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Summary of current and next issues

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Next issue articles are papers that have been copy-edited and typeset but not yet paginated for inclusion in an issue of the journal. The final version of articles can be downloaded from the "Current issue" and "Downloadable articles" section.

Next issue: volume 46 (1), January-June 2022

  • Unusual-event processes for count data

    Wanrudee Skulpakdee and Mongkol Hunkrajok

Current issue: volume 45 (2), July-December 2021

  • Nonparametric estimation of the probability of default with double smoothing

    Rebeca Peláez, Ricardo Cao and Juan M. Vilar

    Abstract: In this paper, a general nonparametric estimator of the probability of default is proposed and studied. It is derived from an estimator of the conditional survival function for censored data obtained with a double smoothing, on the covariate and on the variable of interest. An empirical study, based on modified real data, illustrates its practical application and a simulation study shows the performance of the proposed estimator and compares its behaviour with smoothed estimators only in the covariate. Asymptotic expressions for the bias and the variance of the probability of default estimator are found and asymptotic normality is proved.

    Keywords: Censored data, kernel method, probability of default, risk analysis, survival analysis

    Pages: 93–120


  • Modified almost unbiased two-parameter estimator for the Poisson regression model with an application to accident data

    Mustafa I. Alheety, Muhammad Qasim, Kristofer Månsson and B. M. Golam Kibria

    Abstract: Due to the large amount of accidents negatively affecting the wellbeing of the survivors and their families, a substantial amount of research is conducted to determine the causes of road accidents. This type of data come in the form of non-negative integers and may be modelled using the Poisson regression model. Unfortunately, the commonly used maximum likelihood estimator is unstable when the explanatory variables of the Poisson regression model are highly correlated. Therefore, this paper proposes a new almost unbiased estimator which reduces the instability of the maximum likelihood estimator and at the same time produce smaller mean squared error. We study the statistical properties of the proposed estimator and a simulation study has been conducted to compare the performance of the estimators in the smaller mean squared error sense. Finally, Swedish traffic fatality data are analyzed to show the benefit of the proposed method.

    Keywords: Applied traffic modeling, Maximum likelihood estimator, mean squared error matrix, Poisson regression, simulation study, traffic fatality

    Pages: 121–142


  • Bayesian hierarchical nonlinear modelling of intra-abdominal volume during pneumoperitoneum for laparoscopic surgery

    Gabriel Calvo, Carmen Armero, Virgilio Gómez-Rubio and Guido Mazzinari

    Abstract: Laparoscopy is an operation carried out in the abdomen through small incisions with visual control by a camera. This technique needs the abdomen to be insufflated with carbon dioxide to obtain a working space for surgical instruments’ manipulation. Identifying the critical point at which insufflation should be limited is crucial to maximizing surgical working space and minimizing injurious effects. A Bayesian nonlinear growth mixed-effects model for the relationship between the insufflation pressure and the intra–abdominal volume generated is discussed as well as its plausibility to represent the data.

    Keywords: Intra-abdominal pressure, logistic growth function, Markov chain, Monte Carlo methods, random effects

    Pages: 143–162


  • Median bilinear models in presence of extreme values

    Miguel Santolino

    Abstract: Bilinear regression models involving a nonlinear interaction term are applied in many fields (e.g., Goodman’s RC model, Lee-Carter mortality model or CAPM financial model). In many of these contexts data often exhibit extreme values. We propose the use of bilinear models to estimate the median of the conditional distribution in the presence of extreme values. The aim of this paper is to provide alternative methods to estimate median bilinear models. A calibration strategy based on an iterative estimation process of a sequence of median linear regression is developed. Mean and median bilinear models are compared in two applications with extreme observations. The first application deals with simulated data. The second application refers to Spanish mortality data involving years with atypical high mortality (Spanish flu, civil war and HIV/AIDS). The performance of the median bilinear model was superior to that of the mean bilinear model. Median bilinear models may be a good alternative to mean bilinear models in the presence of extreme values when the centre of the conditional distribution is of interest.

    Keywords: Outliers, quantile regression, single factor models, nonlinear, multiplicative

    Pages: 163–180


  • Exponentiated power Maxwell distribution with quantile regression and applications

    Francisco A. Segovia, Yolanda M. Gómez and Diego I. Gallardo

    Abstract: In this paper we introduce an extension of the power Maxwell distribution. We also discuss a reparametrized version of this model applied to quantile regression. Some properties of the model and estimation based on the maximum likelihood estimation method are studied. We also present a simulation study to assess the performance of estimators in such finite samples, and two applications to real data sets to illustrate the model. .

    Keywords: Maxwell distribution, exponentiated distributions, maximum likelihood, quantile regression

    Pages: 181–200