A5005771983- Statistical Methods and Inference
- Control Systems and Identification
- Numerical methods in inverse problems
- Cardiovascular Disease and Adiposity
- Approximation Theory and Sequence Spaces
- Probabilistic and Robust Engineering Design
- Image and Signal Denoising Methods
- Fault Detection and Control Systems
- Data Mining Algorithms and Applications
- Air Quality Monitoring and Forecasting
- E-Learning and Knowledge Management
- Sparse and Compressive Sensing Techniques
- Matrix Theory and Algorithms
- Atmospheric chemistry and aerosols
- Statistical and numerical algorithms
- Air Quality and Health Impacts
- Educational Technology in Learning
- Spectroscopy and Chemometric Analyses
- Stability and Controllability of Differential Equations
- Electromagnetic Scattering and Analysis
- Genetic and phenotypic traits in livestock
- Financial Risk and Volatility Modeling
- Network Security and Intrusion Detection
- Statistical Methods and Bayesian Inference
- Advanced Statistical Methods and Models
Universidad Complutense de Madrid
2024
Universidad de Oviedo
2019-2020
Universidad de Granada
2015-2019
The Functional Linear Model with Response (FLMFR) is one of the most fundamental models to assess relation between two functional random variables. In this paper, we propose a novel goodness-of-fit test for FLMFR against general, unspecified, alternative. statistic formulated in terms Cram\'er-von Mises norm over doubly-projected empirical process which, using geometrical arguments, yields an easy-to-compute weighted quadratic norm. A resampling procedure calibrates through wild bootstrap on...
Functional Analysis of Variance (FANOVA) from Hilbert-valued correlated data with spatial rectangular or circular supports is analyzed, when Dirichlet conditions are assumed on the boundary. Specifically, a fixed effect model error term defined an Autoregressive Hilbertian process order one (ARH(1) process) considered, extending formulation given in Ruiz-Medina (2016). A new statistical test also derived to contrast significance functional parameters. The established at boundary affect...
J.E. Ruiz-Castro1, C.J. Acal-González1, A.M. Aguilera Del Pino1, F.J. Alonso-Morales1, J. Álvarez-Liébana2, B. Cobo-Rodríguez3, García-Montero4, R. Raya-Miranda1, A.R. Sánchez-Morales5 1University of Granada (SPAIN) 2University Oviedo 3Complutense University Madrid 4Consejería de Educación. Junta Andalucía 5External Colaborator.
A special class of standard Gaussian Autoregressive Hilbertian processes order one (Gaussian ARH(1) processes), with bounded linear autocorrelation operator, which does not satisfy the usual Hilbert-Schmidt assumption, is considered. To compensate slow decay diagonal coefficients a faster velocity eigenvalues trace autocovariance operator innovation process assumed. As usual, eigenvectors are considered for projection, since, here, they assumed to be known. Diagonal componentwise classical...
New results on strong-consistency, in the Hilbert-Schmidt and trace operator norms, are obtained, parameter estimation of an autoregressive Hilbertian process order one (ARH(1) process). In particular, a strongly-consistent diagonal componentwise estimator autocorrelation is derived, based its empirical singular value decomposition.
This work derives new results on strong consistent estimation and prediction for autoregressive processes of order 1 in a separable Banach space B. The consistency are obtained the componentwise estimator autocorrelation operator norm $\mathcal{L}(B)$ bounded linear operators associated plug-in predictor then follows $B$-norm. A Gelfand triple is defined through Hilbert constructed Kuelbs' Lemma \cite{Kuelbs70}. Hilbert--Schmidt embedding introduces Reproducing Kernel (RKHS), generated by...