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Exponential-logarithmic distribution

In probability theory and statistics, the Exponential-Logarithmic (EL) distribution is a family of lifetime distributions with decreasing failure rate, defined on the interval [0, ∞). This distribution is parameterized by two parameters and .

Introduction
The study of lengths of the lives of organisms, devices, materials, etc., is of major importance in the biological and engineering sciences. In general, the lifetime of a device is expected to exhibit decreasing failure rate (DFR) when its behavior over time is characterized by 'work-hardening' (in engineering terms) or 'immunity' (in biological terms). The exponential-logarithmic model, together with its various properties, are studied by Tahmasbi and Rezaei (2008). This model is obtained under the concept of population heterogeneity (through the process of compounding). == Properties of the distribution ==
Properties of the distribution
Distribution The probability density function (pdf) of the EL distribution is given by Tahmasbi and Rezaei (2008) :\operatorname{Li}_a(z) =\sum_{k=1}^{\infty}\frac{z^k}{k^a}. Hence the mean and variance of the EL distribution are given, respectively, by :E(X)=-\frac{\operatorname{Li}_2(1-p)}{\beta\ln p}, :\operatorname{Var}(X)=-\frac{2 \operatorname{Li}_3(1-p)}{\beta^2\ln p}-\left(\frac{ \operatorname{Li}_2(1-p)}{\beta\ln p}\right)^2. The survival, hazard and mean residual life functions The survival function (also known as the reliability function) and hazard function (also known as the failure rate function) of the EL distribution are given, respectively, by : s(x)=\frac{\ln(1-(1-p)e^{-\beta x})}{\ln p}, : h(x)=\frac{-\beta(1-p)e^{-\beta x}}{(1-(1-p)e^{-\beta x})\ln(1-(1-p)e^{-\beta x})}. The mean residual lifetime of the EL distribution is given by : m(x_0;p,\beta)=E(X-x_0|X\geq x_0;\beta,p)=-\frac{\operatorname{Li}_2(1-(1-p)e^{-\beta x_0})}{\beta \ln(1-(1-p)e^{-\beta x_0})} where \operatorname{Li}_2 is the dilogarithm function Random number generation Let U be a random variate from the standard uniform distribution. Then the following transformation of U has the EL distribution with parameters p and β: : X = \frac{1}{\beta}\ln \left(\frac{1-p}{1-p^U}\right). == Estimation of the parameters ==
Estimation of the parameters
To estimate the parameters, the EM algorithm is used. This method is discussed by Tahmasbi and Rezaei (2008). The EM iteration is given by : \beta^{(h+1)} = n \left( \sum_{i=1}^n\frac{x_i}{1-(1-p^{(h)})e^{-\beta^{(h)}x_i}} \right)^{-1}, : p^{(h+1)}=\frac{-n(1-p^{(h+1)})} { \ln( p^{(h+1)}) \sum_{i=1}^n \{1-(1-p^{(h)})e^{-\beta^{(h)} x_i}\}^{-1}}. ==Related distributions==
Related distributions
The EL distribution has been generalized to form the Weibull-logarithmic distribution. If X is defined to be the random variable which is the minimum of N independent realisations from an exponential distribution with rate parameter β, and if N is a realisation from a logarithmic distribution (where the parameter p in the usual parameterisation is replaced by ), then X has the exponential-logarithmic distribution in the parameterisation used above. ==References==
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