Inspired by intuitive meanings of truncated power basis's
coefficients, the local penalization based on range's linear decreasing function is given
in penalized spline regression model. This method gives less penalization to fitting curve
where data is with more volatility, which makes fitted curve controls tradeoff between
goodness-of-fit and smoothness better. Simulations show that regression models with local
penalized spline obtain lower information rules' scores than global penalized spline when
the data is with heteroskedasticity.