Statistical Modelling最新文献_第5页

Bayesian discrete conditional transformation models 贝叶斯离散条件变换模型

IF 1 4区数学

Statistical Modelling Pub Date : 2022-05-17 DOI: 10.1177/1471082x221114177

Manuel Carlan, T. Kneib

{"title":"Bayesian discrete conditional transformation models","authors":"Manuel Carlan, T. Kneib","doi":"10.1177/1471082x221114177","DOIUrl":"https://doi.org/10.1177/1471082x221114177","url":null,"abstract":"We propose a novel Bayesian model framework for discrete ordinal and count data based on conditional transformations of the responses. The conditional transformation function is estimated from the data in conjunction with an a priori chosen reference distribution. For count responses, the resulting transformation model is novel in the sense that it is a Bayesian fully parametric yet distribution-free approach that can additionally account for excess zeros with additive transformation function specifications. For ordinal categoric responses, our cumulative link transformation model allows the inclusion of linear and non-linear covariate effects that can additionally be made category-specific, resulting in (non-)proportional odds or hazards models and more, depending on the choice of the reference distribution. Inference is conducted by a generic modular Markov chain Monte Carlo algorithm where multivariate Gaussian priors enforce specific properties such as smoothness on the functional effects. To illustrate the versatility of Bayesian discrete conditional transformation models, applications to counts of patent citations in the presence of excess zeros and on treating forest health categories in a discrete partial proportional odds model are presented.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46689765","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

Interpretable modelling of retail demand and price elasticity for passenger flights using booking data 使用预订数据对客运航班零售需求和价格弹性进行解释性建模

IF 1 4区数学

Statistical Modelling Pub Date : 2022-05-09 DOI: 10.1177/1471082x221083343

Jan Felix Meyer, Goeran Kauermann, M. Smith

{"title":"Interpretable modelling of retail demand and price elasticity for passenger flights using booking data","authors":"Jan Felix Meyer, Goeran Kauermann, M. Smith","doi":"10.1177/1471082x221083343","DOIUrl":"https://doi.org/10.1177/1471082x221083343","url":null,"abstract":"We propose a model of retail demand for air travel and ticket price elasticity at the daily booking and individual flight level. Daily bookings are modelled as a non-homogeneous Poisson process with respect to the time to departure. The booking intensity is a function of booking and flight level covariates, including non-linear effects modelled semi-parametrically using penalized splines. Customer heterogeneity is incorporated using a finite mixture model, where the latent segments have covariate-dependent probabilities. We fit the model to a unique dataset of over one million daily counts of bookings for 9 602 scheduled flights on a short-haul route over two years. A control variate approach with a strong instrument corrects for a substantial level of price endogeneity. A rich latent segmentation is uncovered, along with strong covariate effects. The calibrated model can be used to quantify demand and price elasticity for different flights booked on different days prior to departure and is a step towards continuous pricing; something that is a major objective of airlines. As our model is interpretable, forecasts can be created under different scenarios. For instance, while our model is calibrated on data collected prior to COVID-19, many of the empirical insights are likely to remain valid as air travel recovers in the post-COVID-19 period.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43450403","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

On Lasso and adaptive Lasso for non-random sample in credit scoring 信用评分中非随机样本的套索和自适应套索

IF 1 4区数学

Statistical Modelling Pub Date : 2022-05-09 DOI: 10.1177/1471082x221092181

E. Ogundimu

引用次数: 0

A time-varying GARCH mixed-effects model for isolating high- and low- frequency volatility and co-volatility 一种时变GARCH混合效应模型，用于隔离高、低频波动和共波动

IF 1 4区数学

Statistical Modelling Pub Date : 2022-03-14 DOI: 10.1177/1471082x221080488

Zeynab Aghabazaz, I. Kazemi, A. Nematollahi

{"title":"A time-varying GARCH mixed-effects model for isolating high- and low- frequency volatility and co-volatility","authors":"Zeynab Aghabazaz, I. Kazemi, A. Nematollahi","doi":"10.1177/1471082x221080488","DOIUrl":"https://doi.org/10.1177/1471082x221080488","url":null,"abstract":"This article studies long-term, short-term volatility and co-volatility in stock markets by introducing modelling strategies to the multivariate data analysis that deal with serially correlated innovations and cross-section dependence. In particular, it presents an innovative mixed-effects model through a GARCH process, allowing for heterogeneity effects and time-series dynamics. We propose a non-parametric regression model of the penalized low-rank smoothing spline to present time trends into the variance and covariance equations. The strategy provides flexible modelling of the low-frequency volatility and co-volatility in equity markets. The decomposed low-frequency matrix was modelled using the modified Cholesky factorization. The Hamiltonian Monte Carlo technique is implemented as a Bayesian computing process for estimating parameters and latent factors. The advantage of our modelling strategy in empirical studies is highlighted by examining the effect of latent financial factors on a panel across 10 equities over 110 weekly series. The model can differentiate non-parametrically dynamic patterns of high and low frequencies of variance–covariance structural equations and incorporate economic features to predict variabilities in stock markets regarding time-series evidence.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42631773","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 modelling of integer-valued transfer function models 整值传递函数模型的贝叶斯建模

IF 1 4区数学

Statistical Modelling Pub Date : 2022-03-01 DOI: 10.1177/1471082x221075477

Aljo Clair Pingal, Cathy W. S. Chen

{"title":"Bayesian modelling of integer-valued transfer function models","authors":"Aljo Clair Pingal, Cathy W. S. Chen","doi":"10.1177/1471082x221075477","DOIUrl":"https://doi.org/10.1177/1471082x221075477","url":null,"abstract":"External events are commonly known as interventions that often affect times series of counts. This research introduces a class of transfer function models that include four different types of interventions on integer-valued time series: abrupt start and abrupt decay (additive outlier), abrupt start and gradual decay (transient shift), abrupt start and permanent effect (level shift) and gradual start and permanent effect. We propose integer-valued transfer function models incorporating a generalized Poisson, log-linear generalized Poisson or negative binomial to estimate and detect these four types of interventions in a time series of counts. Utilizing Bayesian methods, which are adaptive Markov chain Monte Carlo (MCMC) algorithms to obtain the estimation, we further employ deviance information criterion (DIC), posterior odd ratios and mean squared standardized residual for model comparisons. As an illustration, this study evaluates the effectiveness of our methods through a simulation study and application to crime data in Albury City, New South Wales (NSW) Australia. Simulation results show that the MCMC procedure is reasonably effective. The empirical outcome also reveals that the proposed models are able to successfully detect the locations and type of interventions.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45000599","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: 3

Power logit regression for modeling bounded data 建模有界数据的幂logit回归

IF 1 4区数学

Statistical Modelling Pub Date : 2022-02-03 DOI: 10.1177/1471082x221140157

Francisco F. Queiroz, S. Ferrari

引用次数: 0

Editorial 编辑

IF 1 4区数学

Statistical Modelling Pub Date : 2022-02-01 DOI: 10.1177/1471082x211051203

Vicente Núñez-Antón, Andrea Mayr, F. Bartolucci

引用次数: 0

Guest Editorial: Special Issue in Honour of Murray Aitkin 客座编辑：纪念Murray Aitkin特刊

IF 1 4区数学

Statistical Modelling Pub Date : 2022-02-01 DOI: 10.1177/1471082X221076884

B. Francis, J. Hinde

引用次数: 0

Obituary: Brian Douglas Marx 讣告:布莱恩·道格拉斯·马克思

IF 1 4区数学

Statistical Modelling Pub Date : 2022-02-01 DOI: 10.1177/1471082x211072584

Paul H. C. Eilers, E. Lesaffre

引用次数: 0

Statistical modelling of COVID-19 data: Putting generalized additive models to work 新冠肺炎数据的统计建模：使广义加性模型发挥作用

IF 1 4区数学

Statistical Modelling Pub Date : 2022-01-04 DOI: 10.1177/1471082X221124628

Cornelius Fritz, Giacomo De Nicola, Martje Rave, M. Weigert, Yegane Khazaei, U. Berger, Helmut Kuchenhoff, G. Kauermann

{"title":"Statistical modelling of COVID-19 data: Putting generalized additive models to work","authors":"Cornelius Fritz, Giacomo De Nicola, Martje Rave, M. Weigert, Yegane Khazaei, U. Berger, Helmut Kuchenhoff, G. Kauermann","doi":"10.1177/1471082X221124628","DOIUrl":"https://doi.org/10.1177/1471082X221124628","url":null,"abstract":"Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter estimates are independent of the (unknown) case-detection ratio, which plays an important role in COVID-19 surveillance data. Second, we model the incidence of hospitalizations, for which data is only available with a temporal delay. We illustrate how correcting for this reporting delay through a nowcasting procedure can be naturally incorporated into the GAM framework as an offset term. Third, we propose a multinomial model for the weekly occupancy of intensive care units (ICU), where we distinguish between the number of COVID-19 patients, other patients and vacant beds. With these three examples, we aim to showcase the practical and ‘off-the-shelf’ applicability of GAMs to gain new insights from real-world data.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48853286","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