The ordinary least square (OLS) method is very efficient in estimating the regression parameters a linear model under classical assumptions. If contains outliers, performance of OLS estimator becomes imprecise. Multicollinearity another issue that can reduce estimator. This study proposed Robust Jackknife Kibria-Lukman (RJKL) based on M-estimator to deal with multicollinearity and outliers. We ...

Multicollinearity is a very common problem in studies that employ path analysis agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize effects of multicollinearity. Seven explanatory were measured – fruit mass (...

We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single paramet...

We argue for the adoption of a predictive approach to model specification. Specifically, we derive the difference between means and the ratio of determinants of covariance matrices when a subset of explanatory variables is included or excluded from a regression. For several special cases these measures are shown to be related to widely used tools for studying model specification. Results for a ...

Multicollinearity negatively affects the efficiency of maximum likelihood estimator (MLE) in both linear and generalized models. The Kibria Lukman (KLE) was developed as an alternative to MLE handle multicollinearity for regression model. In this study, we proposed Logistic Kibria-Lukman (LKLE) logistic We theoretically established superiority condition new over MLE, ridge (LRE), Liu (LLE), Liu...

Testing for collinearity continues to be a controversial issue in the literature. Multicollinearity detection criteria, such as variance inflation factor, often fail detect true extent of multicollinearity. In this article, we propose utilizing Bayesian approach an attractive alternative. Under approach, recommend comparing marginal posterior regression parameters under two different priors. If...

The Human Development Index (HDI) is an important indicator in measuring the success of national development. Central Java with a high population can be considered as obstacle and driver To find out factors that affect HDI, it necessary to make model. One statistical methods used multiple linear regression analysis. However, modeling there are assumptions must met, namely linearity, normality, ...