Materials & studying resources for Advanced Topics in Reliability course from AFIT that I learned myself
After completing this course, students will:
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Thoroughly understand the basic concepts on which statistical methods for life-data analyses are based, specifically:
- How life-data analysis methods differ from other statistical methods
- The importance of statistical censoring
- Differences between right, left, and interval censoring
- Why all data is interval censored data
- How to use nonparametric methods for fitting models to data
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Be able to implement maximum likelihood (ML) estimation, specifically:
- How to construct likelihood functions for various types of data
- How to fit data to a parametric model by maximizing likelihood functions analytically, graphically, and computationally
- The importance of ML estimation in the frequentist and Bayesian domains
- The purpose and importance of the relative likelihood and profile likelihood functions
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Understand various point and interval estimation techniques, specifically:
- How compute estimates for parameters and functions of parameters from data
- How to compute confidence intervals and credible intervals for parameters and functions of parameters from data
- How to apply the Delta method to compute uncertainty propagation for an arbitrary function of the parameters
- Advantages and disadvantages between various procedures used to compute confidence intervals
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Use software to analyze real-world reliability data and communicate the results of their analyses, specifically: