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Next issue: volume 42 (1), January-June 2018

  • Using a Bayesian change-point statistical model with autoregressive terms to study the monthly number of dispensed asthma medications by public health services

    José André Mota de Queiroz, Davi Casale Aragon, Luane Marques de Mello, Isolde Terezinha Santos Previdelli and Edson Martinez

  • Evaluating the complexity of some families of functional data

    Enea Bongiorno, Aldo Goia and Philippe Vieu

  • Preliminary test and Stein-type shrinkage LASSO-based estimators

    Mina Norouzirad and Mohammad Arashi

  • Heteroscedasticity irrelevance when testing means difference

    Pablo Flores and Jordi Ocaña

Current issue: volume 41 (2), July-December 2017

  • Hierarchical models with normal and conjugate random effects: a review (invited article)

    Geert Molenberghs, Geert Verbeke and Clarice G.B. Demétrio

    Abstract: Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework to model hierarchical data subject to within-unit correlation and/or overdispersion. The framework extends classical overdispersion models as well as generalized linear mixed models. Subsequent work has examined various aspects that lead to the formulation of several extensions. A unified treatment of the model framework and key extensions is provided. Particular extensions discussed are: explicit calculation of correlation and other moment-based functions, joint modelling of several hierarchical sequences, versions with direct marginally interpretable parameters, zero-inflation in the count case, and influence diagnostics. The basic models and several extensions are illustrated using a set of key examples, one per data type (count, binary, multinomial, ordinal, and time-to-event).

    Keywords: Conjugacy, frailty, joint modelling, marginalized multilevel model, mixed model, overdispersion, underdispersion, variance component, zero-inflation.

    Pages: 191–254

    DOI: 10.2436/20.8080.02.58

  • A bivariate response model for studying the marks obtained in two jointly-dependent modules in higher education

    Emilio Gómez-Déniz, Nancy Dávila Cárdenes and María D. García Artiles

    Abstract: We study the factors which may affect students’ marks in two modules, mathematics and statistics, taught consecutively in the first year of a Business Administration Studies degree course. For this purpose, we introduce a suitable bivariate regression model in which the dependent variables have bounded support and the marginal means are functions of explanatory variables. The marginal probability density functions have a classical beta distribution. Simulation experiments were performed to observe the behaviour of the maximum likelihood estimators. Comparisons with univariate beta regression models show the proposed bivariate regression model to be superior.

    Keywords: Beta distribution, bivariate beta distribution, conditional distributions, covariate, marginal distributions, regression, mathematics, statistics, business studies.

    Pages: 255–276

    DOI: 10.2436/20.8080.02.59

  • Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices

    Xavier Barber, David Conesa, Antonio López-Quílez, Asunción Mayoral, Javier Morales and Antoni Barber

    Abstract: A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the spatial bioclimatic probability distribution of each bioclimatic index, which allows researchers to obtain the probability of each location belonging to different bioclimates. The methodology is evaluated on two indices in the Island of Cyprus.

    Keywords: Bioclimatology, geostatistics, parallel computation, spatial prediction.

    Pages: 277–296

    DOI: 10.2436/20.8080.02.60

  • The Pareto IV power series cure rate model with applications

    Diego I. Gallardo, Yolanda M. Gómez, Barry C. Arnold and Héctor W. Gómez

    Abstract: Cutaneous melanoma is thought to be triggered by intense, occasional exposure to ultraviolet radiation, either from the sun or tanning beds, especially in people who are genetically predisposed to the disease. When skin cells are damaged by ultraviolet light in this way, often showing up as a sunburn, they are more prone to genetic defects that cause them to rapidly multiply and form potentially fatal (malignant) tumors. Melanoma originates in a type of skin cell called a melanocyte, such cells help produce the pigments of our skin, hair, and eyes. We propose a new cure rate survival regression model for predicting cutaneous melanoma. We assume that the unknown number of competing causes that can influence the survival time is governed by a power series Distribution and that the time until the tumor cells are activated follows the Pareto IV distribution. The parameter estimation is based on the EM algorithm which for this model can be implemented in a simple way in computational terms. Simulation studies are presented, showing the good performance of the proposed estimation procedure. Finally, two real applications related to a cutaneous melanoma and melanoma data sets are presented.

    Keywords: Competing risks, cure rate models, EM algorithm, Pareto IV distribution, power series distribution.

    Pages: 297–318

    DOI: 10.2436/20.8080.02.61

  • Estimating regional social accounting matrices to analyse rural development

    Alfredo Mainar-Causapé, José Manuel Rueda Cantuche, M. Alejandro Cardenete, Patricia Fuentes-Saguar, M. Carmen Delgado, Fabien Santini and Sergio Gómez y Paloma

    Abstract: This paper has two complementary objectives: on the one hand, it introduces the EURO method for the estimation of (regional) Social Accounting Matrices. This method is widely used by Eurostat for the estimation of missing national Supply, Use and Input-output tables but it has not been used before within the context of social accounting matrices or of regional statistics and/or regional impact analyses. On the other hand, this work discusses the possibility of producing non-survey based regional Social Accounting Matrices that may eventually allow the user to carry out impact analyses such as those of rural development policies, among others. The analysis is carried out for 12 selected European regions based on clusters.

    Keywords: Social accounting matrices, rural development, European regions, impact analysis.

    Pages: 319–346

    DOI: 10.2436/20.8080.02.62

  • Joint models for longitudinal counts and left-truncated time-to event data with applications to health insurance

    Xavier Piulachs, Ramon Alemany, Montserrat Guillén and Dimitris Rizopoulos

    Abstract: Aging societies have given rise to important challenges in the field of health insurance. Elderly policyholders need to be provided with fair premiums based on their individual health status, whereas insurance companies want to plan for the potential costs of tackling lifetimes above mean expectations. In this article, we focus on a large cohort of policyholders in Barcelona (Spain), aged 65 years and over. A shared-parameter joint model is proposed to analyse the relationship between annual demand for emergency claims and time until death outcomes, which are subject to left truncation. We compare different functional forms of the association between both processes, and, furthermore, we illustrate how the fitted model provides time-dynamic predictions of survival probabilities. The parameter estimation is performed under the Bayesian framework using Markov chain Monte Carlo methods.

    Keywords: Joint models, panel count data, left truncation, Bayesian framework, health insurance.


    DOI: 10.2436/20.8080.02.63

  • Statistical and machine learning approaches for the minimization of trigger errors in parametric earthquake catastrophe bonds

    Laura Calvet, Madeleine Lopeman, Jésica de Armas, Guillermo Franco and Angel A. Juan

    Abstract: Catastrophe bonds are financial instruments designed to transfer risk of monetary losses arising from earthquakes, hurricanes, or floods to the capital markets. The insurance and reinsurance industry, governments, and private entities employ them frequently to obtain coverage. Parametric catastrophe bonds base their payments on physical features. For instance, given parameters such as magnitude of the earthquake and the location of its epicentre, the bond may pay a fixed amount or not pay at all. This paper reviews statistical and machine learning techniques for designing trigger mechanisms and includes a computational experiment. Several lines of future research are discussed.

    Keywords: Catastrophe bonds, risk of natural hazards, classification techniques, earthquakes, insurance.

    Pages: 373–392

    DOI: 10.2436/20.8080.02.64

  • Horizontal collaboration in freight transport: concepts, benefits and environmental challenges

    Adrián Serrano-Hernández, Angel A. Juan, Javier Faulin and Elena Perez-Bernabeu

    Abstract: Since its appearance in the 1990s, horizontal collaboration (HC) practices have revealed themselves as catalyzers for optimizing the distribution of goods in freight transport logistics. After introducing the main concepts related to HC, this paper offers a literature review on the topic and provides a classification of best practices in HC. Then, the paper analyses the main benefits and optimization challenges associated with the use of HC at the strategic, tactical, and operational levels. Emerging trends such as the concept of ‘green’ or environmentally-friendly HC in freight transport logistics are also introduced. Finally, the paper discusses the need of using hybrid optimization methods, such as simheuristics and learnheuristics, in solving some of the previously identified challenges in real-life scenarios dominated by uncertainty and dynamic conditions.

    Keywords: Horizontal collaboration, freight transport, sustainable logistics, supply chain management, combinatorial optimization.

    Pages: 393–414

    DOI: 10.2436/20.8080.02.65

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