Dimensionality reduction with image data

A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images

Entrevista a Daniel Peña

Entrevista con Daniel Peña, con motivo de la finalización de su mandato, tras ocho años como Rector de la Universidad Carlos III

A multivariate Kolmogorov-Smornov test of goodnes of fit

This paper presents a distribution free multivariate Kolmogorov-Smirnov goodıness of fit test. The test uses an statistic which is built using Rosenblatt's transformation and an algorithm is developed to compute it in the bivariate case. An approximate test, that can be easily computed in any dimension, is also presented. The power of these multivariate tests is studied in a simulationı study

Un contraste de normalidad basado en la transformación de Box-Cox

En este trabajo se presenta un estudio de las posibilidades de utilizar las transformaciones Box-Cox para testar la Normalidad de una variable aleatoria. Se derivan los tests de Razón de Verosimilitud y Multiplicador de Lagrange correspondientes y se presenta un estudio de simulación de la potencia comparada del test LR frente a otros tipos de «omnibus» y direccionales.----------------------------------------------------------------------------In this paper we show how to use the farnily of transforrnations by Box and Cox as a test for norrnality. Likelihood ratio and lagrange rnultiplier test are derivated, and it is showed the results of a Monte Cario sirnulation about the power of LR test faced with sorne others alternatives.Publicad

Clustering and classifying images with local and global variability

A procedure for clustering and classifying images determined by three classification variables is presented. A measure of global variability based on the singular value decomposition of the image matrices, and two average measures of local variability based on spatial correlation and spatial changes. The performance of the procedure is compared using three different databases

Missing observations and additive outliers in time series models

El trabajo se centra en la estimacion de valores ausentes en series generadas por modelos ARIMA en general no estacionarios. Primero, se considera la estructura del filtro de interpolacion optimo, usando la funciones de autocorrelacion dual o inversa, y se analiza la relacion con el problema de estimacion de observaciones atipicas y con el problema de descomponer una serie en señal mas ruido. Los resultados se generalizan, primero, para el caso de observaciones ausentes cerca del final de la seriedespues, para el caso de una secuencia de observaciones ausentes y, finalmente, para el caso general de cualquier numero de secuencias de cualquier longitud. El estimador optimo se puede expresar siempre de forma compacta en terminos de la funcion de autocorrelacion dual, en ocasiones truncada. El error cuadratico medio es igual a la inversa de la matriz de autocovarianzas duales. (amh) (dp) (mac

Estimation of the common component in Dynamic Factor Models

One of the most effective techniques that allows a low-dimensional representation of Big Datasets is the Dynamic Factor Model (DFM). We analyze the finite sample performance of the well-known Principal Component estimator for the common component under different scenarios. Simulation results show that for data samples with large number of observations and small time series dimension, the variance-covariance matrix specification with lags provides better estimations than the classic variance-covariance matrix. However, in high-dimension data samples the classic variance-covariance matrix performs better no matter the sample size. Second, we apply the Principal Component estimator to obtain estimates of the business cycles of the Euro Area and its country members. This application, together with a cluster analysis, studies the phenomenon known as the Two-Speed Europe with two groups of countries not geographically related.The first author acknowledge financial support from the Spanish Ministry of Education, Culture and Sport for the Training of University Teachers, and the second author from the Spanish Ministry of Education and Science, research project ECO2015-66593-P. Supported by the Spanish Ministerio de Educación, Cultura y Deporte under grant FPU15/03983