In this paper we present properties of an algorithm to determine the maximum likelihood estimators of the covariance matrix when two processes jointly affect the observations.
The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.
This thesis wants to answer the question of how we can recombine this small homogeneous groups to reconstruct the structure of the data set
We introduce SAGRA (Split And Group Recombining Algorithm), a cluster analysis methodology which split the data set into small homogeneous groups and later recombine those groups using Bayes factors.
We estimate the relation between the monthly amount of MSW separated voluntarily from apartments located in Santiago and the monthly price required to participate in a municipal recycling program.
We develop an algorithm that integrates a splitting process inherited from the SAR algorithm (Peña et al., 2004) with unimodality tests such as the dip test proposed by Hartigan and Hartigan (1985), and finally, we use anetwork configuration to visualize the results
This article discusses the problem of forming groups from previously split data.