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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 43 (2), July-December 2019

  • Modeling human network behavior using simulation and optimization tools: the need for simheuristics

    Aljoscha Gruler, Jesica de Armas, Angel A. Juan and David Goldsman

  • Tail risk measures using flexible parametric distributions

    José María Sarabia, Montserrat Guillen, Helena Chuliá and Faustino Prieto

  • False discovery rate control for p-values distributed on a discrete support with application to a neuroimaging study

    Hien Nguyen, Yohan Yee, Geoffrey McLachlan and Jason Lerch

  • Kernel distribution estimation for grouped data

    Miguel Reyes, Mario Francisco-Fernández, Ricardo Cao and Daniel Barreiro-Ures

  • Detecting outliers in multivariate volatility models: A wavelet procedure

    Aurea Grané, Belén Martín-Barragán and Helena Veiga

Current issue: volume 43 (1), January-June 2019

  • A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

    Lorena Reyes-Rubiano, Daniele Ferone, Angel A. Juan and Javier Faulin

    Abstract: Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.

    Keywords: Vehicle routing problem, electric vehicles, green transport and logistics, smart cities, simheuristics, biased-randomized heuristics

    Pages: 3–24

    DOI: 10.2436/20.8080.02.77

  • New L2-type exponentiality tests

    Marija Cuparić, Bojana Milosević and Marko Obradović

    Abstract: We introduce new consistent and scale-free goodness-of-fit tests for the exponential distribution based on the Puri-Rubin characterization. For the construction of test statistics we employ weighted L2 distance between V-empirical Laplace transforms of random variables that appear in the characterization. We derive the asymptotic behaviour under the null hypothesis as well as under fixed alternatives. We compare our tests, in terms of the Bahadur efficiency, to the likelihood ratio test, as well as some recent characterization based goodness-of-fit tests for the exponential distribution. We also compare the power of our tests to the power of some recent and classical exponentiality tests. According to both criteria, our tests are shown to be strong and outperform most of their competitors.

    Keywords: Goodness-of-fit, exponential distribution, Laplace transform, Bahadur efficiency, V-statistics with estimated parameters

    Pages: 25–50

    DOI: 10.2436/20.8080.02.78

  • Bayesian joint spatio-temporal analysis of multiple diseases

    Virgilio Gómez-Rubio, Francisco Palmí-Perales, Gonzalo López-Abente, Rebeca Ramis-Prieto and Pablo Fernández-Navarro

    Abstract: In this paper we propose a Bayesian hierarchical spatio-temporal model for the joint analysis of multiple diseases which includes specific and shared spatial and temporal effects. Dependence on shared terms is controlled by disease-specific weights so that their posterior distribution can be used to identify diseases with similar spatial and temporal patterns. The model proposed here has been used to study three different causes of death (oral cavity, esophagus and stomach cancer) in Spain at the province level. Shared and specific spatial and temporal effects have been estimated and mapped in order to study similarities and differences among these causes. Furthermore, estimates using Markov chain Monte Carlo and the integrated nested Laplace approximation are compared.

    Keywords: Bayesian modelling, Joint modelling, Multivariate disease mapping, Shared components. Spatio-temporal epidemiology

    Pages: 51–74

    DOI: 10.2436/20.8080.02.79

  • Internalizing negative externalities in vehicle routing problems through green taxes and green tolls

    Adrián Serrano-Hernández and Javier Faulín

    Abstract: Road freight transportation includes various internal and external costs that need to be accounted for in the construction of efficient routing plans. Typically, the resulting optimization problem is formulated as a vehicle routing problem in any of its variants. While the traditional focus of the vehicle routing problem was the minimization of internal routing costs such as travel distance or duration, numerous approaches to include external factors related to environmental routing aspects have been recently discussed in the literature. However, internal and external routing costs are often treated as competing objectives. This paper discusses the internalization of external routing costs through the consideration of green taxes and green tolls. Numeric experiments with a biased-randomization savings algorithm, show benefits of combining internal and external costs in delivery route planning.

    Keywords: Vehicle routing problem, biased randomization, green logistics, negative road externalities, internalization

    Pages: 75–94

    DOI: 10.2436/20.8080.02.80

  • A probabilistic model for explaining the points achieved by a team in football competition. Forecasting and regression with applications to the Spanish competition

    Emilio Gómez-Déniz, Nancy Dávila Cárdenes and José María Pérez Sánchez

    Abstract: In the last decades, a lot of research papers applying statistical methods for analysing sports data have been published. Football, also called soccer, is one of the most popular sports all over the world organised in national championships in a round robin format in which the team reaching the most points at the end of the tournament wins the competition. The aim of this work is to develop a suitable probability model for studying the points achieved by a team in a football match. For this purpose, we built a discrete probability distribution taking values, zero for losing, one for a draw and three for a victory. We test its performance using data from the Spanish Football League (First division) during the 2013-14 season. Furthermore, the model provides an attractive framework for predicting points and incorporating covariates in order to study the factors affecting the points achieved by the teams.

    Keywords: Covariate, football data, forecasting, regression, sport statistics, truncated distribution, weighted distribution

    Pages: 95–112

    DOI: 10.2436/20.8080.02.81

  • Automatic regrouping of strata in the goodness-of-fit chi-square test

    Vicente Núñez-Antón, Juan Manuel Pérez-Salamero González, Marta Regúlez-Castillo, Manuel Ventura-Marco and Carlos Vidal-Meliá

    Abstract: Pearson’s chi-square test is widely employed in social and health sciences to analyse categorical data and contingency tables. For the test to be valid, the sample size must be large enough to provide a minimum number of expected elements per category. This paper develops functions for regrouping strata automatically, thus enabling the goodness-of-fit test to be performed within an iterative procedure. The usefulness and performance of these functions is illustrated by means of a simulation study and the application to different datasets. Finally, the iterative use of the functions is applied to the Continuous Sample of Working Lives, a dataset that has been used in a considerable number of studies, especially on labour economics and the Spanish public pension system.

    Keywords: Goodness-of-fit chi-square test, statistical software, Visual Basic for Applications, Mathematica, Continuous Sample of Working Lives

    Pages: 113–142

    DOI: 10.2436/20.8080.02.83

  • On the optimism correction of the area under the receiver operating characteristic curve in logistic prediction models

    Amaia Iparragirre, Irantzu Barrio and María Xosé Rodríguez-Álvarez

    Abstract: When the same data are used to fit a model and estimate its predictive performance, this estimate may be optimistic, and its correction is required. The aim of this work is to compare the behaviour of different methods proposed in the literature when correcting for the optimism of the estimated area under the receiver operating characteristic curve in logistic regression models. A simulation study (where the theoretical model is known) is conducted considering different number of covariates, sample size, prevalence and correlation among covariates. The results suggest the use of k-fold cross-validation with replication and bootstrap.

    Keywords: Prediction models, logistic regression, area under the receiver operating characteristic curve, validation, bootstrap

    Pages: 145–162

    DOI: 10.2436/20.8080.02.82

  • Efficient algorithms for constructing D- and I-optimal exact designs for linear and non-linear models in mixture experiments

    Raúl Martín Martín, Irene García-Camacha Gutiérrez and Bernard Torsney

    Abstract: The problem of finding optimal exact designs is more challenging than that of approximate optimal designs. In the present paper, we develop two efficient algorithms to numerically construct exact designs for mixture experiments. The first is a novel approach to the well-known multiplicative algorithm based on sets of permutation points, while the second uses genetic algorithms. Using (i) linear and non-linear models, (ii) D- and I-optimality criteria, and (iii) constraints on the ingredients, both approaches are explored through several practical problems arising in the chemical, pharmaceutical and oil industry.

    Keywords: Optimal experimental design, D-optimality, I-optimality, mixture experiments, multiplicative algorithm, genetic algorithm, exact designs

    Pages: 163–190

    DOI: 10.2436/20.8080.02.84