A Two Parameter Discrete Lindley Distribution

• TASSADDAQ HUSSAIN ^[2] ; MUHAMMAD ASLAM ^[1] ; MUNIR AHMAD ^[3]
  1. [1] King Abdulaziz University

     King Abdulaziz University

     Arabia Saudí

     
  2. [2] Government Postgraduate College Department of Statistics
  3. [3] National College of Business Administration and Economics

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• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 39, Nº. 1, 2016, págs. 45-61
• Idioma: inglés
• DOI: 10.15446/rce.v39n1.55138
• Títulos paralelos:
  • Distribución Lindley de dos parámetros
• Enlaces
  • Texto completo
• Resumen
  • español

    En este artículo propusimos y discutimos la distribución Lindley de dos parámetros. La obtención de este Nuevo modelo está basada en una metodología en dos etapas: mezclar y luego discretizar, y puede ser vista como una generalización de una distribución geométrica. El modelo propuesto demostró tener la menor pérdida de información al ser aplicado a un cierto número de bases de datos (con estructuras de supra y sobredispersión). Los modelos estándar con los que se puede comparar son las distribuciones Poisson, Binomial Negativa, Poisson Generalizado y Gamma discrete.Su clasificación de tiempo de vida, kurtosis, sesgamiento, momentos factorials ascendientes y descendientes, al igual que sus relaciones de recurrencia, momentos negativos, estimación de parámetros via máxima verosimilitud, caracterización y discretización del caso bivariado son presentados.

  • English

    In this article we have proposed and discussed a two parameter discrete Lindley distribution. The derivation of this new model is based on a two step methodology i.e. mixing then discretizing, and can be viewed as a new generalization of geometric distribution. The proposed model has proved itself as the least loss of information model when applied to a number of data sets (in an over and under dispersed structure). The competing models such as Poisson, Negative binomial, Generalized Poisson and discrete gamma distributions are the well known standard discrete distributions. Its Lifetime classification, kurtosis, skewness, ascending and descending factorial moments as well as its recurrence relations, negative moments, parameters estimation via maximum likelihood method, characterization and discretized bi-variate case are presented.

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