Parameter Estimation for a Generalized Gamma Distribution

Stacy, E. W.; Mihram, G. Arthur

doi:10.1080/00401706.1965.10490268

Cited by 195 publications

(52 citation statements)

References 2 publications

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“…Finally, we also employ a third model only for heteroscedastic and heavy tailed data where the true underlying model of G is used to illustrate the best case scenario; we refer to this model as GGM-het2. Several methods exist for the estimation of the parameters of this distribution (Hager and Bain, 1970;Lawless, 1980;Wingo, 1987;Cohen and Whitten, 1986;Stacy and Mihram, 1965;Balakrashnan and Chan, 1994). We employ full-information maximum likelihood method to estimate the parameters of the model.…”

Section: Estimatorsmentioning

confidence: 98%

Generalized modeling approaches to risk adjustment of skewed outcomes data

Manning

Basu

Mullahy

2005

Journal of Health Economics

633

270

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There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., ordinary least square (OLS) on ln(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we encompass these two classes of models using the three parameter generalized Gamma (GGM) distribution, which includes several of the standard alternatives as special cases-OLS with a normal error, OLS for the log-normal, the standard Gamma and exponential with a log link, and the Weibull. Using simulation methods, we find the tests of identifying distributions to be robust. The GGM also provides a potentially more robust alternative estimator to the standard alternatives. An example using inpatient expenditures is also analyzed.

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Section: Estimatorsmentioning

confidence: 98%

Generalized modeling approaches to risk adjustment of skewed outcomes data

Manning

Basu

Mullahy

2005

Journal of Health Economics

633

270

View full text Add to dashboard Cite

show abstract

“…The most general form of gamma distribution is the modified (also known as generalized) gamma distribution (CG) (Grant et al, 2011). It was introduced by Stacy and Mihran (1965). The detailed Gamma distribution can be referred from Vasile (2010).…”

Section: Gamma Distributionmentioning

confidence: 99%

Derivation of rice crop calendar and evaluation of crop phenometrics and latitudinal relationship for major south and south-east Asian countries: A remote sensing approach

Manjunath

Jain

et al. 2016

Computers and Electronics in Agriculture

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“…The shape adjustment parameter, s, accounts for the tail behavior away from the origin (the upper tail), the Nakagami parameter, m, accounts for the tail behavior close to the origin (the lower tail) and the noise power, Ω, determines the spread of the density function [18]. The distribution parameters (m and s) can be estimated from the positive subband data using the maximum likelihood estimation technique as [31] …”

Section: Probability Density Function For the Speckle Wavelet Coefficmentioning

confidence: 99%