@ARTICLE{10.21494/ISTE.OP.2022.0864, 

TITLE={Latent variable estimation by generalized Kalman recursions}, 
  
AUTHOR={Joseph Ngatchou-Wandji, Sadeq A.Kadhim, }, 
	
JOURNAL={Biostatistics and Health Sciences}, 
	
VOLUME={3}, 

NUMBER={Issue 1}, 
	
YEAR={2022}, 

URL={https://www.openscience.fr/Latent-variable-estimation-by-generalized-Kalman-recursions}, 
	
DOI={10.21494/ISTE.OP.2022.0864}, 
	
ISSN={2632-8291}, 
	
ABSTRACT={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.}}