A characterization of distributions based on linear regression of order statistics and record values

We obtain the family of distributions for which the regression of one order statistic on another, not necessarily adjacent, is linear. As a consequence, we present a characterization of uniform distributions on an interval. We also characterize the distributions that appear when we impose the condition of linearity of regression for record values

Análisis de sensibilidad en el muestreo en poblaciones finitas

Las conclusiones de un análisis estadístico dependen en gran medida de las hipótesis de partida, en general expresadas en términos de un modelo, y de las observaciones experimentales. Esta dependencia motiva la necesidad de estudiar estimadores, tests ... que sean robustos ante determinadas perturbaciones del modelo, de identificar las observaciones atípicas o amortiguar el efecto de su presencia, y de evaluar el impacto de cada una de las opservaciones sobre las conclusiones del estudio. Naturalmente, la reflexión anterior es válida en el muestreo en poblaciones finitas. El estudio de las referencias a los problemas antes señalados, en el contexto del muestreo en poblaciones finitas, pone de manifiesto un desarrollo desigual de los distintos tópicos, y la importancia del esquema probabilístico desde el que se aborda la inferencia: población fija o modelo de superpoblación. En este trabajo se presentan algunas de las referencias sobre los problemas descritos. En particular, se recogen algunos resultados referentes a la robustez del estimador de razón y una recopilación exhaustiva de los diagnósticos de influencia en el muestreo en poblaciones finitas

A new approach to influence analysis in linear models

propose a new approach to the study of influence in the General Linear Model based on conditional bias. This approach enables us to apply such an analysis to all particular cases of this model. The theoretical foundation, on which this approach is based, does not presuppose a particular hypothesis on the distribution of the variables. Applying the results obtained to the Multiple Linear Regression Model, measures of influence are obtained as already proposed by other authors. Finally we carry out an application of the results on the analysis of covariance

Influence analysis on discriminant coordinates

Discriminant analysis (DA), particularly Discriminant Coordinates (DC), is broadly applied in the scientific literature and included in many statistical software packages. DC is used to analyze biomedical data, especially for differential diagnosis on the basis of laboratory profiles. Papers handling influence analysis in DA can be found in the literature; however this topic has been scarcely touched upon in DC. In this paper the case-deletion approach is followed to introduce a perturbation in the data and influence measures are proposed to assess the effect on three statistics of interest: the transformation matrix, canonical directions and configuration of the sample centroids.Ministerio de Ciencia y Tecnologí

El sesgo condicionado en el análisis de influencia: una revisión

El sesgo condicionado se ha propuesto como diagnóstico de influencia en distintos modelos y tecnicas estadísticas. Tratando de recoger una visión global de la utilidad del concepto, en este trabajo se hace una revisión general del mismo relacionandolo con la curva de sensibilidad y la curva de influencia muestral. Además, se señalan posibles líneas de trabajo que permitiran abordar el análisis de la influencia a través de este enfoque en una gran variedad de tecnicas estadísticas

El sesgo condicionado en el análisis de influencia: una revisión

El sesgo condicionado se ha propuesto como diagnóstico de influencia en distintos modelos y técnicas estadísticas. Tratando de recoger una visión global de la utilidad del concepto, en este trabajo se hace una revisión general del mismo relacionándolo con la curva de sensibilidad y la curva de influencia muestral. Además, se señalan posibles líneas de trabajo que permitirán abordar el análisis de la influencia a través de este enfoque en una gran variedad de técnicas estadísticas

Usos del e-learning en las Universidades Andaluzas: Estado de la situación y análisis de buenas prácticas. (Versión completa)

Secretaría General de Universidades, Investigación y Tecnología P07-SE-J.0267

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Publisher Copyright: © 2021, The Author(s).Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.Peer reviewe

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages