El test mongil de actividades de la vida diaria básicas, instrumentales y avanzadas y su utilidad en el envejecimiento

El declive funcional del organismo parece comenzar cuando desciende la secreción de hormona del crecimiento, manifestándose el envejecimiento cuando decrece la vitalidad y proporcionalmente aumenta la vulnerabilidad. El envejecimiento es un proceso decreciente intrínsecamente unido a la fragilidad, que es una condición que antecede a la discapacidad. En gerontología se precisa un abordaje global que integre las consecuencias de la enfermedad y sus dimensiones psicológicas y sociales, pues todo ello repercute en lo que en términos de salud llamamos función del individuo y por ende en su calidad de vida. Es prioritario llegar a la cuantificación y objetivación del estado funcional de los individuos integrantes de poblaciones para que el resultado, como base del estudio poblacional, sirva de referencia y permita conclusiones comparativas. Es por ello que partiendo de los test CM 98 de Actividades de la Vida Diaria (AVD) Básicas (B) e Instrumentales (I) y siguiendo la estructura, criterios de construcción y características de estas pruebas, se proponen los test Mongil de AVD B, I y Avanzadas (A) que suponen una innovación pues con tan sólo un cambio en el orden de aplicación de las preguntas muestran unos beneficios reseñables. Entre los mismos destacamos la mayor facilidad para poder ejecutar estas pruebas pues presentan un orden lógico y como resultado del mismo se obtiene información vinculada con la presencia de deterioro cognitivo y/o demencia en los tres test. Otro aspecto a considerar es la posibilidad de ser utilizados como prueba útil en el transcurso de un psicodiagnóstico en los distintos niveles asistenciales donde se atiende a personas mayores: consultas para aquellos que residen en la comunidad, residencias y hospitales. Estas mediciones tienen, además, un alto interés no sólo para los clínicos sino también para los economistas de la salud por su relación directa con la dependencia.The functional deterioration of the organism seems to start when the secretion of the growth hormone descends, and this hormone starts showing the ageing when the vitality descends and proportionally the vulnerability increases. Ageing is a process that descends intrinsically and it is connected with the fragility, which is a condition that precedes the disabilities. In terms of gerontology is required a global approach that settles in the consequences of the disease and its psychological and social dimensions, because all this has a repercussion in terms of health that we know as “functions” of the individual and moreover in life’s quality. It is a priority to reach the quantifications or objectivity of the functional phase of the individuals that form the population, to create a result based on the research of the population that can serve as a reference and also can allow comparative conclusions. Taking into account the test CM 98 Basic activities of daily living (BADL) and instrumental activities of daily living (IADL) following the structure, the criterion of construction and the characteristics of the test, It is proposed to use test Mongil of BADL, IADL and Advanced activities of daily living (AADL), that suppose an innovation because with a simple change in the order of the application of the questions, they show prominent benefits. Basing on theses benefits we outline an increase on the facility of executing these tests because they present a logical order and, as a result of this fact is possible to obtain information related with the presence of cognitive impairment and/or dementia in the tests. Another aspect to consider is the possibility of being used as a useful test in the course of a psychodiagnosis in the various levels of care where seniors are attended: consultations for those residing in the community, nursing home and hospitals. These measurements also have a great interest not only to clinicians but also for health economists for its direct relationship with the dependence

Robust Approaches for Fuzzy Clusterwise Regression Based on Trimming and Constraints

Three different approaches for robust fuzzy clusterwise regression are reviewed. They are all based on the simultaneous application of trimming and constraints. The first one follows from the joint modeling of the response and explanatory variables through a normal component fitted in each cluster. The second one assumes normally distributed error terms conditional on the explanatory variables while the third approach is an extension of the Cluster Weighted Model. A fixed proportion of “most outlying” observations are trimmed. The use of appropriate constraints turns these problem into mathematically well-defined ones and, additionally, serves to avoid the detection of non-interesting or “spurious” linear clusters. The third proposal is specially appealing because it is able to protect us against outliers in the explanatory variables which may act as “bad leverage” points. Feasible and practical algorithms are outlined. Their performances, in terms of robustness, are illustrated in some simple simulated examples.Spanish Ministerio de Economía y Competitividad, grant MTM2017-86061-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León and FEDER, grant VA005P17 and VA002G18

Comments on “The power of monitoring: how to make the most of a contaminated multivariate sample”

These are comments on the invited paper “The power of monitoring: How to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony Atkinson and Aldo Corbellini.Spanish Ministerio de Economía y Competitividad, grant MTM2017-86061-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León and FEDER, grant VA005P17 and VA002G18

The TCLUST Approach to Robust Cluster Analysis

Producción CientíficaA new method for performing robust clustering is proposed. The method is designed with the aim of ¯tting clusters with di®erent scat- ters and weights. A proportion ® of contaminating data points is also allowed. Restrictions on the ratio between the maximum and the min- imum eigenvalues of the groups scatter matrices are introduced. These restrictions make the problem to be well-de¯ned guaranteeing the ex- istence and the consistency of the sample estimators to the population parameters.Estadística e I

Robust estimation of mixtures of regressions with random covariates, via trimming and constraints

Producción CientíficaA robust estimator for a wide family of mixtures of linear regression is presented. Robustness is based on the joint adoption of the Cluster Weighted Model and of an estimator based on trimming and restrictions. The selected model provides the conditional distribution of the response for each group, as in mixtures of regression, and further supplies local distributions for the explanatory variables. A novel version of the restrictions has been devised, under this model, for separately controlling the two sources of variability identified in it. This proposal avoids singularities in the log-likelihood, caused by approximate local collinearity in the explanatory variables or local exact fits in regressions, and reduces the occurrence of spurious local maximizers. In a natural way, due to the interaction between the model and the estimator, the procedure is able to resist the harmful influence of bad leverage points along the estimation of the mixture of regressions, which is still an open issue in the literature. The given methodology defines a well-posed statistical problem, whose estimator exists and is consistent to the corresponding solution of the population optimum, under widely general conditions. A feasible EM algorithm has also been provided to obtain the corresponding estimation. Many simulated examples and two real datasets have been chosen to show the ability of the procedure, on the one hand, to detect anomalous data, and, on the other hand, to identify the real cluster regressions without the influence of contamination. Keywords Cluster Weighted Modeling · Mixture of Regressions · Robustnes

Robust estimation for mixtures of Gaussian factor analyzers, based on trimming and constraints

Producción CientíficaMixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneous population, offering - at the same time - dimension reduction and model-based clustering. Unfortunately, the high prevalence of spurious solutions and the disturbing effects of outlying observations, along maximum likelihood estimation, open serious issues. In this paper we consider restrictions for the component covariances, to avoid spurious solutions, and trimming, to provide robustness against violations of normality assumptions of the underlying latent factors. A detailed AECM algorithm for this new approach is presented. Simulation results and an application to the AIS dataset show the aim and effectiveness of the proposed methodology

The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers.

Producción CientíficaMixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneous population, offering – at the same time – dimension reduction and model-based clustering. The high prevalence of spurious solutions and the disturbing effects of outlying observations in maximum likelihood estimation may cause biased or misleading inferences. Restrictions for the component covariances are considered in order to avoid spurious solutions, and trimming is also adopted, to provide robustness against violations of normality assumptions of the underlying latent factors. A detailed AECM algorithm for this new approach is presented. Simulation results and an application to the AIS dataset show the aim and effectiveness of the proposed methodology.Ministerio de Economía y Competitividad and FEDER, grant MTM2014-56235-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León, grant VA212U13, by grant FAR 2015 from the University of Milano-Bicocca and by grant FIR 2014 from the University of Catania

Robust Principal Component Analysis Based On Trimming Around Affine Subspaces

Principal Component Analysis (PCA) is a widely used technique for reducing dimensionality of multivariate data. The principal component subspace is defined as the affine subspace of a given dimension d giving the best fit to the data. However, PCA suffers from a well-known lack of robustness. As a robust alternative, one can resort to an impartial trimming based approach. Here one searches for the best subsample containing a proportion 1 − α of the observations, with 0 < α < 1, and the best d-dimensional affine subspace fitting this subsample, yielding the trimmed principal component subspace. A population version will be given and existence of a solution to both the sample and population problem will be proven. Moreover, under mild conditions, the solutions of the sample problem are consistent toward the solutions of the population problem. The robustness of the method is studied by proving quantitative robustness, computing the breakdown point, and deriving the influence functions. Furthermore, asymptotic efficiencies at the normal model are derived, and finite sample efficiencies of the estimators are studied by means of a simulation studyEstadística e I

Robustness and Outliers

Producción CientíficaUnexpected deviations from assumed models as well as the presence of certain amounts of outlying data are common in most practical statistical applications. This fact could lead to undesirable solutions when applying non-robust statistical techniques. This is often the case in cluster analysis, too. The search for homogeneous groups with large heterogeneity between them can be spoiled due to the lack of robustness of standard clustering methods. For instance, the presence of (even few) outlying observations may result in heterogeneous clusters artificially joined together or in the detection of spurious clusters merely made up of outlying observations. In this chapter we will analyze the effects of different kinds of outlying data in cluster analysis and explore several alternative methodologies designed to avoid or minimize their undesirable effects.Ministerio de Economía, Industria y Competitividad (MTM2014-56235-C2-1-P)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA212U13