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We present a new set of distributions for positive data based on a skewnormalalpha-power PSN model including a new parameter which in turnmakes the log-skew-normal alpha-power LPSN model more flexible thanboth the log-normal LN model and log-skew-normal LSN model. TheLPSN model contains the LN model and LSN model as special cases. Furthermore,it models positive data with asymmetry and kurtosis larger thanthe one permitted by the LN distribution. Precipitation data illustrates theusefulness of the LPSN model being less influenced by outliers.

Tipo de documento: Artículo - Article

Palabras clave: symmetry, Fisher information matrix, Kurtosis, Likelihood ratio test, Maximum likelihood estimator.





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Revista Colombiana de Estadística Junio 2013, volumen 36, no.
1, pp.
43 a 57 The Family of Log-Skew-Normal Alpha-Power Distributions using Precipitation Data La familia de distribuciones alfa-potencia log-skew-normal usando datos de precipitación Guillermo Martínez-Flórez1,a , Sandra Vergara-Cardozo2,b , Luz Mery González2,c 1 Departamento de Matemáticas y Estadística, Universidad de Córdoba, Montería, Colombia 2 Departamento de Estadística, Facultad de Ciencias, Univesidad Nacional de Colombia, Bogotá D.C, Colombia Abstract We present a new set of distributions for positive data based on a skewnormal alpha-power (PSN) model including a new parameter which in turn makes the log-skew-normal alpha-power (LPSN) model more flexible than both the log-normal (LN) model and log-skew-normal (LSN) model.
The LPSN model contains the LN model and LSN model as special cases.
Furthermore, it models positive data with asymmetry and kurtosis larger than the one permitted by the LN distribution.
Precipitation data illustrates the usefulness of the LPSN model being less influenced by outliers. Key words: Asymmetry, Fisher information matrix, Kurtosis, Likelihood ratio test, Maximum likelihood estimator. Resumen Presentamos una nueva familia de distribuciones para datos positivos basada en el modelo skew-normal alpha-power (PSN), incluyendo un nuevo parámetro el cual hace el modelo log-skew-normal alpha-power (LPSN) más flexible que los modelos log-normal (LN) y log-skew-normal (LSN).
El modelo LPSN contiene el modelo LN y el modelo LSN como casos particulares.
Además, modela datos positivos con asimetría y curtosis más allá de lo permitido por la distribución LN.
Datos de precipitación ilustran la utilidad del modelo LPSN siendo menos influenciado por outliers. Palabras clave: asimetría, curtosis, estimador máxima verosimilitud, matriz de información de Fisher, test de razón de verosimilitud. a Professor. E-mail: gmartinez@correo.unicordoba.edu.co profes...






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