8 citations · 9 across the 6 of their papers we have counts for
7 papers
Geographically Weighted Regression for Air Quality Low-Cost Sensor Calibration
Jean-Michel Poggi, Bruno Portier, Emma Thulliez
This article focuses on the use of Geographically Weighted Regression (GWR) method to correct air quality low-cost sensors measurements. Those sensors are of major interest in the…
An efficient Averaged Stochastic Gauss-Newton algorithm for estimating parameters of non linear regressions models
Peggy Cénac, Antoine Godichon-Baggioni, Bruno Portier
Non linear regression models are a standard tool for modeling real phenomena, with several applications in machine learning, ecology, econometry... Estimating the parameters of the…
An efficient stochastic Newton algorithm for parameter estimation in logistic regressions
Bernard Bercu, Antoine Godichon-Baggioni, Bruno Portier
Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a binary random variable. It has a wide range of applications incl…
An averaged projected Robbins-Monro algorithm for estimating the parameters of a truncated spherical distribution
Antoine Godichon-Baggioni, Bruno Portier
The objective of this work is to propose a new algorithm to fit a sphere on a noisy 3D point cloud distributed around a complete or a truncated sphere. More precisely, we introduce…
A Durbin-Watson serial correlation test for ARX processes via excited adaptive tracking
Bernard Bercu, Bruno Portier, Victor Vazquez
We propose a new statistical test for the residual autocorrelation in ARX adaptive tracking. The introduction of a persistent excitation in the adaptive tracking control allows us…
On the asymptotic behavior of the Durbin-Watson statistic for ARX processes in adaptive tracking
Bernard Bercu, Bruno Portier, Victor Vazquez
A wide literature is available on the asymptotic behavior of the Durbin-Watson statistic for autoregressive models. However, it is impossible to find results on the Durbin-Watson s…
Strong uniform consistency and asymptotic normality of a kernel based error density estimator in functional autoregressive models
Nadine Hilgert, Bruno Portier
Estimating the innovation probability density is an important issue in any regression analysis. This paper focuses on functional autoregressive models. A residual-based kernel esti…