Strong uniform consistency rates of conditional quantiles for time series data in the single functional index model
Journal: NEW TRENDS IN MATHEMATICAL SCIENCES (Vol.3, No. 2)Publication Date: 2015-06-01
Authors : Amina Angelika Bouchentouf; Souad Mekkaoui; Abbes Rabhi;
Page : 181-198
Keywords : Conditional quantile conditional cumulative distribution derivatives of conditional cumulative distribution functional random variable kernel estimator nonparametric estimation semi-metric strong mixing processes.;
Abstract
The main objective of this paper is to estimate non-parametrically the quantiles of a conditional distribution when the sample is considered as an $alpha$-mixing sequence. First of all, a kernel type estimator for the conditional cumulative distribution function ({em cond-cdf}) is introduced. Afterwards, we give an estimation of the quantiles by inverting this estimated {em cond-cdf}, the asymptotic properties are stated when the observations are linked with a single-index structure. The pointwise almost complete convergence and the uniform almost complete convergence (with rate) of the kernel estimate of this model are established. This approach can be applied in time series analysis. For that, the whole observed time series has to be split into a set of functional data, and the functional conditional quantile approach can be employed both in foreseeing and building confidence prediction bands.
Other Latest Articles
- Mannheim Partner D-Curves in the Euclidean 3-space
- Some Characterizations of Constant Breadth Spacelike Curves in Minkowski 4-space
- Soft sets combined with interval valued intuitionistic fuzzy sets of type-2 and rough sets
- Heat transfer analysis of a fin with temperature-dependent thermal conductivity and heat transfer coefficient
- Frobenius-Euler and Frobenius-Genocchi Polynomials and their differential equations
Last modified: 2016-10-30 05:03:55