COPULA BASED PREDICTION MODEL - (NIM : 10108090); Pembimbing : Khreshna I.A. Syuhada, M.Sc, Ph.D, OCTAVINA
Pearson's correlation coefficient is a measure of association which widely used in many fields. However, Pearson's correlation coefficient has limitation to detect non-linear association. Kendall's Tau and Spearman's Rho are measures of association which can detect both linear and non-linear association and can be stated in a multivariate distribution function model, named copula. A prediction model is established to predict a value for future observation. Generally, the model prediction is based on Pearson's correlation coefficient. However, considering the limitation that Pearson's correlation coefficient has, in this final project will be established a prediction model based on copula.