Statistics (Lectures 1-7) HT18
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)
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
Permutation tests and Monte Carlo tests. Lecture, Code.
Two-sample Wilcoxon Rank Sum Test. Location tests. Lecture,
(code as L1).
Confidence Intervals for location; One-sample Wilcoxon Signed Rank Test. Lecture, (code as L1).
Smoothing methods. Linear Smoothers and kernel estimation. Lecture,
Code, CMB data.
Polynomial Smoothers. Boundary error. Bandwidth and cross validation. Lecture
Polynomial. Smoothing Splines and Penalised Regression. Lecture,
CV Code and Spline Code
Smoothing Splines and Penalised Regression (cont).
Multivariate Smoothers. Lecture, Code
students hand in by Tuesday 12:00 before class.
Week 3 classes: Problem
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
MSc Lab session (for lectures 1-7)
Week 3, Friday 11-1. Non-assessed practical
on Lectures 1-5. Practical; R-solutions; baseball
Reading (for Lectures 1-7)
Wasserman, “All of Nonparametric Statistics”, Springer, 2005
Wasserman, “All of Statistics”, Springer, 2004.
D. Gibbons, “Nonparametric Statistical Inference”, Marcel Dekker, 1985
D. Gibbons and S. Chakraborti, “Nonparametric
Statistical Inference”, CRC Press, 2011
Givens and J.A. Hoeting, “Computational Statistic”s, 2nd edition, Wiley, 2012.
H. Randles and D. A. Wolfe, “Introduction to the
Theory of Nonparametric Statistics”, Wiley 1979