Abstract
The Miller–Ross-type Poisson distribution is an important model for plenty of real-world applications. In the present analysis, we study and introduce a new class of bi-univalent functions defined by means of Gegenbauer polynomials with a Miller–Ross-type Poisson distribution series. For functions in each of these bi-univalent function classes, we have derived and explored estimates of the Taylor coefficients and and Fekete-Szegö functional problems for functions belonging to these new subclasses.
Keywords:
Poisson distribution series; Gegenbauer polynomials; bi-univalent functions; analytic functions; Fekete-Szegö problem MSC:
30C45
1. Definitions and Preliminaries
In recent years, the distributions of random variables have generated a great deal of interest. Their probability density functions have played an important role in statistics and probability theory. Because of this, the study of distributions has been considerable. Many forms of distributions are regarded from real-life situations, such as binomial distribution, Poisson distribution and hyper geometric distribution.
A distribution is a Poisson distribution if its probability density function for a random variable x is given by:
and m is the parameter of the distribution.
Let denote the class of all normalized analytic functions f of the form:
In addition, the open unit disk Further, let denote the class of all functions which are univalent in .
Let the functions f and g be analytic in . We say that the function f is subordinate to g, written as if there exists a Schwarz function which is analytic in with
such that
In addition, if the function g is univalent in , then the following equivalence holds:
and
It is well known that every function has an inverse , defined by
and
where
A function is said to be bi-univalent in if both and are univalent in .
Let denote the class of bi-univalent functions in given by (2). For interesting subclasses of functions in the class , see [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21].
Orthogonal polynomials have been extensively studied in recent years from various perspectives due to their importance in mathematical statistics, mathematical physics, probability theory and engineering. From a mathematical point of view, orthogonal polynomials often arise from solutions of ordinary differential equations under certain conditions imposed by a certain model. Orthogonal polynomials that appear most commonly in applications are the classical orthogonal polynomials (Legendre polynomials, Chebyshev polynomials, Horadam polynomials, Fibonacci polynomials and Jacobi polynomials). For a recent connection between the geometric function theory and orthogonal polynomials, see [7,22,23,24].
In 2020, Amourah et al. [1] considered the following generating function of Gegenbauer polynomials:
For a fixed x, the function is analytic in , so it can be expanded in a Taylor series as:
where , and is a Gegenbauer polynomial of degree n.
Clearly, generates nothing when . Therefore, the generating function of the Gegenbauer polynomial is set to be:
for . Moreover, it is worth mentioning that a normalization of to be greater than is desirable [25]. Gegenbauer polynomials can also be defined by the following recurrence relations:
with the initial values:
Special cases:
- i
- When we obtain the Chebyshev Polynomials.
- ii
- When we obtain the Legendre Polynomials.
Let be the Miller–Ross function [26] (see also, [10,27,28]) defined by
In addition, let be the two parameters of the Mittag–Leffler function [18] defined by:
Several properties of the Mittag–Leffler function and the generalized Mittag–Leffler function can be found in [3,4,6,8].
Very recently, Şeker et al. [30] introduced a power series whose coefficients are Miller–Ross-type Poisson distributions as follows:
where , .
In addition, they define the series
Now, we consider the linear operator defined by the convolution or Hadamard product
where and .
Motivated essentially by the work of Amourah et al. [20], we introduce a new subclass of involving the Pascal distribution associated with Gegenbauer polynomial and obtain bounds for the Taylor–Maclaurin coefficients and and Fekete-Szegö functional problems [31] for functions in this new class.
2. Coefficient Bounds of the Class
We begin this section by defining the new subclass associated with the Miller–Ross-type Poisson distribution
Definition 1.
Upon specializing the parameters and , one can obtain the various new subclasses of , as illustrated in the following examples.
Example 1.
Example 2.
Example 3.
Unless otherwise mentioned, we shall assume in this paper that ,and
First, we give the coefficient estimates for the class given in Definition 1.
Theorem 1.
Proof.
Let From Definition 1, for some analytic functions v such that and for all then we can write:
and
It is fairly well known that if
and
then
Thus, applying (8), we conclude that
This completes the proof of the Theorem. □
Making use of the values of and , we prove the following Fekete–Szegö inequality for functions in the class .
Theorem 2.
3. Corollaries and Consequences
Corresponding essentially to Examples 1–3, Theorems 1 and 2 yield the following corollaries.
Corollary 1.
Corollary 2.
Corollary 3.
Corollary 4.
Remark 1.
The results presented in this paper would lead to various other new results for the classes for Chebyshev Polynomials and for Legendre Polynomials.
4. Conclusions
In our present investigation, we have introduced a new class of normalized analytic and bi-univalent functions associated with the Miller–Ross-type Poisson distribution series. For functions belonging to this class, we have derived the estimates of the Taylor–Maclaurin coefficients and and the Fekete–Szegö functional problems. Furthermore, the results for the subclasses and , which are defined in Examples 1–3, respectively, are associated with the Miller–Ross-type Poisson distribution series.
Author Contributions
Conceptualization, A.A., B.A.F. and T.M.S.; Data curation, A.A., B.A.F. and T.M.S.; Formal analysis, A.A., B.A.F. and T.M.S.; Funding acquisition, A.A. and T.M.S.; Investigation, A.A., B.A.F. and T.M.S.; Methodology, A.A., B.A.F. and T.M.S.; Resources, T.M.S.; Software, B.A.F. and T.M.S.; Writing—original draft, A.A., B.A.F. and T.M.S.; Writing—review & editing, A.A., B.A.F. and T.M.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Deanship of Scientific Research at Umm Al-Qura University, grant number [22UQU4350561DSR02], and the APC was funded by the Deanship of Scientific Research at Umm Al-Qura University.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
No data were used to support this study.
Acknowledgments
The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4350561DSR02).
Conflicts of Interest
The authors declare no conflict of interest.
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