936017. The authors have recently identified two mistakes in the R code that they used to estimate the Poisson-Tweedie mixed model (ptmixed) in simulations C and D, whose results are presented in Section 3.3 of the OnlineFirst version of the article. Therefore, they have proceeded to rerun such simulations with the corrected code, and to update the results of Section 3.3 accordingly. The amended results of simulations C and D will be published in the onlinefirst version of the article and the subsequent issue in which it is published.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 1","pages":"471 - 471"},"PeriodicalIF":1.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082x211014368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49223831","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}

{"title":"Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling","authors":"Daniel Sheanshang, P. White, D. Keeler","doi":"10.1177/1471082X211043946","DOIUrl":"https://doi.org/10.1177/1471082X211043946","url":null,"abstract":"In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust methods are often too simple for some applications. In our motivating application, scientists drill snow cores and measure snow density to infer densification rates that aid in estimating snow water accumulation rates and glacier mass balances. Advanced measurement techniques can measure density at high resolution over depth but are sensitive to core imperfections, making them prone to outliers. Outlier accommodation is challenging in this setting because the distribution of outliers evolves over depth and the data demonstrate natural heteroscedasticity. To address these challenges, we present a two-component mixture model using a physically motivated snow density model and an outlier model, both of which evolve over depth. The physical component of the mixture model has a mean function with normally distributed depth-dependent heteroscedastic errors. The outlier component is specified using a semiparametric prior density process constructed through a normalized process convolution of log-normal random variables. We demonstrate that this model outperforms alternatives and can be used for various inferential tasks.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"23 1","pages":"151 - 172"},"PeriodicalIF":1.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43671214","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}