SB1.2/SM2 Computational Statistics (Lectures 1-7) HT20                    


Course details

Further details of classes and labs to follow, but you can find them on the dept pages and Canvas.


Lecture slides (to appear; schedule subject to possible adjustment)

The lectures will cover the material in Sections 1 and 4 of these lecture notes prepared by

Prof Meinshausen. My slides from last year are also available.


Week 1

L1 Permutation tests and Monte Carlo tests. Slides and Code

L2 Two-sample Wilcoxon Rank Sum Test. Location tests. Slides

Week 2

L3 Confidence Intervals for location; One-sample Wilcoxon Signed Rank Test. Slides

L4 Smoothing methods. Linear Smoothers. Slides and Code

Week 3

L5 Local Polynomial Smoothers. Boundary error. Slides

L6 Bandwidth and cross validation. Smoothing Splines and Penalised Regression. Slides and Code

Week 4

L7 Smoothing Splines and Penalised Regression (cont). Multivariate Smoothers. Slides, Code for

Splines and Multivariate smoothing.


Problem sheets

Week 3 classes: Problem Sheet 1

Week 5 classes: Problem Sheet 2


Part B Lab session (for lectures 1-7)

Week 4, Weds 4-5:30pm – Assessed Prac; R-soln Q1&2 + code assessed Q; Data: baseball & beetle


MSc Lab session (for lectures 1-7)

Week 3, Fri 11-1 - Practical; R-solutions; baseball data.


Reading (for Lectures 1-7)

L. Wasserman, “All of Nonparametric Statistics”, Springer, 2005

L. Wasserman, “All of Statistics”, Springer, 2004.

J. D. Gibbons, “Nonparametric Statistical Inference”, Marcel Dekker, 1985

J. D. Gibbons and S. Chakraborti, “Nonparametric Statistical Inference”, CRC Press, 2011

G.H. Givens and J.A. Hoeting, “Computational Statistic”s, 2nd edition, Wiley, 2012.

R. H. Randles and D. A. Wolfe, “Introduction to the Theory of Nonparametric Statistics”, Wiley 1979


Geoff Nicholls