In this paper, compared eight methods for generating the initial value and the impact of these methods to estimate the parameter of a autoregressive model, as was the use of three of the most popular methods to estimate the model and the most commonly used by researchers MLL method, Barg method  and the least squares method and that using the method of simulation model  first order autoregressive through the design of a number of simulation experiments and the different sizes of the samples.

Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson

The main aim of this paper is to study how the different estimators of the two unknown parameters (shape and scale parameter) of a generalized exponential distribution behave for different sample sizes and for different parameter values. In particular, 

. Maximum Likelihood, Percentile and Ordinary Least Square estimators had been implemented for different sample sizes (small, medium, and large) and assumed several contrasts initial values for the two parameters. Two indicators of performance Mean Square Error and Mean Percentile Error were used and the comparisons were carried out between different methods of estimation  by using monte carlo simulation technique .. It was obse

In this paper, the maximum likelihood estimates for parameter ( ) of two parameter's Weibull are studied, as well as white estimators and (Bain & Antle) estimators, also Bayes estimator for scale parameter ( ), the simulation procedures are used to find the estimators and comparing between them using MSE. Also the application is done on the data for 20 patients suffering from a headache disease.

Biscuits are a global snack due to their convenience, variety, and durability. Biscuits with nutritious ingredients are in demand as customers become more health conscious. This change led to interest about utilizing agricultural by-products to enhance the nutritional value of widely consumed foods. Mango (Mangifera indica L.), a frequently cultivated tropical fruit, produces vital by-products during its processing, mainly comprising peels and kernels. The by-products, comprising around 35–60% of the mango fruit's weight, are high in bioactive compounds including dietary fiber, polyphenols, carotenoids, and essential fatty acids. Mango peels and kernels, even with their nutritional potential, frequently neglected, resulting in ris

Biscuits are a global snack due to their convenience, variety, and durability. Biscuits with nutritious ingredients are in demand as customers become more health conscious. This change led to interest about utilizing agricultural by-products to enhance the nutritional value of widely consumed foods. Mango (Mangifera indica L.), a frequently cultivated tropical fruit, produces vital by-products during its processing, mainly comprising peels and kernels. The by-products, comprising around 35–60% of the mango fruit's weight, are high in bioactive compounds including dietary fiber, polyphenols, carotenoids, and essential fatty acids. Mango peels and kernels, even with their nutritional potential, frequently neglected, resulting in ris

Abstract:

The models of time series often suffer from the problem of the existence of outliers ​​that accompany the data collection process for many reasons, their existence may have a significant impact on the estimation of the parameters of the studied model. Access to highly efficient estimators  is one of the most important stages of statistical analysis, And it is therefore important to choose the appropriate methods to obtain good  estimators. The aim of this research is to compare the ordinary estimators and the robust estimators of the estimation of the parameters of

Copula modeling is widely used in modern statistics. The boundary bias problem is one of the problems faced when estimating by nonparametric methods, as kernel estimators are the most common in nonparametric estimation. In this paper, the copula density function was estimated using the probit transformation nonparametric method in order to get rid of the boundary bias problem that the kernel estimators suffer from. Using simulation for three nonparametric methods to estimate the copula density function and we proposed a new method that is better than the rest of the methods by five types of copulas with different sample sizes and different levels of correlation between the copula variables and the different parameters for the function. The