Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

We propose a method for reconstructing a probability density function (pdf) from a sample of an n-dimensional probability distribution. The method works by iteratively applying some simple ...

We discuss the application of orthogonal polynomials to the estimation of probability density functions, particularly with regard to accessing features of a portfolio's profit/loss distribution. Such ...

Continuity or discontinuity of probability density functions of data often plays a fundamental role in empirical economic analysis. For example, for identification and inference of causal effects in ...

The performance of a kernel density estimator depends crucially on the size of its smoothing bandwidth. A data-driven bandwidth selector for density estimation at a point is proposed in this paper.

A brief description of the methods used by the SYSLIN procedure follows. For more information on these methods, see the references at the end of this chapter. There are two fundamental methods of ...