We introduce a novel regression model for the conditional left and right tail of a possibly heavy-tailed response. The proposed model can be used to learn the effect of covariates on an extreme value setting via a Lasso-type specification based on a Lagrangian restriction. Our model can be used to track if some covariates are significant for the lower values, but not for the (right) tail—and vice versa; in addition to this, the proposed model bypasses the need for conditional threshold selection in an extreme value theory framework. We assess the finite-sample performance of the proposed methods through a simulation study that reveals that our method recovers the true conditional distribution over a variety of simulation scenarios, along with being accurate on variable selection. Rainfall data are used to showcase how the proposed method can learn to distinguish between key drivers of moderate rainfall, against those of extreme rainfall. Supplementary materials accompanying this paper appear online.
Portuguese Labor force survey, from 4th quarter of 2014 onwards, started georeferencing the sampling units, namely the dwellings in which the surveys are carried. This opens new possibilities in analysing and estimating unemployment and its spatial distribution across any region. The labor force survey choose, according to an preestablished sampling criteria, a certain number of dwellings across the nation and survey the number of unemployed in these dwellings. Based on this survey, the National Statistical Institute of Portugal presently uses direct estimation methods to estimate the national unemployment figures. Recently, there has been increased interest in estimating these figures in smaller areas. Direct estimation methods, due to reduced sampling sizes in small areas, tend to produce fairly large sampling variations therefore model based methods, which tend to "borrow strength" from area to area by making use of the areal dependence, should be favored. These model based methods tend use areal counting processes as models and typically introduce spatial dependence through the model parameters by a latent random effect. In this paper, we suggest modeling the spatial distribution of residential buildings across Portugal by a Log Gaussian Cox process and the number of unemployed per residential unit as a mark attached to these random points. Thus the main focus of the study is to model the spatial intensity function of this marked point process. Number of unemployed in any region can then be estimated using a proper functional of this marked point process. The principal objective of this point referenced method for unemployment estimation is to get reliable estimates at higher spatial resolutions and at the same time incorporate in the model the auxiliary information available at residential units such as average income or education level of individuals surveyed in these units.
Introduction: The Self-Care Dependency Evaluation Form assesses dependency in performing self-care activities, but its original version is extensive and provides redundant information. The present study aims to scrutinise the items of the scale with the purpose of creating a revised version and to evaluate its psychometric properties. Methods: The study was conducted in two phases. In the first phase, an exploratory and correctional analysis of the items of the original form was performed from a database with 282 participants, followed by a review by a panel of experts who analysed the discriminatory ability and the contribution and relevance of each item, which resulted in the revised version. In the second phase, a new study with a sample comprising 150 participants was conducted to test the psychometric properties of the revised version. All ethical aspects and matters of confidentiality and privacy were assured. Results: The scale with 27 items shows good internal consistency, ranging from 0.67 (taking medication) to 0.96 (walking). It was moderately correlated with the Barthel Index and the Lawton and Brody Scale, proven to be a discriminatory measurement instrument. Discussion/Conclusion: This measure will enable health professionals to better evaluate self-care activities and provide more efficient, simple and effective prescriptions.
A aliança de trabalho é um preditor da eficácia da intervenção psicológica e envolve o trabalho colaborativo entre psicólogo/a e cliente. Neste estudo, analisa-se em que medida a experiência profissional dos/as psicólogos/as, o rendimento económico e a (in)decisão do/da cliente predizem a aliança de trabalho inicial e se esses fatores e a aliança inicial predizem a aliança final. Participaram 49 clientes do aconselhamento de construção da carreira, com idade entre os 17 e os 52 anos e sete psicólogas. Utilizou-se um questionário sociodemográfico, a Escala de Decisão de Carreira e o Inventário da Aliança Terapêutica. Resultados de análises de regressão linear múltipla indicam a experiência profissional dos/as psicólogos/as e o rendimento económico dos/as clientes como preditores da aliança inicial. A experiência profissional dos/as psicólogos/as, a indecisão final dos/as clientes e a aliança inicial predizem a aliança final. Os resultados deste estudo são discutidos, retirando-se implicações para a investigação e prática.
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