INE building
NIPO 096-22-006-0
ISSN 2695-9070

Spanish Journal of Statistics (SJS)

The Spanish Journal of Statistics (SJS) is the official journal of the National Statistics Institute of Spain (INE). The journal replaces Estadística Española, edited and published in Spanish by the INE for more than 60 years, which has long been highly influential in the Spanish scientific community.

The journal seeks papers containing original theoretical contributions of direct or potential value in applications, but the practical implications of methodological aspects are also welcome. The levels of innovation and impact are crucial in the papers published in SJS.

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    SJS aims to publish original sound papers on either well-established or emerging areas in the scope of the journal. The objective of papers should be to contribute to the understanding of official statistics and statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. Within these parameters, the kinds of contribution considered include:

    • Official Statistics.
    • Theory and methods.
    • Computation and simulation studies that develop an original methodology.
    • Critical evaluations and new applications
    • Development, evaluation, review, and validation of statistical software and algorithms.
    • Reviews of methodological techniques.
    • Letters to the editor.

    One volume is published annually in two issues, but special issues covering up-to-date challenging topics may occasionally be published.

Issue 3. 2021

 Full issue DOI:

Presentation of Volume 3, 1, 2021

José María Sarabia

DOI:   Nº SJS/003

Some recent methods for analyzing high dimensional time series

Daniel Peña

DOI:   Nº SJS/003

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  • Abstract

    This article analyzes six recent advances in the analyses of high dimensional time series. The first two procedures have the objective of understanding the structure of the set of series: dy- namic quantiles for data visualization and clustering by dependency to split the series into homo-geneous groups. The other four methods are oriented to modeling and forecasting large sets of time series by dynamic factor models (DFM): procedures for determining the number of factors, for estimating DFM with cluster structure, for forecasting generalized dynamic factor models and for modeling matrices of time series are described. Some comments about the future evolution of the field of dependent high dimensional data are included in the conclusions.

Misreported longitudinal data in epidemiology: Review of mixture-based advances and current challenges

David Moriña, Amanda Fernández-Fontelo, Alejandra Cabaña, Argimiro Arratia, Pedro Puig

DOI:   Nº SJS/003

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  • Abstract

    The problem of dealing with misreported data is very common in a wide range of contexts and for different reasons. This has been and still is an important issue for data analysts and statisticians as not accounting for it could led to biased estimates and conclusions, and in many cases that would have implications in a posterior decision making process, as we all have seen in the current worldwide Covid-19 pandemic. In the last few years, many approaches have been proposed in the literature to accomodate data presenting this issue, especially in the fields of epidemiology and public health but also in other areas as social science. In this work, a comprehensive review of the recently proposed methods based on mixture models for longitudinal data (correlated and uncorrelated) is presented and several examples of application are discussed, including several approaches to the burden of Covid-19 infection cases in Spain and different approaches to deal with underreported registries of human papillomavirus infections and genital warts in Catalunya.

A note on explicit expressions for moments of order statistics

Fredy Castellares, Artur J. Lemonte, Marcos A.C. Santos

DOI:   Nº SJS/003

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  • Abstract

    Closed-form expressions for moments of order statistics from the normal, log-normal, gamma and beta distributions were provided in the statistics literature. In particular, the explicit expressions involve the Lauricella function of type A, and the generalized Kampé de Fériet function. We note that the expressions provided by the author do not appear correct, which implies that the expressions cannot be recommended to users. An alternative closed-form expression for moments of order statistics is then provided. We also consider numerical studies to show that the formulas we provide deliver satisfactory results.