In Auburngrads/teachingApps: Apps for Teaching Statistics, R Programming, and Shiny App Development

Maximum Likelihood (ML) Estimation

• Is a versitile method used to fit statistical models to data

• Can be applied to a wide variety of statistical models and data structures

• Provides a numerical procedure to discern which model best-fits a data set (among a set of selected models)

• Produces efficient and consistent estimators under certain regularity conditions

  • Efficient - Estimates $\mathbf{\underline{\theta}}=\theta_{1},\theta_{2},...$ in the "best-manner"

  • Consistent - $\text{as}\;n\rightarrow\infty, \;\;f(\hat{\theta}{{MLE}}\xrightarrow{L}\theta)$

About this app

• The Silly Example tab

  • Walks through a 'not-so-real-world' example showing how ML Estimation works

• The Details Tab

  • Presents an interactive simulation showing how likelihood values are computed for different distributions

Auburngrads/teachingApps documentation built on June 17, 2020, 4:57 a.m.