The Jackknife Interval Estimation of Parametersin Partial Least Squares Regression Modelfor Poverty Data Analysis

Pudji Ismartini, Sony Sunaryo, Setiawan Setiawan


One of the major problem facing the data modelling at social area is multicollinearity. Multicollinearity can have significant impact on the quality and stability of the fitted regression model. Common classical regression technique by using Least Squares estimate is highly sensitive to multicollinearity problem. In such a problem area, Partial Least Squares Regression (PLSR) is a useful and flexible tool for statistical model building; however, PLSR can only yields point estimations. This paper will construct the interval estimations for PLSR regression parameters by implementing Jackknife technique to poverty data. A SAS macro programme is developed to obtain the Jackknife interval estimator for PLSR.


Partial Least Squares Regression; multicollinearity; interval estimator; Jackknife

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