It is pointed out that, one of the results in the recently published article, ’On the Iyengar-Madhava Rao-Nanjundiah inequality and it’s hyperbolic version’ [3] by J´ozsef S´andor is logically incorrect and new corrected result with it’s proof is presented.

Qishi Wu , Nageswara S.V. Rao , Richard R. Brooks , S. Sitharama Iyengar , Mengxia Zhu 3) 1) Center for Engineering Science Advanced Research, Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6355, {wuqn, raons}@ornl.gov 2) Distributed Systems Department, Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA ...

Using Hayashi's inequality, an Iyengar type inequality for functions with bounded second derivative is obtained. This result improves a similar result from [N. Elezovi´c, J. Pečari´c, Steffensen's inequality and estimates of error in trapezoidal rule, Appl. In 1938 Iyengar proved the following inequality in [1]: Theorem 1. Let function f be differentiable on [a, b] and | f (x)| ≤ M. Then 1 b − ...

As an application of the improved Cauchy-Schwartz inequality due to Walker (Statist. Probab. Lett. (2017) 122:86-90), we obtain an improved version of the Cramer-Rao inequality for randomly censored data derived by Abdushukurov and Kim (J. Soviet. Math. (1987) pp. 2171-2185). We derive a lower bound of Bhattacharya type for the mean square error of a parametric function based on randomly censor...

The book ‘Breaking Barriers: Story of a Dalit Chief Secretary’ published by Emesco Books Private Limited in 2022, and edited D. Chandrasekhar Reddy, is powerful account journey from fear to fearlessness, subjugation assertion being no one becoming revered exemplary civil servant. Authored the former IAS officer Kaki Madhava Rao, explores inner mechanism service at ground level casts light on mi...

In most situations the best estimator of a function of the parameter exists, but sometimes it has a complex form and we cannot compute its variance explicitly. Therefore, a lower bound for the variance of an estimator is one of the fundamentals in the estimation theory, because it gives us an idea about the accuracy of an estimator. It is well-known in statistical inference that the Cram&eac...