Characterization of Lindley Distribution Based on Truncated Moments of Order Statistics

2017 ◽  
Vol 6 (2) ◽  
pp. 355-360
Author(s):  
N. M. Kilany
Keyword(s):  
Order Statistics ◽  
Lindley Distribution ◽  
Moments Of Order Statistics

scholarly journals On the quotient moment of lower generalized order statistics and characterization

2015 ◽  
Vol 11 (1) ◽  
pp. 73-89
Author(s):  
Devendra Kumar
Keyword(s):  
Order Statistics ◽  
Recurrence Relation ◽  
Conditional Expectation ◽  
General Class ◽  
Recurrence Relations ◽  
Generalized Order Statistics ◽  
Lower Records ◽  
Moments Of Order Statistics ◽  
Generalized Order

Abstract In this paper we consider general class of distribution. Recurrence relations satisfied by the quotient moments and conditional quotient moments of lower generalized order statistics for a general class of distribution are derived. Further the results are deduced for quotient moments of order statistics and lower records and characterization of this distribution by considering the recurrence relation of conditional expectation for general class of distribution satisfied by the quotient moment of the lower generalized order statistics.

Characterization of generalized Gamma-Lindley distribution using truncated moments of order statistics

2021 ◽  
Vol 71 (2) ◽  
pp. 455-474
Author(s):  
Dorsaf Laribi ◽  
Afif Masmoudi ◽  
Imen Boutouria
Keyword(s):  
Order Statistics ◽  
Survival Function ◽  
Lindley Distribution ◽  
Underlying Distribution ◽  
First Order ◽  
New Class ◽  
Generalized Gamma ◽  
Special Cases ◽  
Two Parameters

Abstract Having only two parameters, the Gamma-Lindley distribution does not provide enough flexibility for analyzing different types of lifetime data. From this perspective, in order to further enhance its flexibility, we set forward in this paper a new class of distributions named Generalized Gamma-Lindley distribution with four parameters. Its construction is based on certain mixtures of Gamma and Lindley distributions. The truncated moment, as a characterization method, has drawn a little attention in the statistical literature over the great popularity of the classical methods. We attempt to prove that the Generalized Gamma-Lindley distribution is characterized by its truncated moment of the first order statistics. This method rests upon finding a survival function of a distribution, that is a solution of a first order differential equation. This characterization includes as special cases: Gamma, Lindley, Exponential, Gamma-Lindley and Weighted Lindley distributions. Finally, a simulation study is performed to help the reader check whether the available data follow the underlying distribution.

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