THE USE OF PENALIZED WEIGHTED LEAST SQUARE TO OVERCOME CORRELATIONS BETWEEN TWO RESPONSES

Abstract

The non-parametric regression model can consider two correlated responses. However, for these conditions, we cannot use the usual estimation process because there are violations of assumptions. To solve this problem, we use a penalized weighted least square involving knots, smoothing parameters, and weighting in the estimation criteria simultaneously. The estimation process involves a weighted criteria matrix in the estimation criteria. Estimation results show that the estimated two-response non-parametric regression function with penalized spline is a linear estimation class in y response observation and depends on the knot point and smoothing parameter. Furthermore, the use of the model on toddler growth data shows some changes in the pattern of weight and height gain. The pattern segmentation that experienced a gradual increase was age 7-43 months for weight and age 6-54 months for height