| dc.description.abstract | Spline truncated regression is one of the nonparametric approach model that has
been modified from segmented polynomial .The estimator form of spline is being
strongly influenced by λ as the value of smoothing parameter which is essentially
determining the location of knots. This research aims to examines the usage of the
least squares method with a matrix approach in order to determines estimator the
spline linear regression two knots well as to recognize the best method as the
criteria for the optimal knots, specifically MSE and GCV. As the result of this
research, it shows if the regression estimator can be solved with the least squares
method through a matrix approach. The usage of the least squares method assume
the form of spline functions and provide the easier interpretation way through
statistical models. Meanwhile from the data output voltage cencorship polymer, it
discovered that the selection of the best spline regression model using MSE (λ) is
equal to 0.760617 and GCV (λ) is equal to 1.188464. The minimum result of both
methods constantly showed the same knot point location in every trial error. It is
indicated if the result shows that both methods have the same effectiveness in
determining the optimal location of the point knots. However, refering to the
value result, the value of MSE (λ) is the minimum value and it could be
recognized as the best method since it is quite efficient and easier to be used in
spline linear regression. | en_US |
