First record of the epizoic red seaweed Polysiphonia carettia Hollenberg in the Mediterranean Sea.

Primera cita del alga roja epizoica Polysiphonia carettia Hollenberg en el mar Mediterráneo Key words. Caretta caretta, epibionts, Mediterranean Sea, Polysiphonia carettia. Palabras claves. Caretta carom, cpibiontes, Mar Mediterráneo, Polysiphonia carettia

Contribución a la corología de las macroalgas marinas bentónicas del litoral malagueño. I.

Continuando con un proyecto sobre la corología de las macroalgas marinas bentónicas del litoral malagueño (Conde, 1981; Conde, 1984; Conde & Soto, 1986) reseñamos en esta nota la presencia en la zona de 5 nuevas especies, y se confirman otras 5, citadas anteriormente en la literatura o de arribazón. La ordenación de las especies se ha realizado según la propuesta de Gallardo ét al. (1985)

Aspectos ultraestructurales de algunas coralináceas (Rhodophyta) del Mediterráneo andaluz (S. de España).

En el presente artículo se estudian e interpretan al microscopio electrónico de barrido (M.E.B.) algunos caracteres cualitativos de siete especies de algas coralináceas del litoral andaluz, pertenecientes a los géneros Corallina,Jania y Amphiroa.Using the scanning electron microscopy some qualitatives characteristics of seven seaweeds species of the mediterranean coasts of Andalucía (S. of Spain), of the genus Corallina, Junio y Amphiroa have been studied

Exploring and correcting the bias in the estimation of the Gini measure of inequality

The Gini index is probably the most commonly used indicator to measure inequality. For continuous distributions, the Gini index can be computed using several equivalent formulations. However, this is not the case with discrete distributions, where controversy remains regarding the expression to be used to estimate the Gini index. We attempt to bring a better understanding of the underlying problem by regrouping and classifying the most common estimators of the Gini index proposed in both infinite and finite populations, and focusing on the biases. We use Monte Carlo simulation studies to analyse the bias of the various estimators under a wide range of scenarios. Extremely large biases are observed in heavy-tailed distributions with high Gini indices, and bias corrections are recommended in this situation. We propose the use of some (new and traditional) bootstrap-based and jackknife-based strategies to mitigate this bias problem. Results are based on continuous distributions often used in the modelling of income distributions. We describe a simulation-based criterion for deciding when to use bias corrections. Various real data sets are used to illustrate the practical application of the suggested bias corrected procedures.Regional Government of Andalusia and the European Regional Development Fund (project P18-RT-576)Grants of the University of Granada (Unidad Científica de Excelencia “Desigualdad, Derechos Humanos y Sostenibilidad – DEHUSO” del Plan Propio; and Programa de Ayudas a la revisión de textos científicos de la Facultad de Ciencias Económicas y Empresariales

Single Imputation Methods and Confidence Intervals for the Gini Index

This research has been partially supported by the Ministry of Economy, Industry and Competitiveness, the Spanish State Research Agency (SRA) and European Regional Development Fund (ERDF) (project reference ECO2017-86822-R). This research has been partially supported by the Ministry of Economy, Industry and Competitiveness, the Spanish State Research Agency (SRA) and European Regional Development Fund (ERDF) (project reference ECO2017-84138-P).The problem of missing data is a common feature in any study, and a single imputation method is often applied to deal with this problem. The first contribution of this paper is to analyse the empirical performance of some traditional single imputation methods when they are applied to the estimation of the Gini index, a popular measure of inequality used in many studies. Various methods for constructing confidence intervals for the Gini index are also empirically evaluated. We consider several empirical measures to analyse the performance of estimators and confidence intervals, allowing us to quantify the magnitude of the non-response bias problem. We find extremely large biases under certain non-response mechanisms, and this problem gets noticeably worse as the proportion of missing data increases. For a large correlation coefficient between the target and auxiliary variables, the regression imputation method may notably mitigate this bias problem, yielding appropriate mean square errors. We also find that confidence intervals have poor coverage rates when the probability of data being missing is not uniform, and that the regression imputation method substantially improves the handling of this problem as the correlation coefficient increases.Ministry of Economy, Industry and Competitiveness Spanish State Research Agency (SRA)European Commission ECO2017-84138-