# Statisticelle: Girl Meets Stats

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## Pages
- [About](https://statisticelle.com/about/)

## Posts
- [Comprehending complex designs: Cluster randomized trials](https://statisticelle.com/comprehending-complex-designs-cluster-randomized-trials/)
- [EM Algorithm Essentials: Estimating standard errors using the empirical information matrix](https://statisticelle.com/em-algorithm-essentials-estimating-standard-errors-using-the-empirical-information-matrix/): Unlike alternative maximization techniques, the EM algorithm does not require the gradient or Hessian\. So, how do we obtain standard errors for resulting point estimates? This blog post demonstrates how the first derivatives of the EM objective function can be used to approximate the Hessian and construct empirical standard errors\. Cool\!\!\! Results are comparable to SEs obtained using numerical differentiation\. 
- [EM Algorithm Essentials: Maximizing objective functions using R's optim](https://statisticelle.com/em-algorithm-essentials-maximizing-objective-functions-using-rs-optim/): Motivated by a two\-component Gaussian mixture, this blog post demonstrates how to maximize objective functions using R’s optim function\. Unconstrained maximization using BFGS and constrained maximization using L\-BFGS\-B is demonstrated\.
- [Embracing the EM algorithm: One continuous response](https://statisticelle.com/embracing-the-em-algorithm-one-continuous-response/)
- [Nonparametric neighbours: U\-statistic structural components and jackknife pseudo\-observations for the AUC](https://statisticelle.com/nonparametric-neighbours-u-statistic-structural-components-and-jackknife-pseudo-observations-for-the-auc/)

## Categories
- [Theory](https://statisticelle.com/category/theory/)
- [Programming](https://statisticelle.com/category/programming/)
- [Nonparametric Statistics](https://statisticelle.com/category/nonparametric-statistics/)
- [Epidemiology](https://statisticelle.com/category/epidemiology/)
- [Machine Learning](https://statisticelle.com/category/machine-learning/)

## Tags
- [Estimation](https://statisticelle.com/tag/estimation/)
- [R](https://statisticelle.com/tag/r/)
- [Statistics](https://statisticelle.com/tag/statistics/)
- [Mathematical Statistics](https://statisticelle.com/tag/mathematical-statistics/)
- [Distribution](https://statisticelle.com/tag/distribution/)

## Optional
- [Sitemap index](https://statisticelle.com/sitemap_index.xml)