Reproducing Kernel Hilbert Space Approach to Multiresponse Smoothing Spline Regression Function

In statistical analyses, especially those using a multiresponse regression model approach, mathematical that describes functional relationship between more than one response variables and or predictor is often involved. The these expressed by function. the nonparametric (MNR) part of model, estimating function becomes main problem, as there correlation responses such it necessary to include symmetric weight matrix into penalized weighted least square (PWLS) optimization during estimation process. This is, course, very complicated mathematically. this study, estimate MNR we developed PWLS method for proposed previous researcher, used reproducing kernel Hilbert space (RKHS) approach based on smoothing spline obtain solution optimization. Additionally, determined optimal parameter, investigated consistency estimator. We provide an illustration effects parameters results simulation data. future, theory generated from study can be within scope inference, purpose testing hypotheses involving models semiparametric models, component Indonesian toddlers’ standard growth charts.