ahmad flaih - Academia.edu (original) (raw)

Papers by ahmad flaih

International Journal of Agricultural and Statistical Sciences, Nov 30, 2023

Journal of Al-Qadisiyah for Computer Science and Mathematics

This paper presented a study of the analysis of the mediation of a multi-model and the knowledge ... more This paper presented a study of the analysis of the mediation of a multi-model and the knowledge of the role of these variables in the transfer of the effect of the independent variable to the dependent variable. In this article, the delta method was used to estimate the standard error and because this method violates a number of assumptions, especially with small sampling sizes, the method of shoeboxes was used to overcome such problems. According to the results obtained from the application for this model, it was found that the shoe bass method is better than the Delta method according to numerical results.

Periodicals of Engineering and Natural Sciences (PEN), Jun 14, 2020

Lasso estimate as the posterior mode assuming that the parameter  has prior density as double ex... more Lasso estimate as the posterior mode assuming that the parameter  has prior density as double exponential distribution [1]. In this paper, we proposed Scale Mixture of Normals mixing with Rayleigh (SMNR) density on their variances to represent the double exponential distribution. Hierarchical model formulation presented with Gibbs sampler under SMNR as alternative Bayesian analysis of minimization problem of classical lasso. We conducted two simulation examples to explore path solution of the Ridge, Lasso, Bayesian Lasso, and New Bayesian Lasso (R, L, BL, NBL) regression methods through the prediction accuracy using the bias of the estimates with different sample sizes, bias indicates that the lasso regression perform well, followed by the NBL. The Median Mean Absolute Deviations (MMAD) used to compared the perform of the regression methods using real data, MMAD indicates that the proposed method (NBL) perform better than the others.

Periodicals of Engineering and Natural Sciences (PEN), Jun 14, 2020

In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite... more In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite quantile regression model when the response variable is binary. For selecting variables in binary composite quantile regression lasso the adaptive lasso penalty is derived in a Bayesian framework. Simulation study and real data examples are used to examine the performance of the proposed methods compared to the other existing methods. We conclude that the proposed method is comparable.

Periodicals of Engineering and Natural Sciences (PEN), Jun 14, 2020

Since lasso method launched, a lot of applications and extensions were run on it which made it to... more Since lasso method launched, a lot of applications and extensions were run on it which made it to become deeply widely used in various discipline. In this paper, we proposed the Scale Mixture of Normals mixing with Rayleigh (SMNR) distribution on their variances to represent the double exponential distribution. Hierarchical model formula have derived with Gibbs sampler for SMNR. The proposed models; Bayesian Tobit Adaptive Lasso (BTAL) and Bayesian Tobit Lasso (BTL) models are illustrated using simulation example and a real data example through the prediction accuracy using the estimated relative efficiency with different sample. This is the first work that discussed regularization regression models under SMNR.

مجلة القادسية لعلوم الحاسبات والرياضيات, Aug 26, 2019

Counts data models cope with the response variable counts, where the number of times that a certa... more Counts data models cope with the response variable counts, where the number of times that a certain event occurs in a fixed point is called count data, its observations consists of non-negative integers values {0,1,2,…}. Because of the nature of count data, the response variables are usually considered doing not follow normal distribution. Therefore, linear regression is not an appropriate method to analysis count data due to the skewed distribution. Hence, using linear regression model to analysis count data is likely to bias the results, under these limitations, Poisson regression model and "Negative binomial regression" are likely the appropriate models to analysis count data. Sometimes researchers may Counts more zeros than the expected. Count data with many Zeros leads to a concept called "Zero-inflation". Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the "zero-altered Poisson" (ZAP) model and the "negative binomial" model. In this paper, the models, Poisson, Negative Binomial, ZAP, and ZANB were been used to analyze rainfall data.

مجلة القادسية لعلوم الحاسبات والرياضيات, Aug 26, 2019

Count data, including zero counts arise in a wide variety of application, hence models for counts... more Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the zero-inflated Poisson (ZIP) model and the negative binomial model. In this paper, the models, Poisson, Negative Binomial, ZIP, and ZINB were been used to analyze rainfall data.

Mağallaẗ al-qādisiyyaaẗ li-l-ʻulūm al-ṣirfaẗ, Jul 7, 2021

Bayesian regression analysis has great importance in recent years, especially in the Regularizati... more Bayesian regression analysis has great importance in recent years, especially in the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing the prior distribution of the interested parameter is the main idea in the Bayesian regression analysis. By penalizing the

PHYSICAL MESOMECHANICS OF CONDENSED MATTER: Physical Principles of Multiscale Structure Formation and the Mechanisms of Nonlinear Behavior: MESO2022

Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 )

Abbas [1] proposed new hierarchical representation of the adaptive Bayesian lasso model as unifor... more Abbas [1] proposed new hierarchical representation of the adaptive Bayesian lasso model as uniform density , mixing with standard exponential distribution based on a transformation of the mixture of uniform density and a particular gamma distribution formulation provided by Mallick & Yi [2] They consider the new proposed hierarchical formulation model and prior distributions, as well as the full Conditional posterior distributions structural under non conditioning on σ2 which makes the uncertainty, of a unimodal full posterior, Conditioning on σ2 is important, because it guarantees a unimodal full posterior Park and Casella [3]. So, we can conclude that [1] proposed new hierarchical representation utilizing a Non- scale mixture distributions, which needs to deal with this problem . To address this problem we consider new hierarchical representation of the adaptive Bayesian lasso for Tobit model based on scale mixture of Uniform density, mixing with standard exponential distribution.

Al-Qadisiyah Journal of Pure Science

Bayesian regression analysis has great importance in recent years, especially in the Regularizati... more Bayesian regression analysis has great importance in recent years, especially in the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing the prior distribution of the interested parameter is the main idea in the Bayesian regression analysis. By penalizing the

AL-Qadisiyah Journal For Administrative and Economic sciences, 2005

AL-Qadisiyah Journal For Administrative and Economic sciences, 2006

Enterprise and Organizational Modeling and Simulation, Jun 7, 2010

This paper discusses the problem of decision support systems in the organization. The procedure (... more This paper discusses the problem of decision support systems in the organization. The procedure (linear combination) developed with the aim to combine some predicted results obtained with simulation of linear and nonlinear regression models (experts), multiple regression model, nonparametric regression model, and semi parametric regression model. This adjustment procedure enforce some statistical characteristics like the expected value of the gross production rate based on Cobb-Douglas production function is unbiased for the actual value, and the total weights(importance) of all models(experts) is equal to one. We used modeling and simulation techniques to generate our data and to apply the procedure.

Periodicals of Engineering and Natural Sciences (PEN), 2020

Lasso estimate as the posterior mode assuming that the parameter has prior density as double expo... more Lasso estimate as the posterior mode assuming that the parameter has prior density as double exponential distribution [1]. In this paper, we proposed Scale Mixture of Normals mixing with Rayleigh (SMNR) density on their variances to represent the double exponential distribution. Hierarchical model formulation presented with Gibbs sampler under SMNR as alternative Bayesian analysis of minimization problem of classical lasso. We conducted two simulation examples to explore path solution of the Ridge, Lasso, Bayesian Lasso, and New Bayesian Lasso (R, L, BL, NBL) regression methods through the prediction accuracy using the bias of the estimates with different sample sizes, bias indicates that the lasso regression perform well, followed by the NBL. The Median Mean Absolute Deviations (MMAD) used to compared the perform of the regression methods using real data, MMAD indicates that the proposed method (NBL) perform better than the others.

Periodicals of Engineering and Natural Sciences (PEN), 2020

Since lasso method launched, a lot of applications and extensions were run on it which made it to... more Since lasso method launched, a lot of applications and extensions were run on it which made it to become deeply widely used in various discipline. In this paper, we proposed the Scale Mixture of Normals mixing with Rayleigh (SMNR) distribution on their variances to represent the double exponential distribution. Hierarchical model formula have derived with Gibbs sampler for SMNR. The proposed models; Bayesian Tobit Adaptive Lasso (BTAL) and Bayesian Tobit Lasso (BTL) models are illustrated using simulation example and a real data example through the prediction accuracy using the estimated relative efficiency with different sample. This is the first work that discussed regularization regression models under SMNR.

Periodicals of Engineering and Natural Sciences (PEN), 2020

In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite... more In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite quantile regression model when the response variable is binary. For selecting variables in binary composite quantile regression lasso the adaptive lasso penalty is derived in a Bayesian framework. Simulation study and real data examples are used to examine the performance of the proposed methods compared to the other existing methods. We conclude that the proposed method is comparable.

Journal of Statistics Applications & Probability Letters, 2019

We propose the use of the Epsilon Skew Exponential Power family of distribution as an alternative... more We propose the use of the Epsilon Skew Exponential Power family of distribution as an alternative model to make inference on estimating the interested parameters of the Tobit regression model. Tobit model assumes the normality of residuals term. Elsalloukh[5] proposed the Epsilon Skew Exponential Power (ESEP) family of distributions which include the ESN distribution and many other distributions as special cases. Moreover, ESEP family of densities can accommodate both heavy tails and skewness behaviors. We develop the basic properties of the ESEP Tobit model, such as the structural equation, the expected value of the censored variable, and the log-likelihood functions based on the piecewise nature of the ESEP density. We provide data analysis when the residuals are distributed according to the ESEP(θ,σ,α,ε) family.

Journal of Statistics Applications & Probability Letters, 2020

In this work, we consider a case study for the Bi-Epsilon Skew Exponential Power (BIESEP) ROC cur... more In this work, we consider a case study for the Bi-Epsilon Skew Exponential Power (BIESEP) ROC curve proposed by Flaih et al. [3]. This model is a generalization of the Epsilon Skew bi-normal ROC curve proposed by Mashtare Jr. and Huston [10]. Elsalloukh [1, 2] provided the Epsilon Skew Exponential Power (ESEP) which is less sensitive to outliers. The ESEP family can be adopted to cope with skewness and kurtosis of a data set. The ESEP model provides an appropriate choice to increase the robustness of data analysis. We develop the Epsilon Skew bi-normal ROC curve based on the outcomes of the diagnostic test that is distributed according to the ESEP distribution. More specifically, we derive the BIESEP ROC parameters and the Area Under the Curve (AUC). Also, we consider the parameter estimation of BIESEP ROC curve and the AUC of a diagnostic test. We employ the BIESEP ROC curve to analyze a real dataset.

Journal of Al-Qadisiyah for Computer Science and Mathematics, 2019

Count data, including zero counts arise in a wide variety of application, hence models for counts... more Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the zero-inflated Poisson (ZIP) model and the negative binomial model. In this paper, the models, Poisson, Negative Binomial, ZIP, and ZINB were been used to analyze rainfall data.

International Journal of Agricultural and Statistical Sciences, Nov 30, 2023

Journal of Al-Qadisiyah for Computer Science and Mathematics

This paper presented a study of the analysis of the mediation of a multi-model and the knowledge ... more This paper presented a study of the analysis of the mediation of a multi-model and the knowledge of the role of these variables in the transfer of the effect of the independent variable to the dependent variable. In this article, the delta method was used to estimate the standard error and because this method violates a number of assumptions, especially with small sampling sizes, the method of shoeboxes was used to overcome such problems. According to the results obtained from the application for this model, it was found that the shoe bass method is better than the Delta method according to numerical results.

Periodicals of Engineering and Natural Sciences (PEN), Jun 14, 2020

Lasso estimate as the posterior mode assuming that the parameter  has prior density as double ex... more Lasso estimate as the posterior mode assuming that the parameter  has prior density as double exponential distribution [1]. In this paper, we proposed Scale Mixture of Normals mixing with Rayleigh (SMNR) density on their variances to represent the double exponential distribution. Hierarchical model formulation presented with Gibbs sampler under SMNR as alternative Bayesian analysis of minimization problem of classical lasso. We conducted two simulation examples to explore path solution of the Ridge, Lasso, Bayesian Lasso, and New Bayesian Lasso (R, L, BL, NBL) regression methods through the prediction accuracy using the bias of the estimates with different sample sizes, bias indicates that the lasso regression perform well, followed by the NBL. The Median Mean Absolute Deviations (MMAD) used to compared the perform of the regression methods using real data, MMAD indicates that the proposed method (NBL) perform better than the others.

Periodicals of Engineering and Natural Sciences (PEN), Jun 14, 2020

In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite... more In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite quantile regression model when the response variable is binary. For selecting variables in binary composite quantile regression lasso the adaptive lasso penalty is derived in a Bayesian framework. Simulation study and real data examples are used to examine the performance of the proposed methods compared to the other existing methods. We conclude that the proposed method is comparable.

Periodicals of Engineering and Natural Sciences (PEN), Jun 14, 2020

Since lasso method launched, a lot of applications and extensions were run on it which made it to... more Since lasso method launched, a lot of applications and extensions were run on it which made it to become deeply widely used in various discipline. In this paper, we proposed the Scale Mixture of Normals mixing with Rayleigh (SMNR) distribution on their variances to represent the double exponential distribution. Hierarchical model formula have derived with Gibbs sampler for SMNR. The proposed models; Bayesian Tobit Adaptive Lasso (BTAL) and Bayesian Tobit Lasso (BTL) models are illustrated using simulation example and a real data example through the prediction accuracy using the estimated relative efficiency with different sample. This is the first work that discussed regularization regression models under SMNR.

مجلة القادسية لعلوم الحاسبات والرياضيات, Aug 26, 2019

Counts data models cope with the response variable counts, where the number of times that a certa... more Counts data models cope with the response variable counts, where the number of times that a certain event occurs in a fixed point is called count data, its observations consists of non-negative integers values {0,1,2,…}. Because of the nature of count data, the response variables are usually considered doing not follow normal distribution. Therefore, linear regression is not an appropriate method to analysis count data due to the skewed distribution. Hence, using linear regression model to analysis count data is likely to bias the results, under these limitations, Poisson regression model and "Negative binomial regression" are likely the appropriate models to analysis count data. Sometimes researchers may Counts more zeros than the expected. Count data with many Zeros leads to a concept called "Zero-inflation". Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the "zero-altered Poisson" (ZAP) model and the "negative binomial" model. In this paper, the models, Poisson, Negative Binomial, ZAP, and ZANB were been used to analyze rainfall data.

مجلة القادسية لعلوم الحاسبات والرياضيات, Aug 26, 2019

Count data, including zero counts arise in a wide variety of application, hence models for counts... more Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the zero-inflated Poisson (ZIP) model and the negative binomial model. In this paper, the models, Poisson, Negative Binomial, ZIP, and ZINB were been used to analyze rainfall data.

Mağallaẗ al-qādisiyyaaẗ li-l-ʻulūm al-ṣirfaẗ, Jul 7, 2021

Bayesian regression analysis has great importance in recent years, especially in the Regularizati... more Bayesian regression analysis has great importance in recent years, especially in the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing the prior distribution of the interested parameter is the main idea in the Bayesian regression analysis. By penalizing the

PHYSICAL MESOMECHANICS OF CONDENSED MATTER: Physical Principles of Multiscale Structure Formation and the Mechanisms of Nonlinear Behavior: MESO2022

Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 )

Abbas [1] proposed new hierarchical representation of the adaptive Bayesian lasso model as unifor... more Abbas [1] proposed new hierarchical representation of the adaptive Bayesian lasso model as uniform density , mixing with standard exponential distribution based on a transformation of the mixture of uniform density and a particular gamma distribution formulation provided by Mallick & Yi [2] They consider the new proposed hierarchical formulation model and prior distributions, as well as the full Conditional posterior distributions structural under non conditioning on σ2 which makes the uncertainty, of a unimodal full posterior, Conditioning on σ2 is important, because it guarantees a unimodal full posterior Park and Casella [3]. So, we can conclude that [1] proposed new hierarchical representation utilizing a Non- scale mixture distributions, which needs to deal with this problem . To address this problem we consider new hierarchical representation of the adaptive Bayesian lasso for Tobit model based on scale mixture of Uniform density, mixing with standard exponential distribution.

Al-Qadisiyah Journal of Pure Science

Bayesian regression analysis has great importance in recent years, especially in the Regularizati... more Bayesian regression analysis has great importance in recent years, especially in the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing the prior distribution of the interested parameter is the main idea in the Bayesian regression analysis. By penalizing the

AL-Qadisiyah Journal For Administrative and Economic sciences, 2005

AL-Qadisiyah Journal For Administrative and Economic sciences, 2006

Enterprise and Organizational Modeling and Simulation, Jun 7, 2010

This paper discusses the problem of decision support systems in the organization. The procedure (... more This paper discusses the problem of decision support systems in the organization. The procedure (linear combination) developed with the aim to combine some predicted results obtained with simulation of linear and nonlinear regression models (experts), multiple regression model, nonparametric regression model, and semi parametric regression model. This adjustment procedure enforce some statistical characteristics like the expected value of the gross production rate based on Cobb-Douglas production function is unbiased for the actual value, and the total weights(importance) of all models(experts) is equal to one. We used modeling and simulation techniques to generate our data and to apply the procedure.

Periodicals of Engineering and Natural Sciences (PEN), 2020

Lasso estimate as the posterior mode assuming that the parameter has prior density as double expo... more Lasso estimate as the posterior mode assuming that the parameter has prior density as double exponential distribution [1]. In this paper, we proposed Scale Mixture of Normals mixing with Rayleigh (SMNR) density on their variances to represent the double exponential distribution. Hierarchical model formulation presented with Gibbs sampler under SMNR as alternative Bayesian analysis of minimization problem of classical lasso. We conducted two simulation examples to explore path solution of the Ridge, Lasso, Bayesian Lasso, and New Bayesian Lasso (R, L, BL, NBL) regression methods through the prediction accuracy using the bias of the estimates with different sample sizes, bias indicates that the lasso regression perform well, followed by the NBL. The Median Mean Absolute Deviations (MMAD) used to compared the perform of the regression methods using real data, MMAD indicates that the proposed method (NBL) perform better than the others.

Periodicals of Engineering and Natural Sciences (PEN), 2020

Since lasso method launched, a lot of applications and extensions were run on it which made it to... more Since lasso method launched, a lot of applications and extensions were run on it which made it to become deeply widely used in various discipline. In this paper, we proposed the Scale Mixture of Normals mixing with Rayleigh (SMNR) distribution on their variances to represent the double exponential distribution. Hierarchical model formula have derived with Gibbs sampler for SMNR. The proposed models; Bayesian Tobit Adaptive Lasso (BTAL) and Bayesian Tobit Lasso (BTL) models are illustrated using simulation example and a real data example through the prediction accuracy using the estimated relative efficiency with different sample. This is the first work that discussed regularization regression models under SMNR.

Periodicals of Engineering and Natural Sciences (PEN), 2020

In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite... more In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite quantile regression model when the response variable is binary. For selecting variables in binary composite quantile regression lasso the adaptive lasso penalty is derived in a Bayesian framework. Simulation study and real data examples are used to examine the performance of the proposed methods compared to the other existing methods. We conclude that the proposed method is comparable.

Journal of Statistics Applications & Probability Letters, 2019

We propose the use of the Epsilon Skew Exponential Power family of distribution as an alternative... more We propose the use of the Epsilon Skew Exponential Power family of distribution as an alternative model to make inference on estimating the interested parameters of the Tobit regression model. Tobit model assumes the normality of residuals term. Elsalloukh[5] proposed the Epsilon Skew Exponential Power (ESEP) family of distributions which include the ESN distribution and many other distributions as special cases. Moreover, ESEP family of densities can accommodate both heavy tails and skewness behaviors. We develop the basic properties of the ESEP Tobit model, such as the structural equation, the expected value of the censored variable, and the log-likelihood functions based on the piecewise nature of the ESEP density. We provide data analysis when the residuals are distributed according to the ESEP(θ,σ,α,ε) family.

Journal of Statistics Applications & Probability Letters, 2020

In this work, we consider a case study for the Bi-Epsilon Skew Exponential Power (BIESEP) ROC cur... more In this work, we consider a case study for the Bi-Epsilon Skew Exponential Power (BIESEP) ROC curve proposed by Flaih et al. [3]. This model is a generalization of the Epsilon Skew bi-normal ROC curve proposed by Mashtare Jr. and Huston [10]. Elsalloukh [1, 2] provided the Epsilon Skew Exponential Power (ESEP) which is less sensitive to outliers. The ESEP family can be adopted to cope with skewness and kurtosis of a data set. The ESEP model provides an appropriate choice to increase the robustness of data analysis. We develop the Epsilon Skew bi-normal ROC curve based on the outcomes of the diagnostic test that is distributed according to the ESEP distribution. More specifically, we derive the BIESEP ROC parameters and the Area Under the Curve (AUC). Also, we consider the parameter estimation of BIESEP ROC curve and the AUC of a diagnostic test. We employ the BIESEP ROC curve to analyze a real dataset.

Journal of Al-Qadisiyah for Computer Science and Mathematics, 2019

Count data, including zero counts arise in a wide variety of application, hence models for counts... more Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the zero-inflated Poisson (ZIP) model and the negative binomial model. In this paper, the models, Poisson, Negative Binomial, ZIP, and ZINB were been used to analyze rainfall data.