Merge branch 'master' of github.com:maxbiostat/Computational_Statistics

README.md

@@ -72,6 +72,12 @@ SMC finds application in many areas, but dynamic (linear) models deserve a speci
 - The original 1983 [paper](https://www.science.org/doi/10.1126/science.220.4598.671) in Science [open link](http://wexler.free.fr/library/files/kirkpatrick%20(1983)%20optimization%20by%20simulated%20annealing.pdf) by Kirpatrick et al is a great read.
 - [These](https://youtu.be/NPE3zncXA5s) visualisations of the traveling salesman problem might prove useful. 
 
+## Bootstrap
+
+- [Efron (1979)](https://projecteuclid.org/journals/annals-of-statistics/volume-7/issue-1/Bootstrap-Methods-Another-Look-at-the-Jackknife/10.1214/aos/1176344552.full) is a great resource and a seminal paper.
+- A good introductory book is [An introduction to the bootstrap](https://www.routledge.com/An-Introduction-to-the-Bootstrap/Efron-Tibshirani/p/book/9780412042317) by Efron and Tibshirani (1993). [PDF](https://cindy.informatik.uni-bremen.de/cosy/teaching/CM_2011/Eval3/pe_efron_93.pdf).
+- The technical justification of the bootstrap relies on the [Glivenko-Cantelli](https://en.wikipedia.org/wiki/Glivenko%E2%80%93Cantelli_theorem) theorem. The technical proof given in class is taken from [here](http://www.ms.uky.edu/~mai/sta709/NoteGC.pdf).
+
 ## Miscellanea
 
 - In [these](https://terrytao.wordpress.com/2010/01/03/254a-notes-1-concentration-of-measure/) notes, [Terence Tao](https://en.wikipedia.org/wiki/Terence_Tao) gives insights into **concentration of measure**, which is the reason why integrating with respect to a probability measure in high-dimensional spaces is _hard_.