Abstract
The aim of this paper is to establish an inequality for the gamma function, using a statistical method. Applications of the inequality are also given, including some estimates of π.
Similar content being viewed by others
1 Introduction and main result
Recently, there have been many papers about the ratio of gamma functions in the literature; see [1–9]. Some of the papers use statistical methods. Gurland [10] has given an inequality satisfied by the gamma function, using the so-called Cramér-Rao lower bound for the variance of unbiased estimators. Olkin [11] has given an extension of Gurland’s inequality. Gokhale [12] has given another inequality, which used an analogue of the Cramér-Rao lower bound derived by Rao [13]. Rao gave a stronger version of Wallis’ formula [14]. We, inspired by the above papers, give an inequality concerning the gamma function. Applications of the inequality are also given. We first recall some definitions, notation, and well-known results in statistical theory, which will be used in this paper.
A normal distribution \(N(\mu,\sigma^{2})\) is described by the probability density function,
When a random variable X is distributed normally with mean μ and variance \(\sigma^{2}\), we write \(X\sim N(\mu,\sigma^{2})\).
Suppose that \(x_{1},x_{2},\ldots,x_{n}\) is a sample from a population with a distribution function \(F_{\theta}(x)\) (\(\theta\in\Omega\)). Let \(\hat{g}=\hat{g}(x_{1},x_{2},\ldots,x_{n})\) be an estimator of a parametric function \(g(\theta)\). If \(E(\hat{g})=g(\theta)\) for all values of parameter \(\theta\in\Omega\), we call \(\hat{g}\) an unbiased estimator of \(g(\theta)\).
Consider an estimation of \(g(\theta)\) based on a sample \(x_{1},x_{2},\ldots,x_{n}\) from some member of a family of distribution functions \(F_{\theta}(x)\), \(\theta\in\Omega\), where Ω is the parameter space. An unbiased estimator \(\hat{g}(x_{1},x_{2},\ldots,x_{n})\) of \(g(\theta)\) is UMVUE, if \(\forall\theta\in\Omega\),
for any other unbiased estimator \(\tilde{g}\).
Euler’s gamma function Γ is defined for \(x>0\) by
If \(x_{1},x_{2},\ldots,x_{n}\) is a sample from a population with distribution \(N(\mu,\sigma^{2})\), then
is the UMVUE of σ.
The main result of this paper is the following theorem.
Theorem 1.1
Suppose that \(n_{k}\) (\(k=1,2,\ldots,m\)) are nonnegative integers and \(\lambda_{k}\in\mathbb{R}\), such that \(0\leq\lambda_{k}\leq1\), \(\sum_{k=1}^{m}\lambda_{k}=1\). Then we have
where \(n=\sum_{k=1}^{m}n_{k}\).
2 Proof of the main result
In this section, we use statistical methods to prove the theorem.
Proof
Let \(x_{11}, x_{12}, \ldots, x_{1n_{1}}, x_{21}, x_{22}, \ldots, x_{2n_{2}}, x_{m1}, x_{m2}, \ldots, x_{mn_{m}}\) be a random sample from a normal distribution \(X\sim N(\mu,\sigma^{2})\). From (1.4), it is known that
is the UMVUE of σ, where \(n=\sum_{i=1}^{m}n_{i}\).
For any \(x_{k1}, x_{k2}, \ldots, x_{kn_{k}}\), \(1\leq k\leq m\)
is an unbiased estimate of σ.
Using (2.2), we construct a new unbiased estimate of σ, i.e.,
where \(0\leq\lambda_{k}\leq1\), \(\sum_{k=1}^{m}\lambda_{k}=1\).
Due to the definition of the UMVUE, the following inequality holds:
After some simple computations, we can obtain
and
Substituting (2.5) and (2.6) into (2.4) gives (1.5). Thus, we complete the proof. □
3 Some applications of Theorem 1.1
In this section, we show some applications of the main result of this paper. First, we give the following inequalities, which include the gamma function and a trigonometric function.
Theorem 3.1
Suppose n is any positive integer, and \(\theta\in\mathbb{R}\), then
Proof
Letting \(m=2\) and \(\lambda_{1}=\sin^{2}\theta\), \(\lambda _{2}=\cos^{2}\theta\) in (1.5) gives
Letting \(n_{1}=n_{2}=a\) in (3.2), one obtains
Employing the following trigonometric formula:
(3.3) becomes
Replacing 2θ by θ and a by n, we obtain (3.1). Thus, we finish the proof. □
In [15], Gurland gave the following estimator of π:
Mortici [1] gave the refinements of Gurland’s formula for π:
Using (3.1), we can get the following similar result.
Corollary 3.2
Suppose n is any nonnegative integer, then
Proof
Letting \(\theta=\frac{\pi}{2}\) in (3.1) gives
We have
See, e.g., [1]. So
Because the equality in (3.11) cannot hold, we get (3.8). □
Now we give an inequality involving combinational coefficients \({n\choose m}\), defined by
Theorem 3.3
Suppose m, w are any positive integers, then
Proof
Since
Letting \(n_{k}=2w\), \(\lambda_{k}={m\choose k}\frac{1}{2^{m}}\) in (1.5), then \(n=\sum_{k=1}^{m}n_{k}=2mw\). We get
Using the inequality of (3.10) and after some simple derivations, we have
Substituting
into (3.15) one obtains (3.12). □
The special case \(w=1\) of (3.12) results in
Finally, we give the following double inequality for π.
Theorem 3.4
Let p, d are positive integers, then
Proof
Letting \(m=2d+1\) and \(n_{1}=n_{2}=\cdots=n_{m}=2p+1\) in (1.5) gives
Using (3.10), the inequality (3.19) can be written in the equivalent form
Letting \(\lambda_{1}=\lambda_{2}=\cdots=\lambda_{m}=\frac{1}{2d+1}\) in (3.20) gives
Similarly, letting \(m=2d+1\), \(n_{1}=n_{2}=\cdots=n_{m}=2p\), and \(\lambda_{1}=\lambda_{2}=\cdots=\lambda_{m}=\frac{1}{2d+1}\) in (1.5), one can obtain
Then the inequality of (3.18) is the combination of the inequality (3.21) and the inequality (3.22). □
The special case \(p=1\) of (3.18) results in
References
Mortici, C: Refinements of Gurland’ formula for pi. Comput. Math. Appl. 62, 2616-2620 (2011)
Mortici, C: New approximation formulas for evaluating the ratio of gamma functions. Math. Comput. Model. 52(1), 425-433 (2010)
Mortici, C: Sharp inequalities and complete monotonicity for the Wallis ratio. Bull. Belg. Math. Soc. Simon Stevin 17(5), 929-936 (2010)
Mortici, C, Qi, F: Some best approximation formulas and inequalities for the Wallis ratio. Appl. Math. Comput. 253, 363-368 (2015)
Mortici, C, Lu, D, Cristea, V: Complete monotonic functions and inequalities associated to some ratio of gamma function. Appl. Math. Comput. 240, 168-174 (2014)
Qi, F: Bounds for the ratio of two gamma functions: from Gautschi’s and Kershaw’s inequalities to complete monotonicity. Turk. J. Anal. Number Theory 2(5), 152-164 (2014). doi:10.12691/tjant-2-5-1
Qi, F, Luo, Q-M: Bounds for the ratio of two gamma functions: from Wendel’s asymptotic relation to Elezović-Giordano-Pečarić’s theorem. J. Inequal. Appl. 2013, 542 (2013). doi:10.1186/1029-242X-2013-542
Qi, F, Luo, Q-M: Bounds for the ratio of two gamma functions - from Wendel’s and related inequalities to logarithmically completely monotonic functions. Banach J. Math. Anal. 6(2), 132-158 (2012)
Qi, F: Bounds for the ratio of two gamma functions. J. Inequal. Appl. 2010, Article ID 493058 (2010). doi:10.1155/2010/493058
Gurland, J: An inequality satisfied by the gamma function. Skand. Aktuarietidskr. 1956, 171-172 (1956)
Olkin, I: An inequality satisfied by the gamma function. Skand. Aktuarietidskr. 1959, 37-39 (1959)
Gokhale, DV: On an inequality for gamma functions. Skand. Aktuarietidskr. 1962, 213-215 (1962)
Rao, BR: On an analogue of Cramer-Rao inequality. Skand. Aktuarietidskr. 1959, 213-215 (1959)
Rao Uppuluri, VR: On a stronger version of Wallis’ formula. Pac. J. Math. 19(1), 183-187 (1966)
Gurland, J: On Wallis’ formula. Am. Math. Mon. 63, 643-645 (1956)
Acknowledgements
The authors would like to thank two anonymous referees for many helpful comments and suggestions. We acknowledge support by the National Natural Science Foundation (grant 11271057) of China.
Author information
Authors and Affiliations
Corresponding author
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
All authors read and approved the final manuscript.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Zhang, P., Wang, M. An inequality for the gamma function via statistics and applications. J Inequal Appl 2015, 185 (2015). https://doi.org/10.1186/s13660-015-0705-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s13660-015-0705-5