E. Boj, J. F. Vera
A two-steps procedure of cluster distance-based regression is proposed. In the first step, the dimension reduction step, the number of elements to be represented using dissimilarities are reduced. Given a dissimilarity matrix obtained from the original data set, it is employed a combination of a k-means procedure for dissimilarities and multidimensional scaling in determining a classification of the observed elements and in determining a reduced latent predictor space. The aim of this cluster-multidimensional procedure is the classification of the objects into clusters while simultaneously the cluster centres are represented in a low dimensional space. In the second step, the prediction step, the reduced clustered space is the latent predictor in a distance-based regression, where the weighted average vector within each cluster is projected on the continuous response variable. The performance of the procedure is illustrated analizing real data in an econometric context.
Palabras clave / Keywords: cluster, multidimensional scaling, distance-based prediction
Programado
Sesión GT02-1: Análisis Multivariante y Clasificación (AMyC-1). Organizadora: Eva Boj del Val
29 de mayo de 2018 10:30
Sala 5
