Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Kolei, Salima El"'
Investigating technical skills of swimmers is a challenge for performance improvement, that can be achieved by analyzing multivariate functional data recorded by Inertial Measurement Units (IMU). To investigate technical levels of front-crawl swimmer
Externí odkaz:
http://arxiv.org/abs/2303.15812
We are interested in assessing the order of a finite-state Hidden Markov Model (HMM) with the only two assumptions that the transition matrix of the latent Markov chain has full rank and that the density functions of the emission distributions are li
Externí odkaz:
http://arxiv.org/abs/2210.03559
Publikováno v:
Journal of Computational and Applied Mathematics, Volume 380, 2020
In this paper, we consider an unknown functional estimation problem in a general nonparametric regression model with the feature of having both multiplicative and additive noise.We propose two new wavelet estimators in this general context. We prove
Externí odkaz:
http://arxiv.org/abs/1906.07695
We study the estimation, in Lp-norm, of density functions defined on [0,1]^d. We construct a new family of kernel density estimators that do not suffer from the so-called boundary bias problem and we propose a data-driven procedure based on the Golde
Externí odkaz:
http://arxiv.org/abs/1810.11107
Autor:
Kolei, Salima El, Patras, Frédéric
Misspecifications (i.e. errors on the parameters) of state space models lead to incorrect inference of the hidden states. This paper studies weakly nonlin-ear state space models with additive Gaussian noises and proposes a method for detecting and co
Externí odkaz:
http://arxiv.org/abs/1704.00587
Autor:
Kolei, Salima El, Pelgrin, Florian
We study a new parametric approach for hidden discrete-time diffusion models. This method is based on contrast minimization and deconvolution and leads to estimate a large class of stochastic models with nonlinear drift and nonlinear diffusion. It ca
Externí odkaz:
http://arxiv.org/abs/1512.08193
Autor:
Kolei, Salima El
For linear and Gaussian state space models parametrized by $\theta_0 \in \Theta \subset \mathbb{R}^r, r \geq 1$ corresponding to the vector of parameters of the model, the Kalman filter gives exactly the solution for the optimal filtering under weak
Externí odkaz:
http://arxiv.org/abs/1303.3518
Autor:
Kolei, Salima El
Publikováno v:
Metrika, journal 184 article 430, 2013
We study a new parametric approach for particular hidden stochastic models such as the Stochastic Volatility model. This method is based on contrast minimization and deconvolution. After proving consistency and asymptotic normality of the estimation
Externí odkaz:
http://arxiv.org/abs/1202.2559
Autor:
Kolei, Salima El, Patras, Frédéric
Publikováno v:
In Journal of Computational and Applied Mathematics 1 May 2018 333:200-214
