Quality and Reliability Methods - SAS

400 Reliability and Survival Analysis II Chapter 20
Nonlinear Parametric Survival Models
Fitting Simple Survival Distributions
The following examples show how to use maximum likelihood methods to estimate distributions from
time-censored data when there are no effects other than the censor status. The Loss Function Templates
folder has templates with formulas for exponential, extreme value, loglogistic, lognormal, normal, and
one-and two-parameter Weibull loss functions. To use these loss functions, copy your time and censor
values into the Time and censor columns of the loss function template.
To run the model, select Nonlinear and assign the loss column as the Loss variable. Because both the
response model and the censor status are included in the loss function and there are no other effects, you do
not need prediction column (model variable).
Exponential, Weibull and Extreme-Value Loss Function Examples
The Fan.jmp data table in the Reliability sample data folder illustrates the Exponential, Weibull, and
Extreme value loss functions discussed in Nelson (1982). The data are from a study of 70 diesel fans that
accumulated a total of 344,440 hours in service. The fans were placed in service at different times. The
response is failure time of the fans or run time, if censored.
Here are the formulas for the loss functions as they appear in the formula editor.
Exponential
Weibull