Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
A functional analysis of NOx levels: location and scale estimation and outlier detection
Computational Statistics
On the use of the bootstrap for estimating functions with functional data
Computational Statistics & Data Analysis
Modelling the mean of a doubly stochastic Poisson process by functional data analysis
Computational Statistics & Data Analysis
Hi-index | 0.00 |
In this paper we intend to illustrate how Functional Data Analysis (FDA) can be very useful for simulation input modelling. In particular, we are interested in the estimation of the cumulative mean function of a non-homogeneous Poisson Process (NHPP). Both parametric and nonparametric methods have been developed to estimate it from observed independent streams of arrival times. As far as we know, these data have not been analyzed as functional data. The basic idea underlying of FDA is treating a functional observation as a single datum rather than as a large set of data on its own. A considerable effort is being made in order to adapt some standard statistical methods for functional data, for instance Principal Components Analysis, ANOVA, classification techniques, bootstrap confidence bands, or outlier detection. We have studied a set of real data making use of these techniques and obtaining very good results.