On measures of non-degeneracy

1992 ◽  
Vol 29 (3) ◽  
pp. 733-739
Author(s):  
K. B. Athreya

If φ is a convex function and X a random variable then (by Jensen's inequality) ψ φ (X) = Eφ (X) – φ (EX) is non-negative and 0 iff either φ is linear in the range of X or X is degenerate. So if φ is not linear then ψ φ (X) is a measure of non-degeneracy of the random variable X. For φ (x) = x2, ψ φ (X) is simply the variance V(X) which is additive in the sense that V(X + Y) = V(X) + V(Y) if X and Y are uncorrelated. In this note it is shown that if φ ″(·) is monotone non-increasing then ψ φ is sub-additive for all (X, Y) such that EX ≧ 0, P(Y ≧ 0) = 1 and E(X | Y) = EX w.p.l, and is additive essentially only if φ is quadratic. Thus, it confirms the unique role of variance as a measure of non-degeneracy. An application to branching processes is also given.


1992 ◽  
Vol 29 (03) ◽  
pp. 733-739
Author(s):  
K. B. Athreya

If φ is a convex function and X a random variable then (by Jensen's inequality) ψ φ (X) = Eφ (X) – φ (EX) is non-negative and 0 iff either φ is linear in the range of X or X is degenerate. So if φ is not linear then ψ φ (X) is a measure of non-degeneracy of the random variable X. For φ (x) = x 2, ψ φ (X) is simply the variance V(X) which is additive in the sense that V(X + Y) = V(X) + V(Y) if X and Y are uncorrelated. In this note it is shown that if φ ″(·) is monotone non-increasing then ψ φ is sub-additive for all (X, Y) such that EX ≧ 0, P(Y ≧ 0) = 1 and E(X | Y) = EX w.p.l, and is additive essentially only if φ is quadratic. Thus, it confirms the unique role of variance as a measure of non-degeneracy. An application to branching processes is also given.



2009 ◽  
Vol 147 (1-2) ◽  
pp. 217-239 ◽  
Author(s):  
Guangyan Jia ◽  
Shige Peng


2018 ◽  
Vol 51 (1) ◽  
pp. 112-130
Author(s):  
Nasir Mehmood ◽  
Saad Ihsan Butt ◽  
Ðilda Pečarić ◽  
Josip Pečarić

AbstractTo procure inequalities for divergences between probability distributions, Jensen’s inequality is the key to success. Shannon, Relative and Zipf-Mandelbrot entropies have many applications in many applied sciences, such as, in information theory, biology and economics, etc. We consider discrete and continuous cyclic refinements of Jensen’s inequality and extend them from convex function to higher order convex function by means of different new Green functions by employing Hermite interpolating polynomial whose error term is approximated by Peano’s kernal. As an application of our obtained results, we give new bounds for Shannon, Relative and Zipf-Mandelbrot entropies.



Filomat ◽  
2016 ◽  
Vol 30 (3) ◽  
pp. 803-814 ◽  
Author(s):  
Adil Khan ◽  
T. Ali ◽  
A. Kılıçman ◽  
Q. Din

In this paper our aim is to give refinements of Jensen?s type inequalities for the convex function defined on the co-ordinates of the bidimensional interval in the plane.



2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Huixia Mo ◽  
Xin Sui ◽  
Dongyan Yu

We introduce the generalized convex function on fractal setsRα  (0<α≤1)of real line numbers and study the properties of the generalized convex function. Based on these properties, we establish the generalized Jensen’s inequality and generalized Hermite-Hadamard's inequality. Furthermore, some applications are given.



2010 ◽  
Vol 7 (3) ◽  
pp. 036009 ◽  
Author(s):  
C Bodenstein ◽  
B Knoke ◽  
M Marhl ◽  
M Perc ◽  
S Schuster


Author(s):  
Muhammad Adil Khan ◽  
Muhammad Hanif ◽  
Zareen Abdul Hameed Khan ◽  
Khurshid Ahmad ◽  
Yu-Ming Chu


2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Ying-Qing Song ◽  
Muhammad Adil Khan ◽  
Syed Zaheer Ullah ◽  
Yu-Ming Chu

We study the notions of strongly convex function as well as F-strongly convex function. We present here some new integral inequalities of Jensen’s type for these classes of functions. A refinement of companion inequality to Jensen’s inequality established by Matić and Pečarić is shown to be recaptured as a particular instance. Counterpart of the integral Jensen inequality for strongly convex functions is also presented. Furthermore, we present integral Jensen-Steffensen and Slater’s inequality for strongly convex functions.



2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Mu Zhao ◽  
Xinai Yang ◽  
Qi He ◽  
Zunrong Zhou ◽  
Xiangyu Ge

AbstractQuantiles of random variable are crucial quantities that give more delicate information about distribution than mean and median and so on. We establish Jensen’s inequality for q-quantile ($q\geq 0.5$ q ≥ 0.5 ) of a random variable, which includes as a special case Merkle (Stat. Probab. Lett. 71(3):277–281, 2005) where Jensen’s inequality about median (i.e. $q= 0.5$ q = 0.5 ) was given. We also refine this inequality in the case where $q<0.5$ q < 0.5 . An application to the confidence interval of parameters in pivotal quantity is also considered by virtue of the rigorous description on the relationship between quantiles and intervals that have required probability.



Author(s):  
D. J. F. Nonnenmacher ◽  
R. Zagst

AbstractJensen's inequality for the expectation of a convex function of a random variable is proved for a wide class of convex functions defined on a space of probability measures. The result is applied to statistical experiments using the concept of Blackwell-sufficiency. In particular, we show a monotonicity result for the expected information of Poisson-experiments. As an application to economics we consider the introduction of new production technologies.



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