Morgenstern type bivariate Lindley Distribution

V S Vaidyanathan; Sharon Varghese, A

doi:10.19139/soic.v4i2.183

Morgenstern type bivariate Lindley Distribution

Statistics Optimization & Information Computing ◽

10.19139/soic.v4i2.183 ◽

2016 ◽

Vol 4 (2) ◽

Cited By ~ 1

Author(s):

V S Vaidyanathan ◽

Sharon Varghese, A

Keyword(s):

Lindley Distribution

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A sustainable generalization of inverse Lindley distribution for wind speed analysis in certain regions of Pakistan

Modeling Earth Systems and Environment ◽

10.1007/s40808-021-01114-7 ◽

2021 ◽

Author(s):

Muhammad Shoaib ◽

Irum Sajjad Dar ◽

Muhammad Ahsan-ul-Haq ◽

Rana Muhammad Usman

Keyword(s):

Wind Speed ◽

Lindley Distribution ◽

Inverse Lindley Distribution

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New closed-form estimators for weighted Lindley distribution

Journal of the Korean Statistical Society ◽

10.1007/s42952-020-00097-y ◽

2021 ◽

Author(s):

Hyoung-Moon Kim ◽

Yu-Hyeong Jang

Keyword(s):

Closed Form ◽

Lindley Distribution

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New class of Lindley distributions: properties and applications

Journal of Statistical Distributions and Applications ◽

10.1186/s40488-021-00127-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Duha Hamed ◽

Ahmad Alzaghal

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Simulation Study ◽

Likelihood Estimation ◽

Statistical Properties ◽

Data Sets ◽

Lifetime Data ◽

Unknown Parameters ◽

Lindley Distribution ◽

New Class

AbstractA new generalized class of Lindley distribution is introduced in this paper. This new class is called the T-Lindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, log-logistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley distributions are investigated in more details. For estimating the unknown parameters, the maximum likelihood estimation has been used and a simulation study was carried out. Lastly, the usefulness of this new proposed class in fitting lifetime data is illustrated using four different data sets. In the application section, the strength of members of the T-Lindley{Y} class in modeling both unimodal as well as bimodal data sets is presented. A member of the T-Lindley{Y} class of distributions outperformed other known distributions in modeling unimodal and bimodal lifetime data sets.

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