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Quantile based entropy function in past lifetime

TLDR
In this article, the authors introduced a quantile version of the entropy function in past lifetime and studied its properties, and proved characterizations theorems for some well known quantile lifetime distributions.
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This article is published in Statistics & Probability Letters.The article was published on 2013-01-01 and is currently open access. It has received 39 citations till now. The article focuses on the topics: Differential entropy & Quantile function.

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Citations
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Kullback–Leibler divergence: A quantile approach

TL;DR: A quantile based definition of the Kullback–Leibler divergence is introduced and the quantile versions of Kull back–Leiberler divergence for residual and past lifetime random variables are proposed.
Journal ArticleDOI

Some properties and applications of cumulative Kullback-Leibler information

TL;DR: In this article, a dynamic version of the cumulative Kullback-Leibler information has been proposed for past lifetimes, which is related to the concept of relative aging.
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Rényi’s residual entropy: A quantile approach

TL;DR: A quantile based Renyi’s entropy function and its residual version are introduced and certain properties and applications of the measure are studied.
Journal ArticleDOI

Quantile-based cumulative entropies

TL;DR: This paper introduces and study quantile versions of the cumulative entropy functions in the residual and past lifetimes, and studies the quantile-based cumulative entropy measures uniquely determine the underlying probability distribution.
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Quantile-Based Entropy of Order Statistics

TL;DR: A quantile-based residual entropy of order statistics, an alternative method to measure the uncertainty of ordered observations for used items, is proposed and an explicit relationship between the quantile density function and quantile to quantile distribution function is derived.
References
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Journal ArticleDOI

A mathematical theory of communication

TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
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Nonparametric Statistical Data Modeling

TL;DR: An approach to statistical data analysis which is simultaneously parametric and nonparametric is described, and density-quantile functions, autoregressive density estimation, estimation of location and scale parameters by regression analysis of the sample quantile function, and quantile-box plots are introduced.
Book

Statistical Modelling with Quantile Functions

TL;DR: In this article, the authors describe the sample and the population statistical foundations of Quantile Models and their construction and their use in identification estimation validation applications, including regression quantile models and Bivariate Quantile models.
Journal ArticleDOI

An approximate method for generating asymmetric random variables

TL;DR: A method for generating values of continuous symmetric random variables that is relatively fast, requires essentially no computer memory, and is easy to use is developed.

On discrete distributions arising out of methods of ascertainment

TL;DR: In this paper, the frequency of families with both parents heterozygous for albinism and having no albino children was investigated and the actual frequency of the event was not ascertainable.
Frequently Asked Questions (11)
Q1. what is the entropy function in past lifetime?

The entropy function in past lifetime (3) in terms of QF is defined byψ(u) = ξ(X;Q (u)) = log u + u−1 u 0 (log q(p))dp. (8)The measure (8) gives the expected uncertainty contained in the conditional density about the predictability of an outcome of X until 100u% point of distribution. 

Many of the quantile functions used in applied work like various forms of lambda distributions (Ramberg and Schmeiser, 1974; Freimer et al., 1988; van Staden and Loots, 2009; Gilchrist, 2000), the power-Pareto distribution (Gilchrist, 2000, Hankin and Lee, 2006), Govindarajulu distribution (Nair et al., 2011) etc. do not have tractable distributions. 

Using the QF defined in (5) and (6), the Shannon entropy in (1) can be written as ξ = ξ(X) = − 1 0 (log fQ (p))fQ (p)dQ (p),= 1 0 (log q(p))dp. (7)Clearly, by knowing eitherQ (u) or q(u), the expression for ξ(X) is quite simple to compute. 

1u log 1+u 1−u , holds for all u if and only if X follows a half-logistic distribution with Q (u) = σ log (1+u) (1−u) , σ > 0.Theorem 10. 

For equilibrium distribution with density functionfE(t) = F(t)/µ, (16)we have 1qE (u) = (1−u) µ , or log qE(u) = logµ − log(1 − u), then using (8) the PQE is given by ψE(u) = 1 + logµ + log u + (1−u)u log(1 − u) = ψE(u)+ log u + (1−u)u log(1 − u). 

Theorem 8. The rv X is distributed as power function with Q (u) = αu1/β;α, β > 0, holds for all u if and only if it satisfies the relationship ψ(u) = C − log A(u) where 0 < C < 1.Theorem 9. The relationshipψ(u) = 2− log A(u)− 

The nonnegative function φ(x) = xα, x > 0, is concave if 0 < α < 1. Hence due to Theorem 1, the inverted Weibull is DPQE for 0 < α < 1. 

Using fw(t), the corresponding density quantile function is given byfw(Q (u)) = w(Q (u))f (Q (u))/µ, where µ = 1 0 w(Q (p))f (Q (p))d(Q (p)) = 1 0 w(Q (p))dp. 

it has been shown by many authors that quantile functionsQ (u) = F−1(u) = inf{t | F(t) ≥ u}, 0 ≤ u ≤ 1 (5)are efficient and equivalent alternatives to the distribution function inmodeling and analysis of statistical data (see Gilchrist, 2000; Nair and Sankaran, 2009). 

Denoting a(x) = f (x)/F(x) the reversed hazard rate (see Block et al., 1998), Eq. (3) can be rewritten asξ(X; t) = 1 − 1F(t) t 0 log(a(x))f (x)dx. (4)Given that at time t , a unit is found to be down, ξ(X; t) measures the uncertainty about its past lifetime. 

For the exponential distribution in the support of (−∞, 0) with F(t) = exp (λt) , λ > 0 (see Block et al., 1998) wehave Q (u) = 1 λ log u, q(u) = 1 λu and A(u) = λ so that ψ(u) = 1 + log(uq(u)) = 1 − log A(u) = 1 − log λ.