- Introduction to fitting distributions to data
- Check the quality of your data
- Censored data
- Matching the properties of the variable and distribution
- Parametric distributions well known to fit a type of variable
- When the random variable follows a stochastic process with a well-known model
- Transforming discrete data before performing a parametric distribution fit
- -Fitting a distribution for a continuous variable
- Fitting a distribution for a continuous variable
- Fitting a continuous non-parametric first-order distribution to data
- Fitting a continuous non-parametric second-order distribution to data
- Fitting a first order parametric distribution to observed data
- Fitting a second order parametric distribution to observed data
- -Fitting a distribution for a discrete variable
- Fitting a distribution for a discrete variable
- Fitting a discrete non-parametric first-order distribution to data
- Fitting a discrete non-parametric second-order distribution to data
- Fitting a first order parametric discrete distribution to observed data
- -Fitting a second order parametric distribution to observed data
- Example: Determining the joint uncertainty distribution for parameters of a Weibull distribution
- Example: Fitting a second order Normal distribution to data
- Finding the Best Fitting Parameters using Optimisation
- Using optimization to maximize a likelihood calculation to obtain MLEs
- Method of Moments -MoM
- Using Method of Moments with the Bootstrap
- Assessing model fit
- Model fit statistics
- Goodness of Fit Plots
- Critical Values and Confidence Intervals for Goodness-of-Fit Statistics
- The Chi-Squared Goodness-of-Fit Statistic
- Kolmogorov-Smirnoff (K-S) Statistic
- Anderson-Darling (A-D) Statistic
- Maximum Likelihood Estimation - MLE
- Using Goodness-of Fit Statistics to optimise Distribution Fitting
- Information Criteria
Overview
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