Postal address: Department of Statistics, 1 South Parks Road, Oxford OX1 3TG, UK
Office: 50.25 Peter Medawar Building, South Parks Road, Oxford, OX1 3SY
Lecture Notes (let me know if you spot typos or incomprehensible passages, by sending me an email)
Lecture 4, page 13, line -4: remove the two minus signs in the exponents, γ is negative here.
Lecture 4, page 14, line 7: S should be Z
Lecture 5, page 18, line 14: The subscript of the first Δ should be s+r, not s+t. In the lines above, various weak inequalities should be strict and vice versa.
Lecture 6, page 22, line -9: T≤s should be T>s, then take complements of both sides.
Lecture 6, page 23, line 9: Add that we assume the condition that the integral of min(1,x)ν(dx) is finite.
Lecture 6, page 24, line 4: the reference should be to Assignment A.3.1.(c)
Lecture 6, page 24, line 6: add a square in the first expectation
Lecture 8, page 31, line -5: change the minus sign into a plus sign
Lecture 9, page 37, Example 60 is false: we need h(x)=γexp(-γx) as dominating pdf for some γ<β
Lecture 9, page 38, line -9: the vector should be a vector of increments, so there should not be a difference of times in the subscript of X, rather the increment of X between the two times
Lecture 9, page 39, line 12: the Zs should be Xs.
Lecture 11, page 52, line -5: the reference to Proposition 3 should be to Proposition 36
These are lecture notes, and lectures are lectures. The notes are here mainly to avoid that you miss important oral explanations while copying from the board. I will also use some transparencies (copied straight from the lecture notes), where appropriate, so you will not be able to copy everything during the lectures, but you will be able to find in the lecture notes everything that you see written in lectures but can't copy, except for the coloured highlighting of important passages. Please attend lectures, to understand and annotate the previously printed lecture notes, but also to pick up the further pictures, examples and explanations, selection and special emphasis, that I offer in lectures.
This course does not contain any (examinable) R programming, but (examinable) simulation techniques and other results in the course are illustrated using R output. Here is some R code for lecture 7
Class teaching material
Infosheet contains literature review and other course information