A new method for nonparametric density estimation

A new method for computing the probability distribution of a given sample o f data is proposed. The observations are mapped into the finite interval [- 1, 1] and a shape-preserving spline is used to calculate the derivative of the cumulative distribution function. Although based on a spline, the proce dure guarantees non-negative density estimates. The method is compared to a normal kernel with plug-in bandwidth for a range of test distributions. As well as requiring less computational effort, the performance of the spline estimate of density is marginally superior to that of the kernel for distr ibutions that have an infinite domain, but is currently inferior to second generation kernels for semi-infinite domains.