🧭 New here?
Take a guided tour of the site.
← Back to Papers

Continue from this paper

Indexing is not verification. Extracted statements should be checked against the source.

Abstract

The main objective of the present paper is to define a subclass Qq(λ, µ, A, B) of analytic functions by using subordination along with the newly defined q-analogue of Choi-Saigo- Srivastava operator. Such results as coefficient estimates, integral representation, linear combination, weighted and arithmetic means, and radius of starlikeness for this class are derived. Mathematics Subject Classification (2010). 30C45, 30C50

Results & Lemmas (8)

Each result is auto-extracted from the paper via OCR - the math is shown verbatim and may carry occasional transcription artifacts. Click to expand the full statement.

Lemma 1.2. Lemma 1.2. [17] Let −1 ≤B2 ≤B1 < A1 ≤A2 ≤1. Then 1 + A1z 1 + B1z ≺1 + A2z 1 + B2z. Throughout this paper, we assume that λ > −1, µ > 0, 0 <…
Lemma 1.2. [17] Let −1 ≤B2 ≤B1 < A1 ≤A2 ≤1. Then 1 + A1z 1 + B1z ≺1 + A2z 1 + B2z . Throughout this paper, we assume that λ > −1, µ > 0, 0 < q < 1 and −1 ≤B < A ≤1, unless otherwise stated. We also suppose that all coefficients an of f are real positive numbers. 2. Main results
Theorem 2.1. Theorem 2.1. Let f ∈A and be of the form (1.1). Then f ∈Qq (λ, µ, A, B) if and only if ∞ X n=2 [n, q] (1 −B) −1 + A [µ, q]n−1 [1 + λ, q]n−1…
Theorem 2.1. Let f ∈A and be of the form (1.1). Then f ∈Qq (λ, µ, A, B) if and only if ∞ X n=2 {[n, q] (1 −B) −1 + A} [µ, q]n−1 [1 + λ, q]n−1 an < A −B. (2.1)
Theorem 2.2. Theorem 2.2. Let f ∈Qq (λ, µ, A, B). Then Iq λ,µf(z) = exp Z z 0 1 t 1 −Aϕ(t) 1 −Bϕ(t)  dq(t) , where |ϕ(z)| < 1.
Theorem 2.2. Let f ∈Qq (λ, µ, A, B) . Then Iq λ,µf(z) = exp Z z 0 1 t 1 −Aϕ(t) 1 −Bϕ(t)  dq(t)  , where |ϕ(z)| < 1.
Theorem 2.3. Theorem 2.3. Let fj ∈Qq (λ, µ, A, B) and have the form fj(z) = z + ∞ X k=1 ak,jzk (j = 1, 2, 3,..., l). Then F ∈Qq (λ, µ, A, B), where F(z)…
Theorem 2.3. Let fj ∈Qq (λ, µ, A, B) and have the form fj(z) = z + ∞ X k=1 ak,jzk (j = 1, 2, 3, . . . , l). Then F ∈Qq (λ, µ, A, B), where F(z) = l X j=1 cjfj(z) with l X j=1
Theorem 2.4. Theorem 2.4. If f and g belong to Qq (λ, µ, A, B), then their weighted mean hj (j ∈N) is also in Qq (λ, µ, A, B), where hj is defined by…
Theorem 2.4. If f and g belong to Qq (λ, µ, A, B) , then their weighted mean hj (j ∈N) is also in Qq (λ, µ, A, B) , where hj is defined by hj(z) = (1 −j) f(z) + (1 + j) g(z) 2 . (2.3)
Theorem 2.5. Theorem 2.5. Let fj with j = 1, 2,..., α (α ∈N) belong to the class Qq (λ, µ, A, B). Then the arithmetic mean h of fj given by h(z) = 1 α α…
Theorem 2.5. Let fj with j = 1, 2, ..., α (α ∈N) belong to the class Qq (λ, µ, A, B). Then the arithmetic mean h of fj given by h(z) = 1 α α X j=1 fj(z) (2.4) also belongs to the class Qq (λ, µ, A, B).
Theorem 2.6. Theorem 2.6. Let f ∈Qq (λ, µ, A, B). Then f ∈S∗(γ), for |z| < r1, where r1 =  (1 −γ) [n, q] (1 −B) −1 + A [µ,q]n−1 [1+λ,q]n−1 (n −γ) (A…
Theorem 2.6. Let f ∈Qq (λ, µ, A, B). Then f ∈S∗(γ) , for |z| < r1, where r1 =  (1 −γ) {[n, q] (1 −B) −1 + A} [µ,q]n−1 [1+λ,q]n−1 (n −γ) (A −B)   1 n−1 .
Theorem 2.7. Theorem 2.7. Let −1 ≤B2 ≤B1 < A1 ≤A2 ≤1 and Iq λ+1,µf(z) ̸= 0 in E. If ([λ + 1, q]) Iq λ,µf(z) qλIq λ+1,µf(z) −[λ, q] qλ ≺1 + A1z 1 + B1z.…
Theorem 2.7. Let −1 ≤B2 ≤B1 < A1 ≤A2 ≤1 and Iq λ+1,µf(z) ̸= 0 in E. If ([λ + 1, q]) Iq λ,µf(z) qλIq λ+1,µf(z) −[λ, q] qλ ≺1 + A1z 1 + B1z . Then f ∈Qq (λ + 1, µ, A2, B2) .
Function classes studied:

Related Papers

On some properties of bi-univalent functions in the unit disc
2026
Coefficient Estimates and Distortion Bounds for Rabotnov Functions with Applicat
2026
Properties of some univalent functions associated with balloon-shaped domain
2026
Texture enhancement of skin lesion images via Hankel determinants of $\lambda$-g
2026
Geometric Properties of Analytic Functions Defined by the Miller–Ross-Type Poiss
2026
↑↓ navigate↵ openesc closeall results
✦ 293 explorers have wandered the registry - thanks for stopping by. Tell us what you'd like to see →
Usage & privacy

We use first-party site metrics to improve navigation: page visits, internal clicks, search result counts (not search terms), and approximate active time. A random tab ID groups events for seven days; GPC/DNT turns this off. The separate visitor count uses a self-hosted pseudonymous fingerprint and also respects GPC/DNT.

💬 Feedback