Regression splines (parametric Smoothing splines (nonparametric Implementing regression splines o The truncated power basis has two principal virtues: Conceptual simplicity The linear model is nested inside it,leading to simple tests of the null hypothesis of linearity Unfortunately,it has a number of computational/numerical flaws-it's inefficient and can lead to overflow and nearly singular matrix problems o The more complicated but numerically much more stable and efficient B-spline basis is often employed instead Patrick Breheny STA 621:Nonparametric StatisticsIntroduction Regression splines (parametric) Smoothing splines (nonparametric) Implementing regression splines The truncated power basis has two principal virtues: Conceptual simplicity The linear model is nested inside it, leading to simple tests of the null hypothesis of linearity Unfortunately, it has a number of computational/numerical flaws – it’s inefficient and can lead to overflow and nearly singular matrix problems The more complicated but numerically much more stable and efficient B-spline basis is often employed instead Patrick Breheny STA 621: Nonparametric Statistics