In this paper, taking into account the possible development of serious disorders of the proliferation of the plasmatic cells, we focus on a dataset concerning the prediction among a chronic disease which has the higher risk of malignant transformation. The purpose of this paper is to argue in favour of the use of multiple correspondence analysis (MCA) as a powerful exploratory tool for such data. Following usual regression terminology, we refer to the primary variable as the response variable and the others as explanatory or predictive variables. As an alternative, a copula based methodology for prediction modeling and an algorithm to stimulate data are proposed.
J. S. de Tibeiro, Jules; Kumar, Pranesh; and Khine S. Myat, Khine
"AN INTEGRATED APPROACH TO REGRESSION ANALYSIS IN MULTIPLE CORRESPONDENCE ANALYSIS AND COPULA BASED MODELS,"
Journal of Statistics Applications & Probability: Vol. 1:
2, Article 1.
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol1/iss2/1