Confidence Intervals on Test Science Research Document Library https://research.testscience.org/keywords/confidence-intervals/ Recent content in Confidence Intervals on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Mon, 01 Jan 2024 00:00:00 +0000 A Preview of Functional Data Analysis for Modeling and Simulation Validation https://research.testscience.org/post/2024-a-preview-of-functional-data-analysis-for-modeling-and-simulation-validation/ Mon, 01 Jan 2024 00:00:00 +0000 https://research.testscience.org/post/2024-a-preview-of-functional-data-analysis-for-modeling-and-simulation-validation/ Modeling and simulation (M&S) validation for operational testing often involves comparing live data with simulation outputs. Statistical methods known as functional data analysis (FDA) provides techniques for analyzing large data sets (“large” meaning that a single trial has a lot of information associated with it), such as radar tracks. We preview how FDA methods could assist M&S validation by providing statistical tools handling these large data sets. This may facilitate analyses that make use of more of the data available and thus allows for better detection of differences between M&S predictions and live test results.

Modeling and simulation (M&S) validation for operational testing often involves comparing live data with simulation outputs. Statistical methods known as functional data analysis (FDA) provides techniques for analyzing large data sets (“large” meaning that a single trial has a lot of information associated with it), such as radar tracks. We preview how FDA methods could assist M&S validation by providing statistical tools handling these large data sets. This may facilitate analyses that make use of more of the data available and thus allows for better detection of differences between M&S predictions and live test results. We demonstrate some fundamental FDA approaches with a notional example of live and simulated radar tracks of a bomber’s flight

Suggested Citation

Medlin, Rebecca M, and Curtis G Miller. A Preview of Functional Data Analysis for Modeling and Simulation Validation. IDA Product ID 3001829. Alexandria, VA: Institute for Defense Analyses, 2024.

Slides:

]]> Parametric Reliability Models Tutorial https://research.testscience.org/post/2018-parametric-reliability-models-tutorial/ Mon, 01 Jan 2018 00:00:00 +0000 https://research.testscience.org/post/2018-parametric-reliability-models-tutorial/ This tutorial demonstrates how to plot reliability functions parametrically in R using the output from any reliability modeling software. It provides code and sample plots of reliability and failure rate functions with confidence intervals for three different skewed probability distributions the exponential, the two-parameter Weibull, and the lognormal. These three distributions are the most common parametric models for reliability or survival analysis. This paper also provides mathematical background for the models and recommendations for when to use them. This tutorial demonstrates how to plot reliability functions parametrically in R using the output from any reliability modeling software. It provides code and sample plots of reliability and failure rate functions with confidence intervals for three different skewed probability distributions the exponential, the two-parameter Weibull, and the lognormal. These three distributions are the most common parametric models for reliability or survival analysis. This paper also provides mathematical background for the models and recommendations for when to use them.

Suggested Citation

Pinelis, Yevgeniya K, and William R Whitledge. “Tutorial: Parametric Reliability Models.” Institute for Defense Analyses IDA Non-Standard Document NS D-9171 (September 2018).

Paper:

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