G. González Farías, J. López Fidalgo, J. U. Márquez Urbina*
We introduce a parsimonious and flexible subclass of the Closed Skew Normal (CSN) distribution. We have derived and proved several important properties for this subclass, showing that it is identifiable and closed under marginalization and conditioning, and that zero correlation implies independence. We discuss why these random fields serve as valid models, and we investigate least squares estimators within this framework. In addition, using the Fisher information matrix of a particular parametrization of the model, we show the optimal locations to improve model estimators under the normal distribution. Finally, to assess the potential impact of this optimization process in linear models, we present a comparative simulation study incorporating different levels of bias.
Palabras clave: Optimal experiment designs, Skew Distributions/Responses, Fisher Information Matrix, D-optimal, Spatial Statistics
Programado
Diseño de Experimentos IV
12 de junio de 2025 19:00
Sala 3. Maria Rúbies Garrofé