Hazard and density estimates for Nonparametric Distribution Analysis (Right Censoring)

The hazard function provides a measure of the likelihood of failure as a function of how long the unit has survived (the instantaneous failure rate at a particular time, t).

Although the nonparametric hazard function is not dependent on any specific distribution, you can use it to help determine which distribution might be appropriate for modeling the data if you decide to use parametric estimation methods. Select a distribution that has a hazard function that resembles the nonparametric hazard function.

The density estimates describe the distribution of failure times and provide a measure of the likelihood that a product fails at particular times.

Although the nonparametric density function is not dependent on any specific distribution, you can use it to help determine which distribution might be appropriate for modeling the data if you decide to use a parametric estimation methods. Select a distribution that has a density function that resembles the nonparametric density function.

Use the log-rank and Wilcoxon tests to compare the survival curves of two or more data sets. Each test detects different types of differences between the survival curves. Therefore, use both tests to determine whether the survival curves are equal.

The log-rank test compares the actual and expected number of failures between the survival curves at each failure time.

The Wilcoxon test is a log-rank test that is weighted by the number of items that still survive at each point in time. Therefore, the Wilcoxon test weights early failure times more heavily.