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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 48 (2), July-December 2024

  • Patient-reported outcomes and survival analysis of chronic obstructive pulmonary disease patients: a two-stage joint modelling approach

    Cristina Galán-Arcicollar, Josu Najera-Zuloaga and Dae-Jin Lee

  • Non-parametric estimation of the covariate-dependent bivariate distribution for censored gap times

    Ewa Strzalkowska-Kominiak, Elisa M. Molanes-López and Emilio Letón

  • Second Order Markov multistate models

    Mireia Besalú and Guadalupe Gómez Melis

  • Conditional likelihood based inference on single index-models for motor Insurance claim severity

    Catalina Bolancé, Ricardo Cao and Montserrat Guillen

Current issue: volume 48 (1), January-June 2024

  • A diffusion-based spatio-temporal extension of Gaussian Matérn fields (invited article with discussion)

    Finn Lindgren, Haakon Bakka, David Bolin, Elias Krainski and Håvard Rue

    Abstract: Gaussian random fields with Matérn covariance functions are popular models in spatial statistics and machine learning. In this work, we develop a spatio-temporal extension of the Gaussian Matérn fields formulated as solutions to a stochastic partial differential equation. The spatially stationary subset of the models have marginal spatial Matérn covariances, and the model also extends to Whittle-Matérn fields on curved manifolds, and to more general non-stationary fields. In addition to the parameters of the spatial dependence (variance, smoothness, and practical correlation range) it additionally has parameters controlling the practical correlation range in time, the smoothness in time, and the type of non-separability of the spatio-temporal covariance. Through the separability parameter, the model also allows for separable covariance functions. We provide a sparse representation based on a finite element approximation, that is well suited for statistical inference and which is implemented in the R-INLA software. The flexibility of the model is illustrated in an application to spatio-temporal modeling of global temperature data.

    Keywords: Stochastic partial differential equations, diffusion, Gaussian fields, non-separable space-time models, INLA, finite element methods

    Pages: 3–66

    DOI:10.57645/20.8080.02.13

  • Estimation of logistic regression parameters for complex survey data: simulation study based on real survey data

    Amaia Iparragirre, Irantzu Barrio, Jorge Aramendi and Inmaculada Arostegui

    Abstract: In complex survey data, each sampled observation has assigned a sampling weight, indicating the number of units that it represents in the population. Whether sampling weights should or not be considered in the estimation process of model parameters is a question that still continues to generate much discussion among researchers in different fields. We aim to contribute to this debate by means of a real data based simulation study in the framework of logistic regression models. In order to study their performance, three methods have been considered for estimating the coefficients of the logistic regression model: a) the unweighted model, b) the weighted model, and c) the unweighted mixed model. The results suggest the use of the weighted logistic regression model is superior, showing the importance of using sampling weights in the estimation of the model parameters.

    Keywords: complex survey data, sampling weights, logistic regression, estimation of model parameters, real data based simulation study

    Pages: 67–92

    DOI:10.57645/20.8080.02.14

  • Kernel Weighting for blending probability and non-probability survey samples

    María del Mar Rueda, Beatriz Cobo, Jorge Luis Rueda-Sánchez, Ramon Ferri-García and Luis Castro-Martín

    Abstract: In this paper we review some methods proposed in the literature for combining a nonprobability and a probability sample with the purpose of obtaining an estimator with a smaller bias and standard error than the estimators that can be obtained using only the probability sample. We propose a new methodology based on the kernel weighting method. We discuss the properties of the new estimator when there is only selection bias and when there are both coverage and selection biases. We perform an extensive simulation study to better understand the behaviour of the proposed estimator.

    Keywords: Kernel weighting, survey sampling, non-probability sample, coverage bias, selection bias

    Pages: 93–124

    DOI:10.57645/20.8080.02.15

  • Small area estimation of the proportion of single-person households: Application to the Spanish Household Budget Survey

    María Bugallo, Domingo Morales and María Dolores Esteban

    Abstract: Household composition reveals vital aspects of the socioeconomic situation and major changes in developed countries for decision-making and mapping the distribution of single-person households is highly relevant and useful. Driven by the Spanish Household Budget Survey data, we propose a new statistical methodology for small area estimation of proportions and total counts of single-person households. Estimation domains are defined as crosses of province, sex and age group of the main breadwinner of the household. Predictors are based on area-level zero-inflated Poisson mixed models. Model parameters are estimated by maximum likelihood and mean squared errors by parametric bootstrap. Several simulation experiments are carried out to empirically investigate the properties of these estimators and predictors. Finally, the paper concludes with an application to real data from 2016.

    Keywords: Small area estimation, zero-inflated Poisson mixed model, area-level data, Household Budget Survey, single-person household

    Pages: 125–152

    DOI:10.57645/20.8080.02.16

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