Carlos Rivero Rodríguez, Carmen Anido Hermida, Teófilo Valdés Sánchez
We present a family of iterative estimation procedures based on imputation techniques and valid to fit linear models when, on the one hand, the distribution of errors is arbitrary and, on the other, the dependent data stem from different sources and, consequently, they may be either non grouped or grouped with different classification criteria.
© 2001-2024 Fundación Dialnet · Todos los derechos reservados