Gaussian and Lerch Models for Unimodal Time Series Forcasting

Autor: Azzouz Dermoune, Daoud Ounaissi, Yousri Slaoui

Publikováno v: Entropy, Vol 25, Iss 10, p 1474 (2023)

We consider unimodal time series forecasting. We propose Gaussian and Lerch models for this forecasting problem. The Gaussian model depends on three parameters and the Lerch model depends on four parameters. We estimate the unknown parameters by mini

Externí odkaz: https://doaj.org/article/301a4f9502c54c59a059329336a186a6

Multilevel Monte Carlo simulation of a diffusion with non-smooth drift

Autor: Rahmania, Azzouz Dermoune Daoud Ounaissi Nadji

We show that Lasso and Bayesian Lasso are very close when the sparsity is large and the noise is small. Then we propose to solve Bayesian Lasso using multivalued stochastic differential equation. We obtain three discretizations algorithms, and propos

Externí odkaz: http://arxiv.org/abs/1504.06441

Parametrizations, weights, and optimal prediction

Autor: Mohammed Es.Sebaiy, Khalifa Es-Sebaiy, Azzouz Dermoune, Jabrane Moustaaid

Publikováno v: Communications in Statistics - Theory and Methods. 50:815-836

The goal of the present paper is to predict the future value yn+1 based on previously observed time series y0, …, yn which are correlated with the constant trend, i.e. ∑i=0nyi≠0. We show that the c...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::92bfdda009ad17f314b90c7531e37bc4
https://doi.org/10.1080/03610926.2019.1642489

Analysis of the maximum likelihood channel estimator for OFDM systems in the presence of unknown interference

Autor: Eric Pierre Simon, Azzouz Dermoune

Publikováno v: EURASIP Journal on Advances in Signal Processing, Vol 2017, Iss 1, Pp 1-11 (2017)

This paper is a theoretical analysis of the maximum likelihood (ML) channel estimator for orthogonal frequency-division multiplexing (OFDM) systems in the presence of unknown interference. The following theoretical results are presented. Firstly, the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c58dbb177a2f7ddb2abc24cdbbaf9fe5
https://doi.org/10.1186/s13634-017-0504-x

Parametrizations, fixed and random effects

Autor: Cristian Preda, Azzouz Dermoune

Publikováno v: Journal of Multivariate Analysis
Journal of Multivariate Analysis, Elsevier, 2016, 154, pp.162-176. ⟨10.1016/j.jmva.2016.11.001⟩
Journal of Multivariate Analysis, 2016, 154, pp.162-176. ⟨10.1016/j.jmva.2016.11.001⟩
Journal of Multivariate Analysis, Elsevier, 2017, 154, pp.162-176. ⟨10.1016/j.jmva.2016.11.001⟩

We consider the problem of estimating the random element s of a finite-dimensional vector space S from the continuous data corrupted by noise with unknown variance ź w 2 . It is assumed that the mean E ( s ) (the fixed effect) of s belongs to a know

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ede75ba775750d1fc508c2cbcdfdab6
https://hal.archives-ouvertes.fr/hal-01424782