# A weighted version of Gamma distribution

Kanchan Jain; Neetu Singla; Rameshwar D. Gupta

Discussiones Mathematicae Probability and Statistics (2014)

- Volume: 34, Issue: 1-2, page 89-111
- ISSN: 1509-9423

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topKanchan Jain, Neetu Singla, and Rameshwar D. Gupta. "A weighted version of Gamma distribution." Discussiones Mathematicae Probability and Statistics 34.1-2 (2014): 89-111. <http://eudml.org/doc/270808>.

@article{KanchanJain2014,

abstract = {Weighted Gamma (WG), a weighted version of Gamma distribution, is introduced. The hazard function is increasing or upside-down bathtub depending upon the values of the parameters. This distribution can be obtained as a hidden upper truncation model. The expressions for the moment generating function and the moments are given. The non-linear equations for finding maximum likelihood estimators (MLEs) of parameters are provided and MLEs have been computed through simulations and also for a real data set. It is observed that WG fits better than its submodels (WE), Generalized Exponential (GE), Weibull and Exponential distributions.},

author = {Kanchan Jain, Neetu Singla, Rameshwar D. Gupta},

journal = {Discussiones Mathematicae Probability and Statistics},

keywords = {gamma distribution; weight function; hazard function; maximum likelihood estimator; Akaike Information criterion; Akaike information criterion},

language = {eng},

number = {1-2},

pages = {89-111},

title = {A weighted version of Gamma distribution},

url = {http://eudml.org/doc/270808},

volume = {34},

year = {2014},

}

TY - JOUR

AU - Kanchan Jain

AU - Neetu Singla

AU - Rameshwar D. Gupta

TI - A weighted version of Gamma distribution

JO - Discussiones Mathematicae Probability and Statistics

PY - 2014

VL - 34

IS - 1-2

SP - 89

EP - 111

AB - Weighted Gamma (WG), a weighted version of Gamma distribution, is introduced. The hazard function is increasing or upside-down bathtub depending upon the values of the parameters. This distribution can be obtained as a hidden upper truncation model. The expressions for the moment generating function and the moments are given. The non-linear equations for finding maximum likelihood estimators (MLEs) of parameters are provided and MLEs have been computed through simulations and also for a real data set. It is observed that WG fits better than its submodels (WE), Generalized Exponential (GE), Weibull and Exponential distributions.

LA - eng

KW - gamma distribution; weight function; hazard function; maximum likelihood estimator; Akaike Information criterion; Akaike information criterion

UR - http://eudml.org/doc/270808

ER -

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