The Gamma Distribution

The gamma distribution is a flexible life distribution model that may offer a good fit to some sets of failure data. It is not, however, widely used as a life distribution model for common failure mechanisms. The gamma distribution does arise naturally as the time-to-first-fail distribution for a system with standby exponentially distributed backups, and is also a good fit for the sum of independent exponential random variables. The gamma distribution is sometimes called the Erlang distribution, which is used frequently in queuing theory applications [32].

The Gamma Probability Density Function
The $$pdf$$  of the gamma distribution is given by:


 * $$f(t)=\frac{t\Gamma (k)}$$

where:


 * $$\begin{align}

z=\ln (t)-\mu \end{align}$$

and:


 * $$\begin{align}

& {{e}^{\mu }}= \text{scale parameter} \\ & k= \text{shape parameter} \end{align}$$

where $$00$$.

The Gamma Reliability Function
The reliability for a mission of time $$t\,\!$$  for the gamma distribution is:


 * $$\begin{align}

R=1-{{\Gamma }_{I}}(k;{{e}^{z}}) \end{align}$$

The Gamma Mean, Median and Mode
The gamma mean or MTTF is:


 * $$\overline{T}=k{{e}^{\mu }}$$

The mode exists if $$k>1\,\!$$  and is given by:


 * $$\tilde{T}=(k-1){{e}^{\mu }}$$

The median is:


 * $$\widehat{T}={{e}^{\mu +\ln (\Gamma _{I}^{-1}(0.5;k))}}$$

The Gamma Standard Deviation
The standard deviation for the gamma distribution is:


 * $${{\sigma }_{T}}=\sqrt{k}{{e}^{\mu }}$$

The Gamma Reliable Life
The gamma reliable life is:


 * $${{T}_{R}}={{e}^{\mu +\ln (\Gamma _{1}^{-1}(1-R;k))}}$$

The Gamma Failure Rate Function
The instantaneous gamma failure rate is given by:


 * $$\lambda =\frac{t\Gamma (k)(1-{{\Gamma }_{I}}(k;{{e}^{z}}))}$$

Characteristics of the Gamma Distribution
Some of the specific characteristics of the gamma distribution are the following:

For $$k>1\,\!$$ :
 * •	As $$t\to 0,\infty\,\! $$ ,  $$f(t)\to 0.\,\!$$
 * •	$$f(t)\,\!$$ increases from 0 to the mode value and decreases thereafter.
 * •	If  $$k\le 2\,\!$$  then  $$pdf$$  has one inflection point at  $$t={{e}^{\mu }}\sqrt{k-1}($$   $$\sqrt{k-1}+1).\,\!$$
 * •	If  $$k>2\,\!$$  then  $$pdf$$  has two inflection points for  $$t={{e}^{\mu }}\sqrt{k-1}(\,\!$$   $$\sqrt{k-1}\pm 1).\,\!$$
 * •	For a fixed $$k\,\!$$, as  $$\mu \,\!$$  increases, the  $$pdf$$ starts to look more like a straight angle.
 * •	As $$t\to \infty ,\lambda (t)\to \tfrac{1}.\,\!$$



For $$k=1$$ :
 * •	Gamma becomes the exponential distribution.
 * •	As $$t\to 0\,\!$$ ,  $$f(T)\to \tfrac{1}.\,\!$$
 * •	As $$t\to \infty ,f(t)\to 0.\,\!$$
 * •	The $$pdf$$  decreases monotonically and is convex.
 * •	 $$\lambda (t)\equiv \tfrac{1}\,\!$$ .  $$\lambda (t)\,\!$$  is constant.
 * •	The mode does not exist.



For $$0<k<1$$ :
 * •	As $$t\to 0\,\!$$ ,  $$f(t)\to \infty .\,\!$$
 * •	As $$t\to \infty ,f(t)\to 0.\,\!$$
 * •	As $$t\to \infty ,\lambda (t)\to \tfrac{1}.\,\!$$
 * •	The $$pdf\,\!$$  decreases monotonically and is convex.
 * •	As $$\mu \,\!$$  increases, the  $$pdf$$  gets stretched out to the right and its height decreases, while maintaining its shape.
 * •	As $$\mu \,\!$$  decreases, the  $$pdf$$  shifts towards the left and its height increases.
 * •	The mode does not exist.



Confidence Bounds
The only method available in Weibull++ for confidence bounds for the gamma distribution is the Fisher matrix, which is described next. The complete derivations were presented in detail (for a general function) in the Confidence Bounds chapter.

Bounds on the Parameters
The lower and upper bounds on the mean, $$\widehat{\mu }$$, are estimated from:


 * $$\begin{align}

& {{\mu }_{U}}= & \widehat{\mu }+{{K}_{\alpha }}\sqrt{Var(\widehat{\mu })}\text{ (upper bound)} \\ & {{\mu }_{L}}= & \widehat{\mu }-{{K}_{\alpha }}\sqrt{Var(\widehat{\mu })}\text{ (lower bound)} \end{align}$$

Since the standard deviation, $$\widehat{\sigma }$$, must be positive,  $$\ln (\widehat{\sigma })$$  is treated as normally distributed and the bounds are estimated from:


 * $$\begin{align}

& {{k}_{U}}= & \widehat{k}\cdot {{e}^{\tfrac{{{K}_{\alpha }}\sqrt{Var(\widehat{k})}}}}\text{ (upper bound)} \\ & {{k}_{L}}= & \frac{\widehat{\sigma }}\text{ (lower bound)} \end{align}$$

where $${{K}_{\alpha }}\,\!$$  is defined by:


 * $$\alpha =\frac{1}{\sqrt{2\pi }}\int_^{\infty }{{e}^{-\tfrac{2}}}dt=1-\Phi ({{K}_{\alpha }})$$

If $$\delta \,\!$$  is the confidence level, then  $$\alpha =\tfrac{1-\delta }{2}\,\!$$  for the two-sided bounds and  $$\alpha =1-\delta \,\!$$  for the one-sided bounds.

The variances and covariances of $$\widehat{\mu }\,\!$$  and  $$\widehat{k}\,\!$$  are estimated from the Fisher matrix, as follows:


 * $$\left( \begin{matrix}

\widehat{Var}\left( \widehat{\mu } \right) & \widehat{Cov}\left( \widehat{\mu },\widehat{k} \right) \\ \widehat{Cov}\left( \widehat{\mu },\widehat{k} \right) & \widehat{Var}\left( \widehat{k} \right) \\ \end{matrix} \right)=\left( \begin{matrix} -\tfrac{{{\partial }^{2}}\Lambda }{\partial {{\mu }^{2}}} & -\tfrac{{{\partial }^{2}}\Lambda }{\partial \mu \partial k} \\ {} & {} \\   -\tfrac{{{\partial }^{2}}\Lambda }{\partial \mu \partial k} & -\tfrac{{{\partial }^{2}}\Lambda }{\partial {{k}^{2}}}  \\ \end{matrix} \right)_{\mu =\widehat{\mu },k=\widehat{k}}^{-1}$$

$$\Lambda \,\!$$ is the log-likelihood function of the gamma distribution, described in Parameter Estimation and Appendix D

Bounds on Reliability
The reliability of the gamma distribution is:


 * $$\widehat{R}(t;\hat{\mu },\hat{k})=1-{{\Gamma }_{I}}(\widehat{k};{{e}^{\widehat{z}}})$$

where:


 * $$\widehat{z}=\ln (t)-\widehat{\mu }$$

The upper and lower bounds on reliability are:


 * $${{R}_{U}}=\frac{\widehat{R}}{\widehat{R}+(1-\widehat{R})\exp (\tfrac{-{{K}_{\alpha }}\sqrt{Var(\widehat{R})\text{ }}}{\widehat{R}(1-\widehat{R})})}\text{ (upper bound)}$$


 * $${{R}_{L}}=\frac{\widehat{R}}{\widehat{R}+(1-\widehat{R})\exp (\tfrac{{{K}_{\alpha }}\sqrt{Var(\widehat{R})\text{ }}}{\widehat{R}(1-\widehat{R})})}\text{ (lower bound)}$$

where:


 * $$Var(\widehat{R})={{(\frac{\partial R}{\partial \mu })}^{2}}Var(\widehat{\mu })+2(\frac{\partial R}{\partial \mu })(\frac{\partial R}{\partial k})Cov(\widehat{\mu },\widehat{k})+{{(\frac{\partial z}{\partial k})}^{2}}Var(\widehat{k})$$

Bounds on Time
The bounds around time for a given gamma percentile (unreliability) are estimated by first solving the reliability equation with respect to time, as follows:


 * $$\widehat{T}(\widehat{\mu },\widehat{\sigma })=\widehat{\mu }+\widehat{\sigma }z$$

where:


 * $$z=\ln (-\ln (R))$$


 * $$Var(\widehat{T})={{(\frac{\partial T}{\partial \mu })}^{2}}Var(\widehat{\mu })+2(\frac{\partial T}{\partial \mu })(\frac{\partial T}{\partial \sigma })Cov(\widehat{\mu },\widehat{\sigma })+{{(\frac{\partial T}{\partial \sigma })}^{2}}Var(\widehat{\sigma })$$

or:


 * $$Var(\widehat{T})=Var(\widehat{\mu })+2\widehat{z}Cov(\widehat{\mu },\widehat{\sigma })+{{\widehat{z}}^{2}}Var(\widehat{\sigma })$$

The upper and lower bounds are then found by:


 * $$\begin{align}

& {{T}_{U}}= & \hat{T}+{{K}_{\alpha }}\sqrt{Var(\hat{T})}\text{ (Upper bound)} \\ & {{T}_{L}}= & \hat{T}-{{K}_{\alpha }}\sqrt{Var(\hat{T})}\text{ (Lower bound)} \end{align}$$