A probability density function (PDF) describes the likelihood of different outcomes for a continuous random variable.

Estimation of a Probability Density Function and Its Derivatives Let X 1, X 2, ⋯ be independent identically distributed random variables having a common probability density function f.

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 ...

Probability density function (PDF) Original research An iterative copula method for probability density estimation This paper puts forward a technique with which to reconstruct a probability density ...

In some situations only the statistical properties of such objects are desired: the three-dimensional probability density function. This article demonstrates that under special symmetries this ...

This paper develops a new scheme for improving an approximation method of a probability density function, which is inspired by the idea in the Hilbert space projection theorem. Moreover, we apply ...

Nonparametric method for multivariate density estimation using neural networks In this paper, a parameter-free method is proposed to determine the probability density function of multi-dimensional ...

In this paper, we propose a functional linear regression model in the space of probability density functions. We treat a cross-sectional distribution of individual earnings as an infinite dimensional ...

Building on the widely-used double-lognormal approach by Bahra (1997), this paper presents a multi-lognormal approach with restrictions to extract risk-neutral probability density functions (RNPs) for ...