Examples 
Example 1. The input data for this example is generated by Monte Carlo method for Kijima model 1 with restoration factor q=0 and Weibull underlying function with shape parameter beta=2.0 and scale parameter eta=1.0. The mean time to repair is 0.02. We obtained the following input data for 5 components with suspension time for each component 4.0: 1.15 1.89 2.39 3.05 4.0 0 0.078 1.92 3.62 4.0 0 1.66 2.39 3.07 3.68 4.0 0 0.351 1.58 2.64 3.08 3.79 4.0 0 1.089 2.39 2.48 3.36 4.0 0
Below you can see the 3 steps of calculation.

Output: 
Example 2. The input data for this example is generated by Monte Carlo method for Kijima model 1 with restoration factor q=1 and LogNormal underlying function with parameter mu=2.0 and parameter sigma=1.0. The mean time to repair is 1.0. We obtained the data for 5 components. The observation of first two components finished at their last failure. Corresponding set of numbers (times) is finished with 1. Last 3 components were suspended at time 60. 13.67 21.08 25.26 32.34 1 1.001 45.96 1 37.27 60.0 0 5.69 10.69 15.33 25.06 27.79 55.11 60.0 0 2.70 7.95 10.35 25.00 54.91 56.91021 60.0 0 Below you can see the 4 steps of calculation. The first 3 steps are similar to steps described above. Step 4 shows result of selected Residual sum of squares method. It took about 10 seconds to calculate this example.

Output: 
Mean time to repair is 1.0
Prediction method is: Residual sum of squares
Kijima Model 1 is used in the calculation
Confidence level is: 90.0%
Var(mu)=0.17098467; Covar(mu, sigma)=0.016717048; Covar(mu, q)=0.059069663;
Var(sigma)=0.007902209; Covar(sigma, q)=0.07389288; Var(q)=4.728893E5