SB1b/SM2 Computational Statistics (Lectures 1-7) HT18                     


Course details

See the SB1b/SM2 course page for Lectures 8-13 for information about the course, including further details of classes and labs.


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. Last year’s slides prepared by Dr Rogers are also available.


Week 1

L1 Permutation tests and Monte Carlo tests. Lecture, Code.

L2 Two-sample Wilcoxon Rank Sum Test. Location tests. Lecture, (code as L1).

Week 2

L3 Confidence Intervals for location; One-sample Wilcoxon Signed Rank Test.  Lecture, (code as L1).

L4 Smoothing methods. Linear Smoothers and kernel estimation. Lecture, Code, CMB data.

Week 3

L5 Polynomial Smoothers. Boundary error. Bandwidth and cross validation. Lecture

L6 Polynomial. Smoothing Splines and Penalised Regression. Lecture, CV Code and Spline Code

Week 4

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


Problem sheets

SB1b students hand in by Tuesday 12:00 before class.

Week 3 classes: Problem Sheet 1

Week 5 classes: Problem Sheet 2 (data in sheet)


Part B Lab session (for lectures 1-7)

Week 4, Weds 3-4:30pm. Assessed practical, and Solutions to Q1. Due 12 midday Monday Week 8.


MSc Lab session (for lectures 1-7)

Week 3, Friday 11-1. Non-assessed practical on Lectures 1-5. 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