Variance function estimation with LS-SVM for replicated data
Jooyong Shim, Hye-Jung Park, Kyung-Ha Seok · Journal of the Korean Data and Information Science Society · 2009
In this paper we propose a variance function estimation method for replicated data based on averages of squared residuals obtained from estimated mean function by the least squares support vector machine. Newton-Raphson method is used to obtain associated parameter vector for the variance function estimation. Furthermore, the cross validation functions are introduced to select the hyper-parameters which affect the performance of the proposed estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.