scholarly journals Bivariate discrete Nadarajah and Haghighi distribution: Properties and different methods of estimation

Filomat ◽  
2019 ◽  
Vol 33 (17) ◽  
pp. 5589-5610
Author(s):  
Sajid Ali ◽  
Muhammad Shafqat ◽  
Ismail Shah ◽  
Sanku Dey
Keyword(s):  
Exponential Distribution ◽  
Count Data ◽  
Discrete Model ◽  
Discrete Data ◽  
Data Sets ◽  
Applied Statistics ◽  
Reliability Engineering ◽  
Unknown Parameters ◽  
Proposed Model ◽  
Better Than

The exponential distribution is commonly used to model different phenomena in statistics and reliability engineering. A new extension of exponential distribution known as the Nadarajah and Haghighi [An extension of the exponential distribution, Statistics: A Journal of Theoretical and Applied Statistics 45 (2011) 543-558.] distribution was introduced in the literature to accommodate the inflation of zero in the data. In practice, however, discrete data are easy to collect as compared to continuous data. Discrete bivariate distributions play important roles in modeling bivariate lifetime count data. Thus focusing on the utility of discrete data, this study presents a new bivariate discrete Nadarajah and Haghighi distribution. We discuss some basic properties of the proposed distribution and study seven different methods of estimation for the unknown parameters to assess the performance of the proposed bivariate discrete model. Two data sets are also analyzed to demonstrate how the proposed model may work in practice. Results show that the proposed model is very flexible and performs better than some of the existing models.

An alternative distribution to Lindley and Power Lindley distributions with characterizations, different estimation methods and data applications

2020 ◽  
Vol 70 (4) ◽  
pp. 953-978
Author(s):  
Mustafa Ç. Korkmaz ◽  
G. G. Hamedani
Keyword(s):  
Hazard Function ◽  
Mixture Distribution ◽  
Real Data ◽  
Quantile Function ◽  
Estimation Methods ◽  
Data Sets ◽  
Unknown Parameters ◽  
Lorenz Curves ◽  
Proposed Model ◽  
New Distribution

AbstractThis paper proposes a new extended Lindley distribution, which has a more flexible density and hazard rate shapes than the Lindley and Power Lindley distributions, based on the mixture distribution structure in order to model with new distribution characteristics real data phenomena. Its some distributional properties such as the shapes, moments, quantile function, Bonferonni and Lorenz curves, mean deviations and order statistics have been obtained. Characterizations based on two truncated moments, conditional expectation as well as in terms of the hazard function are presented. Different estimation procedures have been employed to estimate the unknown parameters and their performances are compared via Monte Carlo simulations. The flexibility and importance of the proposed model are illustrated by two real data sets.

scholarly journals On Erlang-Truncated Exponential Distribution: Theory and Application

2021 ◽  
pp. 155-168
Author(s):  
Ibrahim Elbatal ◽  
A. Aldukeel
Keyword(s):  
Exponential Distribution ◽  
Real Data ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Mean Deviation ◽  
Model Parameters ◽  
Data Sets ◽  
Survival Times ◽  
New Distribution ◽  
Better Than

In this article, we introduce a new distribution called the McDonald Erlangtruncated exponential distribution. Various structural properties including explicit expressions for the moments, moment generating function, mean deviation of the new distribution are derived. The estimation of the model parameters is performed by maximum likelihood method. The usefulness of the new distribution is illustrated by two real data sets. The new model is much better than other important competitive models in modeling relief times and survival times data sets.

scholarly journals An extensive extension of exponentiated exponential distribution using alpha power transformation – statistical properties and applications in engineering science

2021 ◽  
Vol 17 (2) ◽  
pp. 59-74
Author(s):  
S. Qurat Ul Ain ◽  
K. Ul Islam Rather
Keyword(s):  
Exponential Distribution ◽  
Real Life ◽  
Statistical Properties ◽  
Data Sets ◽  
Alpha Power ◽  
Real Life Data ◽  
Proposed Model ◽  
Mean Variance ◽  
New Distribution ◽  
Extra Parameter

Abstract In this article, an extension of exponentiated exponential distribution is familiarized by adding an extra parameter to the parent distribution using alpha power technique. The new distribution obtained is referred to as Alpha Power Exponentiated Exponential Distribution. Various statistical properties of the proposed distribution like mean, variance, central and non-central moments, reliability functions and entropies have been derived. Two real life data sets have been applied to check the flexibility of the proposed model. The new density model introduced provides the better fit when compared with other related statistical models.

scholarly journals On the Extended Generalized Inverse Exponential Distribution with Its Applications

2020 ◽  
pp. 14-27
Author(s):  
Sule Ibrahim ◽  
Bello Olalekan Akanji ◽  
Lawal Hammed Olanrewaju
Keyword(s):  
Exponential Distribution ◽  
Hazard Rate ◽  
Generalized Inverse ◽  
Real Data ◽  
Quantile Function ◽  
Exponential Model ◽  
Data Sets ◽  
Proposed Model ◽  
Method Of Maximum Likelihood ◽  
Inverse Exponential Distribution

We propose a new distribution called the extended generalized inverse exponential distribution with four positive parameters, which extends the generalized inverse exponential distribution. We derive some mathematical properties of the proposed model including explicit expressions for the quantile function, moments, generating function, survival, hazard rate, reversed hazard rate and odd functions. The method of maximum likelihood is used to estimate the parameters of the distribution. We illustrate its potentiality with applications to two real data sets which show that the extended generalized inverse exponential model provides a better fit than other models considered.

scholarly journals A discrete Ramos-Louzada distribution for asymmetric and over-dispersed data with leptokurtic-shaped: Properties and various estimation techniques with inference

2022 ◽  
Vol 7 (2) ◽  
pp. 1726-1741
Author(s):  
Ahmed Sedky Eldeeb ◽  
◽  
Muhammad Ahsan-ul-Haq ◽  
Mohamed. S. Eliwa ◽  
◽  
...  
Keyword(s):  
Count Data ◽  
Discrete Distribution ◽  
Real Data ◽  
Mass Function ◽  
Probability Mass Function ◽  
Data Sets ◽  
Estimation Techniques ◽  
Proposed Model ◽  
Probability Mass ◽  
Von Mises

<abstract> <p>In this paper, a flexible probability mass function is proposed for modeling count data, especially, asymmetric, and over-dispersed observations. Some of its distributional properties are investigated. It is found that all its statistical and reliability properties can be expressed in explicit forms which makes the proposed model useful in time series and regression analysis. Different estimation approaches including maximum likelihood, moments, least squares, Andersonӳ-Darling, Cramer von-Mises, and maximum product of spacing estimator, are derived to get the best estimator for the real data. The estimation performance of these estimation techniques is assessed via a comprehensive simulation study. The flexibility of the new discrete distribution is assessed using four distinctive real data sets ԣoronavirus-flood peaks-forest fire-Leukemia? Finally, the new probabilistic model can serve as an alternative distribution to other competitive distributions available in the literature for modeling count data.</p> </abstract>

An Advanced Discrete Model with Applications in Medical Science

2018 ◽  
Vol 09 (02) ◽  
pp. 1850001
Author(s):  
Bilal Ahmad Para ◽  
Tariq Rashid Jan
Keyword(s):  
Count Data ◽  
Discrete Model ◽  
Medical Science ◽  
Real Life ◽  
Medical Sciences ◽  
Proposed Model ◽  
Specific Parameter ◽  
Rate Functions ◽  
Two Parameter ◽  
New Distribution

In this paper, we introduce a new discrete model by compounding two parameter discrete Weibull distribution with Beta distribution of first kind. The proposed model can be nested to different compound distributions on specific parameter settings. The model is a good competitive for zero-inflated models. In addition, we present the basic properties of the new distribution and discuss unimodality, failure rate functions and index of dispersion. Finally, the model is examined with real-life count data from medical sciences to investigate the suitability of the proposed model.

Modeling Repeated Count Data: Some Extensions of the Rasch Poisson Counts Model

1995 ◽  
Vol 20 (3) ◽  
pp. 241-258 ◽  
Author(s):  
Marijtje A. J. van Duijn ◽  
Margo G. H. Jansen
Keyword(s):  
Count Data ◽  
Dirichlet Distribution ◽  
Random Variables ◽  
Data Sets ◽  
Unknown Parameters ◽  
Intensity Parameters ◽  
Test Parameters ◽  
Within Subjects ◽  
The Subject ◽  
Poisson Counts

We consider data that can be summarized as an N × K table of counts—for example, test data obtained by administering K tests to N subjects. The cell entries yij are assumed to be conditionally independent Poisson-distributed random variables, given the NK Poisson intensity parameters μij. The Rasch Poisson Counts Model (RPCM) postulates that the intensity parameters are products of test difficulty and subject ability parameters. We expand the RPCM by assuming that the subject parameters are random variables having a common gamma distribution with fixed unknown parameters and that the vectors of test difficulty parameters per subject follow a common Dirichlet distribution with fixed unknown parameters. Further, we show how additional structures can be imposed on the test parameters, modeling a within-subjects design. Methods for testing the fit and estimating the parameters of these models are presented and illustrated with the analysis of two empirical data sets.

scholarly journals On the Properties and Applications of a Transmuted Lindley-Exponential Distribution

2019 ◽  
pp. 1-13
Author(s):  
Adamu Abubakar Umar ◽  
Innocent Boyle Eraikhuemen ◽  
Peter Oluwaseun Koleoso ◽  
Jerry Joel ◽  
Terna Godfrey Ieren
Keyword(s):  
Exponential Distribution ◽  
Continuous Distribution ◽  
Real Life ◽  
Likelihood Estimation ◽  
Good Evidence ◽  
Survival Times ◽  
Probability Models ◽  
Unknown Parameters ◽  
Method Of Maximum Likelihood ◽  
Better Than

The Quadratic rank transmutation map proposed for introducing skewness and flexibility into probability models with a single parameter known as the transmuted parameter has been used by several authors and is proven to be useful. This article uses this method to add flexibility to the Lindley-Exponential distribution which results to a new continuous distribution called “transmuted Lindley-Exponential distribution”. This paper presents the definition, validation, properties, application and estimation of unknown parameters of the transmuted Lindley-Exponential distribution using the method of maximum likelihood estimation. The new distribution has been applied to a real life dataset on the survival times (in days) of 72 guinea pigs and the result gives good evidence that the transmuted Lindley-Exponential distribution is better than the Lindley-Exponential distribution, Exponential distribution and Lindley distribution based on the dataset used.

Zero-inflated Conway-Maxwell Poisson Distribution to Analyze Discrete Data

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Shin Zhu Sim ◽  
Ramesh C. Gupta ◽  
Seng Huat Ong
Keyword(s):  
Regression Model ◽  
Poisson Distribution ◽  
Likelihood Ratio ◽  
Regression Models ◽  
Discrete Data ◽  
Likelihood Ratio Tests ◽  
Simulation Studies ◽  
Generalized Poisson ◽  
Proposed Model ◽  
Better Than

Abstract In this paper, we study the zero-inflated Conway-Maxwell Poisson (ZICMP) distribution and develop a regression model. Score and likelihood ratio tests are also implemented for testing the inflation/deflation parameter. Simulation studies are carried out to examine the performance of these tests. A data example is presented to illustrate the concepts. In this example, the proposed model is compared to the well-known zero-inflated Poisson (ZIP) and the zero- inflated generalized Poisson (ZIGP) regression models. It is shown that the fit by ZICMP is comparable or better than these models.

scholarly journals Statistical inference for Kumaraswamy-exponential distribution based on progressive Type-II censored data with binomial removals

2020 ◽  
Vol 53 (2) ◽  
pp. 147-163
Author(s):  
RAKHI MOHAN ◽  
MANOJ CHACKO
Keyword(s):  
Exponential Distribution ◽  
Loss Function ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Type Ii ◽  
Bayes Estimators ◽  
Estimation Of Parameters ◽  
Unknown Parameters ◽  
Data Set ◽  
Better Than

In this paper, estimation of parameters of Kumaraswamy-exponential distribution with shape parameters α and β is considered based on a progressively type-II censored sample with binomial removals. Together with the unknown parameters, the removal probability p is also estimated. Bayes estimators are obtained using different loss functions such as squared error, LINEX loss function and entropy loss function. All Bayesian estimates are compared with the corresponding maximum likelihood estimates numerically in terms of their bias and mean square error values and found that Bayes estimators perform better than MLE’s for β and p and MLEs perform better than Bayes estimators for α. A real data set is also used for illustration.

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