H. C. Taneja

Characterization of a quantitative-qualitative measure of relative information

Abstract A quantitative-qualitative measure of relative information is suggested and is characterized by assuming some suitable postulates. Some properties of the new measure are also mentioned.

A GENERALIZED ENTROPY-BASED RESIDUAL LIFETIME DISTRIBUTIONS

The present communication considers Havrda and Charvat entropy measure to propose a generalized dynamic information measure. It is shown that the proposed measure determines the survival function uniquely. The residual lifetime distributions have been characterized. A bound for the dynamic entropy measure in terms of mean residual life function has been derived, and its monotonicity property is studied.

Dynamic Cumulative Residual and Past Inaccuracy Measures

In this paper, we have developed measures of dynamic cumulative residual and past inaccuracy. We have studied characterization results under proportional hazard model in case of dynamic cumulative residual inaccuracy and under proportional reversed hazard model in case of dynamic cumulative past inaccuracy measure. We have characterized certain specific lifetime distributions using the measures proposed. Some generalized results have also been considered.

Stochastic Comparisons of Residual Entropy of Order Statistics and Some Characterization Results

In this paper, we have presented some results for the residual and past entropies of order statistics. Results on the stochastic comparisons based on residual entropy of order statistics are presented. Characterization results for these dynamic entropies based on the sufficient condition for the uniqueness of the solution of an initial value problem have been considered.

On the quantitative-qualitative measure of relative information

Abstract Taneja and Tuteja [7] have introduced the concept of a relative “useful” information measure which satisfies weighted additivity. In this paper an inverse problem is studied. By assuming the sum property, the equation satisfied by weighted additivity is converted to a functional equation, and then, in terms of the real and continuous solution of the functional equation, the quantitative-qualitative measure of relative “useful” information is characterized.

A Measure of Inaccuracy in Order Statistics

In this article, we consider a measure of inaccuracy between distributions of the i th order statistics and parent random variable. It is shown that the inaccuracy measure characterizes the distribution function of parent random variable uniquely. We also discuss some properties of the proposed measure.

Length biased weighted residual inaccuracy measure

SummaryIn the present communication we introduce a length biased weighted residual inaccuracy measure between two residual lifetime distributions over the interval (t, ∞). Based on proportional hazard model (PHM), a characterization problem for the weighted residual inaccuracy measure has been studied. A lower bound to the weighted residual inaccuracy measure has also been derived.

On characterization of quantitative-qualitative measure of inaccuracy

Abstract We have characterized quantitative-qualitative measure of inaccuracy [4] by assuming a set of intuitive postulates and with the help of functional equations.

On a Class of Meromorphic Functions Defined by Certain Linear Operators

In the present investigation, we introduce new classes of p-valent meromorphic functions defined by Liu-Srivastava linear operator and the multiplier transform and study their properties by using certain first order differential subordination and superordination.

Inaccuracy and the best 1:1 code

Abstract The expected codeword length L UD of the best uniquely decodable ( UD ) code satisfies H ( P ; Q ) ≤ L UD H ( P ; Q ) + 1, where H ( P ; Q ) is the Kerridge inaccuracy. By applying the idea of the best 1:1 code given by Leung Yan Cheong and T. Cover [ IEEE Trans. Inform. Theory IT-24, 331–338 (1978)] a relation between inaccuracy and the best 1:1 codeword length L 1:1 has been obtained. Further, it is shown that the average codeword length L 1:1 is shorter than the average codeword length L UD by no more than log · log n + 3. Also, the lower bounds to the exponentiated mean codeword length in terms of the inaccuracy of type α have been obtained.