Nonparametric estimating equations for circular probability density functions and their derivatives

Estimating and Interpreting Probability Density Functions

This paper examines two approaches to estimating implied risk-neutral probability density functions from the prices of European-style options. It sets up a monte carlo test to evaluate alternative techniques’ ability to recover simulated distributions based on Heston’s (1993) stochastic volatility model. The paper tests both for the accuracy and stability of the estimated summary statistics fro...

Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions

Estimation of probability density functions (pdf) is one major topic in pattern recognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effec...

A methodology for estimating joint probability density functions

We develop a theoretical methodology for estimating joint probability density functions from marginals by, transforming the set of random variables into another set of independent random variables is necessary. To test this theory in practice, we developed a software application which implemented a reduced form of this methodology, and worked well with several datasets. Finally, we discuss how ...

Estimating Functions : Nonparametric and Robusthess Perspectives

Estimating Rate Equations Using Nonparametric Regression Methods