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Statistical Modelling
Journal Prestige (SJR): 1.269
Citation Impact (citeScore): 1
Number of Followers: 18  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1471-082X - ISSN (Online) 1477-0342
Published by Sage Publications Homepage  [1174 journals]
  • Recurrent events analysis with piece-wise exponential additive mixed
           models

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      Authors: Jordache Ramjith, Andreas Bender, Kit C. B. Roes, Marianne A. Jonker
      Abstract: Statistical Modelling, Ahead of Print.
      Recurrent events analysis plays an important role in many applications, including the study of chronic diseases or recurrence of infections. Historically, many models for recurrent events have been variants of the Cox model. In this article we introduce and describe the application of the piece-wise exponential Additive Mixed Model (PAMM) for recurrent events analysis and illustrate how PAMMs can be used to flexibly model the dependencies in recurrent events data. Simulations confirm that PAMMs provide unbiased estimates as well as equivalence to the Cox model when proportional hazards are assumed. Applications to recurrence of staphylococcus aureus and malaria in children illustrate the estimation of seasonality, bivariate non-linear effects, multiple timescales and relaxation of the proportional hazards assumption via time-varying effects. The R package pammtools is extended to facilitate estimation and visualization of PAMMs for recurrent events data.
      Citation: Statistical Modelling
      PubDate: 2022-09-09T04:35:55Z
      DOI: 10.1177/1471082X221117612
       
  • Multi-parameter regression survival modelling with random effects

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      Authors: Fatima-Zahra Jaouimaa, Il Do Ha, Kevin Burke
      Abstract: Statistical Modelling, Ahead of Print.
      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.
      Citation: Statistical Modelling
      PubDate: 2022-09-07T10:47:43Z
      DOI: 10.1177/1471082X221117377
       
  • Response mixture models based on supervised components: Clustering
           floristic taxa

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      Authors: Julien Gibaud, Xavier Bry, Catherine Trottier, Frédéric Mortier, Maxime Réjou-Méchain
      Abstract: Statistical Modelling, Ahead of Print.
      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.
      Citation: Statistical Modelling
      PubDate: 2022-09-05T11:37:36Z
      DOI: 10.1177/1471082X221115525
       
  • A model for space-time threshold exceedances with an application to
           extreme rainfall

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      Authors: Paola Bortot, Carlo Gaetan
      Abstract: Statistical Modelling, Ahead of Print.
      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.
      Citation: Statistical Modelling
      PubDate: 2022-05-28T06:09:13Z
      DOI: 10.1177/1471082X221098224
       
  • Self-exciting point process modelling of crimes on linear networks

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      Authors: Nicoletta D’Angelo, David Payares, Giada Adelfio, Jorge Mateu
      Abstract: Statistical Modelling, Ahead of Print.
      Although there are recent developments for the analysis of first and second-order characteristics of point processes on networks, there are very few attempts in introducing models for network data. Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatiotemporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for which we follow a non-parametric estimation of both the background and the triggering components. Then we consider a semi-parametric version, including a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our model can be easily adapted to multi-type processes. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model.
      Citation: Statistical Modelling
      PubDate: 2022-05-20T04:54:21Z
      DOI: 10.1177/1471082X221094146
       
  • On Lasso and adaptive Lasso for non-random sample in credit scoring

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      Authors: Emmanuel O. Ogundimu
      Abstract: Statistical Modelling, Ahead of Print.
      Prediction models in credit scoring are often formulated using available data on accepted applicants at the loan application stage. The use of this data to estimate probability of default (PD) may lead to bias due to non-random selection from the population of applicants. That is, the PD in the general population of applicants may not be the same with the PD in the subpopulation of the accepted applicants. A prominent model for the reduction of bias in this framework is the sample selection model, but there is no consensus on its utility yet. It is unclear if the bias-variance trade- off of regularization techniques can improve the predictions of PD in non-random sample selection setting. To address this, we propose the use of Lasso and adaptive Lasso for variable selection and optimal predictive accuracy. By appealing to the least square approximation of the likelihood function of sample selection model, we optimize the resulting function subject to L1 and adaptively weighted L1 penalties using an efficient algorithm. We evaluate the performance of the proposed approach and competing alternatives in a simulation study and applied it to the well-known American Express credit card dataset.
      Citation: Statistical Modelling
      PubDate: 2022-05-09T09:11:19Z
      DOI: 10.1177/1471082X221092181
       
  • Interpretable modelling of retail demand and price elasticity for
           passenger flights using booking data

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      Authors: Jan Felix Meyer, Go¨ran Kauermann, Michael Stanley Smith
      Abstract: Statistical Modelling, Ahead of Print.
      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.
      Citation: Statistical Modelling
      PubDate: 2022-05-09T09:01:57Z
      DOI: 10.1177/1471082X221083343
       
  • A time-varying GARCH mixed-effects model for isolating high- and low-
           frequency volatility and co-volatility

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      Authors: Zeynab Aghabazaz, Iraj Kazemi, Alireza Nematollahi
      Abstract: Statistical Modelling, Ahead of Print.
      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.
      Citation: Statistical Modelling
      PubDate: 2022-03-15T05:28:10Z
      DOI: 10.1177/1471082X221080488
       
  • Bayesian modelling of integer-valued transfer function models

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      Authors: Aljo Clair Pingal, Cathy W. S. Chen
      Abstract: Statistical Modelling, Ahead of Print.
      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.
      Citation: Statistical Modelling
      PubDate: 2022-03-02T06:34:05Z
      DOI: 10.1177/1471082X221075477
       
  • Maximum approximate likelihood estimation of general continuous-time
           state-space models

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      Authors: Sina Mews, Roland Langrock, Marius Ötting, Houda Yaqine, Jost Reinecke
      Abstract: Statistical Modelling, Ahead of Print.
      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.
      Citation: Statistical Modelling
      PubDate: 2022-01-17T04:44:45Z
      DOI: 10.1177/1471082X211065785
       
 
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