Direct and flexible marginal inference for semicontinuous data

2015 ◽  
Vol 26 (6) ◽  
pp. 2962-2965 ◽  
Author(s):  
Valerie A Smith ◽  
John S Preisser
Keyword(s):  
Gamma Distribution ◽  
Continuous Data ◽  
Direct Inference ◽  
Generalized Gamma Distribution ◽  
Normal Distributions ◽  
Inverse Gamma ◽  
Special Cases ◽  
Semicontinuous Data ◽  
Log Normal ◽  
Marginal Inference

The marginalized two-part (MTP) model for semicontinuous data proposed by Smith et al. provides direct inference for the effect of covariates on the marginal mean of positively continuous data with zeros. This brief note addresses mischaracterizations of the MTP model by Gebregziabher et al. Additionally, the MTP model is extended to incorporate the three-parameter generalized gamma distribution, which takes many well-known distributions as special cases, including the Weibull, gamma, inverse gamma, and log-normal distributions.

THEORETICAL PROPERTIES OF THE WEIGHTED GENERALIZED GAMMA AND RELATED DISTRIBUTIONS

2015 ◽  
Vol 29 (3) ◽  
pp. 421-432 ◽  
Author(s):  
Hewa A. Priyadarshani ◽  
Broderick O. Oluyede
Keyword(s):  
Gamma Distribution ◽  
Hazard Function ◽  
Gamma Model ◽  
Generalized Gamma Distribution ◽  
New Class ◽  
Generalized Gamma ◽  
Special Cases ◽  
Entropy Measures ◽  
Skewness Coefficient ◽  
Coefficient Of Skewness

A new class of weighted generalized gamma distribution (WGGD) and related distributions are presented. Theoretical properties of the generalized gamma model, WGGD including the hazard function, reverse hazard function, moments, coefficient of variation, coefficient of skewness, coefficient of kurtosis, and entropy measures are derived. The results presented here generalizes the generalized gamma distribution and includes several distributions as special cases. The special cases include generalized gamma, weighted gamma, weighted exponential, weighted Weibull, weighted Rayleigh distributions, and their underlying or parent distributions.

scholarly journals ESTIMATIVA E DISTRIBUIÇÃO DE PRECIPITAÇÕES DECENDIAIS PARA O ESTADO DO PARANÁ

Irriga ◽  
2007 ◽  
Vol 12 (1) ◽  
pp. 38-53 ◽  
Author(s):  
Silvio César Sampaio ◽  
Manoel Moisés Ferreira de Queiroz ◽  
Elisandro Pires Frigo ◽  
Adair José Longo ◽  
Morgana Suszek
Keyword(s):  
Normal Distribution ◽  
Gamma Distribution ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Observation Data ◽  
Normal Distributions ◽  
Kolmogorov Smirnov ◽  
Log Normal ◽  
Log Normal Distribution ◽  
East West

ESTIMATIVA E DISTRIBUIÇÃO DE PRECIPITAÇÕES DECENDIAIS PARA O ESTADO DO PARANÁ  Silvio César Sampaio; Manoel Moisés Ferreira de Queiroz; Elisandro Pires Frigo; Adair José Longo; Morgana SuszekSetor de Recursos Hídricos e Saneamento Ambiental (RHESA), Universidade Estadual do Oeste do Paraná,  Cascavel, Paraná, [email protected]  1 RESUMO O objetivo deste trabalho foi estimar a precipitação provável com 75% de probabilidade nos períodos decendiais, a partir de dados diários de precipitação de 22 postos de medição, com um mínimo de 12 anos de observação, fazendo-se uso das distribuições Gama e Log-normal. Os testes Qui-quadrado e Kolmogorov-Smirnov, ambos com 5% de significância, foram utilizados para verificar a aderência das distribuições às condições pluviométricas decendiais do estado do Paraná. Os resultados mostraram que a distribuição Gama ajustou-se mais adequadamente às condições pluviométricas do estado nos períodos estudados. Os meses mais chuvosos são janeiro e fevereiro, enquanto os mais secos, são julho e agosto. O estado do Paraná apresenta aumento na quantidade de precipitação pluviométrica na direção litoral/oeste e norte/sul. UNITERMOS: probabilidades, chuva provável, distribuição Gama, distribuição log-normal.  SAMPAIO, S.C.; QUEIROZ, M.M.F. de; FRIGO, E.P.; LONGO, A.J.; SUSZEK, M.DISTRIBUTION AND ESTIMATE OF PROBABLE 10-DAY PRECIPITATION INPARANÁSTATE  2 ABSTRACT The objective of this study was to estimate the probable 10-day precipitation, 75% probability, using daily rainfall data from 22 rain gauge sites, which had been collecting observation data for at least 12 years; Gamma and Log-normal distributions were used. Qui-square and Kolmogorov-Smirnov tests, at 5% significance, were utilized to verify distribution adherence to 10-day rainfall conditions in the state of Paraná. The results showed that Gamma distribution was more adequately adjusted to rainfall conditions in the studied periods than Log-normal distributions. January and February are the rainiest months whereas July and August are the driest ones. There is an increase in the east/west and north/south bound rainfall inParanastate. KEYWORDS: probabilities, probable rainfall, gamma distribution, log-normal distribution.  

scholarly journals On Generalized Gamma Distribution and Its Application to Survival Data

2019 ◽  
Vol 8 (5) ◽  
pp. 85 ◽  
Author(s):  
Kiche J ◽  
Oscar Ngesa ◽  
George Orwa
Keyword(s):  
Survival Analysis ◽  
Gamma Distribution ◽  
Survival Data ◽  
Parametric Model ◽  
Parametric Modeling ◽  
Parametric Models ◽  
Generalized Gamma Distribution ◽  
Generalized Gamma ◽  
Special Cases ◽  
Two Parameter

The generalized gamma distribution is a continuous probability distribution with three parameters. It is a generalization of the two-parameter gamma distribution. Since many distributions commonly used for parametric models in survival analysis (such as the Exponential distribution , the Weibull distribution and the Gamma distribution) are special cases of the generalized gamma, it is sometimes used to determine which parametric model is appropriate for a given set of data. Generalized gamma distribution is one of the distributions used in frailty modeling. In this study , it is shown that generalized gamma distribution has three sub-families and its application to the analysis of a survival data has also been explored. The parametric modeling approach has been carried out to find the expected results.

scholarly journals A Note on the Generalized (Positive) Cauchy Distribution

1969 ◽  
Vol 12 (6) ◽  
pp. 865-868 ◽  
Author(s):  
B. Raja Rao ◽  
M. L. Garg
Keyword(s):  
Gamma Distribution ◽  
Cauchy Distribution ◽  
Weibull Distributions ◽  
Generalized Gamma Distribution ◽  
Normal Variable ◽  
Generalized Gamma ◽  
Special Cases ◽  
Chi Squared ◽  
Image Position

In this note, a certain generalization of the Cauchy distribution is obtained, using the result of Malik [2].The generalized gamma distribution having the density(1)is introduced by Stacy [1], who studied some of its properties. As remarked by Stacy [1], the familiar gamma, chi, chi-squared, exponential and Weibull distributions are special cases of (1), as are the distributions of certain functions of a normal variable - viz.

scholarly journals On Generalized Gamma Distribution and Its Application to Survival Data

2019 ◽  
Vol 8 (5) ◽  
pp. 65
Author(s):  
Kiche J ◽  
Oscar Ngesa ◽  
George Orwa
Keyword(s):  
Survival Analysis ◽  
Gamma Distribution ◽  
Survival Data ◽  
Parametric Model ◽  
Parametric Modeling ◽  
Parametric Models ◽  
Generalized Gamma Distribution ◽  
Generalized Gamma ◽  
Special Cases ◽  
Two Parameter

The generalized gamma distribution is a continuous probability distribution with three parameters. It is a generalization of the two-parameter gamma distribution. Since many distributions commonly used for parametric models in survival analysis (such as the Exponential distribution , the Weibull distribution and the Gamma distribution) are special cases of the generalized gamma, it is sometimes used to determine which parametric model is appropriate for a given set of data. Generalized gamma distribution is one of the distributions used in frailty modeling. In this study , it is shown that generalized gamma distribution has three sub-families and its application to the analysis of a survival data has also been explored. The parametric modeling approach has been carried out to find the expected results.

Log-normal distributions in parasitology

1984 ◽  
Vol 93 (6) ◽  
pp. 591-598 ◽  
Author(s):  
Sandeep K Malhotra
Keyword(s):  
Normal Distributions ◽  
Log Normal

scholarly journals Survival and Reliability Analysis with an Epsilon-Positive Family of Distributions with Applications

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 908
Author(s):  
Perla Celis ◽  
Rolando de la Cruz ◽  
Claudio Fuentes ◽  
Héctor W. Gómez
Keyword(s):  
Survival Data ◽  
Maximum Likelihood Estimates ◽  
New Class ◽  
New Family ◽  
Gamma Distributions ◽  
Special Cases ◽  
Log Normal ◽  
Positive Support ◽  
Positive Distributions ◽  
Family Of Distributions

We introduce a new class of distributions called the epsilon–positive family, which can be viewed as generalization of the distributions with positive support. The construction of the epsilon–positive family is motivated by the ideas behind the generation of skew distributions using symmetric kernels. This new class of distributions has as special cases the exponential, Weibull, log–normal, log–logistic and gamma distributions, and it provides an alternative for analyzing reliability and survival data. An interesting feature of the epsilon–positive family is that it can viewed as a finite scale mixture of positive distributions, facilitating the derivation and implementation of EM–type algorithms to obtain maximum likelihood estimates (MLE) with (un)censored data. We illustrate the flexibility of this family to analyze censored and uncensored data using two real examples. One of them was previously discussed in the literature; the second one consists of a new application to model recidivism data of a group of inmates released from the Chilean prisons during 2007. The results show that this new family of distributions has a better performance fitting the data than some common alternatives such as the exponential distribution.

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