This letter illustrates simple assumptions for proving consistency of the maximum likelihood estimator under multivariate Gaussian and Student's t linear cluster-weighted models, which allow density estimation, clustering and linear regression analysis with continuous random predictors in presence of unobserved heterogeneity.
Galimberti G, Soffritti G (2020). A note on the consistency of the maximum likelihood estimator under multivariate linear cluster-weighted models. STATISTICS & PROBABILITY LETTERS, 157, 1-5 [10.1016/j.spl.2019.108630].
A note on the consistency of the maximum likelihood estimator under multivariate linear cluster-weighted models
Galimberti G;Soffritti G
2020
Abstract
This letter illustrates simple assumptions for proving consistency of the maximum likelihood estimator under multivariate Gaussian and Student's t linear cluster-weighted models, which allow density estimation, clustering and linear regression analysis with continuous random predictors in presence of unobserved heterogeneity.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
11585_732057.pdf
Open Access dal 02/03/2022
Tipo:
Postprint
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione
1.05 MB
Formato
Adobe PDF
|
1.05 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
