JEDEC JESD 37
LOGNORMAL ANALYSIS OF UNCENSORED DATA AND OF SINGLY RIGHT-CENSORED DATA UTILIZING THE PERSSON AND ROOTZEN METHOD
|Publication Date:||1 August 2017|
This standard enables the user to estimate the parameters of a two-parameter lognormal distribution from complete or singly right-censored independent data samples. Specifically, this standard is intended for analyzing failure-time (tf) data obtained from a stress test of a sample of units when the natural logarithm of the failure-time (ln tf) follow a normal distribution. This standard is not intended to describe techniques used to determine how well the failure data fits a lognormal distribution. However, if points lie along a straight line for plots generated in section 8 the lognormal distribution estimators will describe the points along the line.
The results of the analysis provide bias-corrected sample estimates for the median time-to-failure (t50), mean of the ln tf values (ln t50), and the standard deviation (σ) of the ln tf value of the lognormal distribution. Additionally, confidence intervals are provided for complete data samples (no censoring). These are all obtained from the failure time values (tf)
Complete data case
This standard may be used to analyze complete data where the failure-time data for the entire sample population is known and used. The analysis uses the most efficient estimators for obtaining estimates of the two primary parameters (t50, σ) of the distribution.
This standard may be used to analyze singly right-censored (Type II censored) data where the test has been stopped before all the parts have failed. The analysis uses the Persson and Rootzen Estimators  corrected for bias [2, 3]. These estimators can be calculated with a hand calculator and are as accurate as more complex estimators.
This standard does not preclude the use of other estimators for determining a lognormal distribution, as long as the method has been shown to provide results similar to those obtained by this standard. For example, Maximum Likelihood Estimators are widely used, have desirable properties and are acceptable when bias is removed. However, these estimators are difficult to obtain without computerized iterative techniques, are subject to similar, if not identical bias and many software tools do not remove this bias.
This standard can also be used to estimate the parameters of a normal distribution from complete and right-censored data samples. Raw data is used without taking the natural logarithm. The mean and standard deviation can be used without modification.