Small area estimation of proportions under area-level compositional mixed models (2019), TEST.

María Dolores Esteban(University Miguel Hernández of Elche),  María José Lombardía (University of A Coruña), Esther López-Vizcaíno ( Galician Institute of Statistics),   Domingo Morales (University Miguel Hernández of Elche) and Agustín Pérez (University Miguel Hernández of Elche).

Abstract. This paper introduces area-level compositional mixed models by applying transformations to a multivariate Fay–Herriot model. Small area estimators of the proportions of the categories of a classification variable are derived from the new model, and the corresponding mean squared errors are estimated by parametric bootstrap. Several simulation experiments designed to analyse the behaviour of the introduced estimators are carried out. An application to real data from the Spanish Labour Force Survey of Galicia (north-west of Spain), in the first quarter of 2017, is given. The target is the estimation of domain proportions of people in the four categories of the variable labour status: under 16 years, employed, unemployed and inactive.

Keywords.  Labour Force Survey; Area-level models; Compositional data; Bootstrap; Labour status,

Programmer eXperience: A Systematic Literature Review (2019), IEEE Access

Jenny Morales (Universidad Autónoma de Chile), Cristian Rusu (Universidad Católica de Valparaíso), Federico Botella (University Miguel Hernández of Elche) and Daniela Quiñones (Universidad Católica de Valparaíso)

Abstract. Programmers use various software development artifacts in their work, such as programming environments, design documents, and programming codes. These software artifacts can be studied and improved based on usability and User eXperience (UX) factors. In this paper, we consider programmers to be a specic case of users and analyze different elements that inuence their experience in this specic context. We conducted a systematic literature review of papers published over the last ten years related to 1) the denition of the Programmer eXperience (PX); 2) the PX, UX, and usability factors regarding the programming environments, design documents, and programming codes; and 3) sets of heuristics to evaluate the software development artifacts mentioned before.We analyzed 73 articles, and the results obtained show that: 1) the important elements that inuence the PX are the motivation of programmers and the choice of tools they use in their work, such as programming environments; 2) most of the identied studies (59%) aimed to evaluate the inuence of the PX, UX, and usability on programming environments; 3) the majority of the studies (70%) used methods such as usability tests and/or heuristic evaluation methods; and 4) four sets of heuristics are used to evaluate software development artifacts in relation to programming environments, programming languages, and application programming interfaces. The results suggest that further research in this area is necessary to better understand and evaluate the concept of the PX.

Keywords. Heuristic evaluation; Programmer eXperience; systematic literature review; User eXperience; usability.