TY  - Type of reference 
TI  - Latent variable estimation by generalized Kalman recursions 
AU  - Joseph Ngatchou-Wandji 
AU  - Sadeq A.Kadhim 
AB  - This paper discusses state-space models with multi-categorical longitudinal observations and states characterized by the so-called Conditional Heteroskedastic AutoRegressive Nonlinear (CHARN) models. The latter are estimated via generalized Kalman recursions based on particle filters and EM algorithm. Our findings generalize the literature. They are illustrated by numerical simulations and applied to data from patients surged for breast cancer. 
DO  - 10.21494/ISTE.OP.2022.0864 
JF  - Biostatistics and Health Sciences 
KW  - Generalized Kalman recursions,  Generalized state space models,  Multicategorical longitudinal data,  Latent variables,  Particle filters,  EM algorthim, Récursivités de Kalman généralisées,  Modèles à espace d’état non-linéaires,  Données multicatégorielles longitudinales,  Variables latentes,  Filtre particulaires,  Algorithme EM,  
L1  - https://www.openscience.fr/IMG/pdf/iste_biostat22v3n1_1.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2022/09/5 
SN  - 2632-8291 
TT  - Estimation des variables latentes par des récursivités de Kalman généralisées 
UR  - https://www.openscience.fr/Latent-variable-estimation-by-generalized-Kalman-recursions 
IS  - Issue 1 
VL  - 3 
ER  -