Template:Non-parametric LDA confidence bounds

Non-Parametric Confidence Bounds
Confidence bounds for non-parametric reliability estimates can be calculated using a method similar to that of parametric confidence bounds. The difficulty in dealing with nonparametric data lies in the estimation of the variance. To estimate the variance for non-parametric data, Weibull++ uses Greenwood's formula [27]:


 * $$\widehat{Var}(\widehat{R}({{t}_{i}}))={{\left[ \widehat{R}({{t}_{i}}) \right]}^{2}}\cdot \underset{j=1}{\overset{i}{\mathop \sum }}\,\frac{\tfrac}{{{n}_{j}}\cdot \left( 1-\tfrac \right)}$$

where:


 * $$\begin{align}

& m= \text{ the total number of intervals} \\ & n= \text{ the total number of units} \end{align}$$

The variable ni is defined by:


 * $${{n}_{i}}=n-\underset{j=0}{\overset{i-1}{\mathop \sum }}\,{{s}_{j}}-\underset{j=0}{\overset{i-1}{\mathop \sum }}\,{{r}_{j,}}\text{ }i=1,...,m$$

where:


 * $$\begin{align}

& {{r}_{j}}= \text{the number of failures in interval }j \\ & {{s}_{j}}= \text{the number of suspensions in interval }j \end{align}$$

Once the variance has been calculated, the standard error can be determined by taking the square root of the variance:


 * $${{\widehat{se}}_{\widehat{R}}}=\sqrt{\widehat{Var}(\widehat{R}({{t}_{i}}))}$$

This information can then be applied to determine the confidence bounds:


 * $$\left[ LC{{B}_{\widehat{R}}},\text{ }UC{{B}_{\widehat{R}}} \right]=\left[ \frac{\widehat{R}}{\widehat{R}+(1-\widehat{R})\cdot w},\text{ }\frac{\widehat{R}}{\widehat{R}+(1-\widehat{R})/w} \right]$$

where:


 * $$w={{e}^{{{z}_{\alpha }}\cdot \tfrac{\left[ \widehat{R}\cdot (1-\widehat{R}) \right]}}}$$

and α is the desired confidence level for the 1-sided confidence bounds.

Example 4: