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Techniques
Cumulative Distribution Function
Cumulative Hazard Function
Weibull Distribution
Normal Distribution
Lognormal Distribution
Exponential Distribution
Exam
Engineered Software Home Page
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There are many methods available for parameter
estimation. In reliability engineering, the most popular methods are:
- Maximum likelihood
- Hazard plotting
- Probability plotting
- Moment estimation
It is desirable for a parameter estimator to have the following
properties:
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Lack of bias—if the expected value of the estimator is equal to
the true value of the parameter, it is said to be unbiased.
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Minimum variance—the smaller the variance of the estimate, the
smaller the sample size required to obtain the level of accuracy desired, and the more
efficient the estimator. The most efficient estimator is the estimator with minimum
variance.
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Consistency—as the sample size is increased, the value of the
estimated parameter becomes closer to the true value of the parameter.
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Sufficiency—the estimator uses all information available in the
data set.
Parameter estimation techniques are tedious, and computers are often employed to aid in
data analysis. This section provides a detailed explanation for each parameter estimation
technique as well as a demonstration using The Reliability & Maintenance Analyst software
package.
  
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