Maximum likelihood estimation method, uniformly minimum variance unbiased estimation method and minimum mean square error estimation, as classical estimation procedures, are frequently used for parameter estimation in statistics, which assuming the parameter is constant , while Bayes method assuming the parameter is random variable and hence the Bayes estimator is an estimator which minimize the Bayes risk for each value the random observable and for square error lose function the Bayes estimator is the posterior mean. It is well known that the Bayesian estimation is hardly used as a parameter estimation technique due to some difficulties to finding a prior distribution.

The interest of this paper is that

In this paper, a new class of harmonic univalent functions was defined by the differential operator. We obtained some geometric properties, such as the coefficient estimates, convex combination, extreme points, and convolution (Hadamard product), which are required

In this paper, we show many conclusions on the Quasi-Hadamard products of new Subclass of analytic functions of β-Uniformly univalent function  defined by Salagean q-differential operator.

     In this research paper, we explain the use of  the convexity and the starlikness properties of  a given function  to generate  special properties of differential subordination and superordination functions in the classes of analytic functions that have  the form   in the unit disk. We also show the significant of  these properties to derive sandwich results when the Srivastava- Attiya operator   is used.

In this paper, a differential operator is used to generate a subclass of analytic and univalent functions with positive coefficients. The studied class of the functions includes:  

which is defined in the open unit disk  satisfying the following condition

This leads to the study of properties such as coefficient bounds, Hadamard product, radius of close –to- convexity, inclusive properties, and (n, τ) –neighborhoods for functions belonging to our class.

    This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find  that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.

     The purpose of this  paper is to show that for a holomorphic and univalent function in class S, an omitted –value transformation  yields a class of starlike functions as a rotation transformation of  the Koebe function, allowing both the image and rotation of the function

   to be connected. Furthermore, these functions have several properties that are not far from a convexity properties. We also show that Pre- Schwarzian derivative is not invariant since the convexity property of the function   is so weak.

    New class A^* (a,c,k,β,α,γ,μ)  is introduced of meromorphic univalent functions with positive coefficient f(z)=â–¡(1/z)+∑_(n=1)^∞▒〖a_n z^n 〗,(a_n≥0,z∈U^*,∀ n∈ N={1,2,3,…}) defined by the integral operator in the punctured unit disc U^*={z∈C∶0<|z|<1}, satisfying |(z^2 (I^k (L^* (a,c)f(z)))^''+2z(I^k (L^* (a,c)f(z)))^')/(βz(I^k (L^* (a,c)f(z)))^''-α(1+γ)z(I^k (L^* (a,c)f(z)))^' )|<μ,(0<μ≤1,0≤α,γ<1,0<β≤1/2 ,k=1,2,3,… ) . Several properties were studied like coefficient estimates, convex set and weighted mean.