Statistical Modelling (Part II, Michaelmas 2024)

2024-10-04

General information

  • This course consists of 16 lectures and 8 practical sessions. It complements the Part II Principles of Statistics, but takes a more applied perspective.

  • Time: 12-1 M/W/F

  • Location: MR4.

  • Instructors: Qingyuan Zhao (lecturer) and Louis Christie (practical instructor).

  • Prerequisites: Part IB Statistics.

  • Please email me or leave a comment below if you find any mistakes or have any questions.

  • Lectures will be recorded and the recordings can be found on Moodle.

Lectures notes

Example sheets

Provisional schedule

Date Number Topic
<2024-10-11 Fri> L1 Introduction; review of OLS
<2024-10-14 Mon> P1 Basic R ( sheet; R code)
<2024-10-16 Wed> L2 Nested projection; Exact inference
<2024-10-18 Fri> P2 Computing linear models ( sheet; R code)
<2024-10-21 Mon> L3 Misspecified linear model
<2024-10-23 Wed> L4 Model diagnostics; Bias-variance trade-off
<2024-10-25 Fri> P3 Data frame; Diagnostics; Quartets ( sheet; R code)
<2024-10-28 Mon> L5 Model selection
<2024-10-30 Wed> L6 Box-Cox transformation; regularization
<2024-11-01 Fri> P4 Model selection ( sheet; R code)
<2024-11-04 Mon> L7 Exponential family; Basic properties
<2024-11-06 Wed> L8 Asymptotic inference; Bayes perspective
<2024-11-08 Fri> L9 Hypothesis testing; Deviance;
<2024-11-11 Mon> P5 Exponential family ( sheet; R code)
<2024-11-13 Wed> L10 Canonical GLMs; analysis of deviance
<2024-11-15 Fri> L11 Dispersion parameter; linkage; MLE
<2024-11-18 Mon> L12 Asymptotic inference; iterative algorithms
<2024-11-20 Wed> L13 GLM diagnostics and selection; Binomial GLMs
<2024-11-22 Fri> P6 Binomial GLMs ( sheet; solution)
<2024-11-25 Mon> L14 Poisson GLM
<2024-11-27 Wed> L15 Contingency tables
<2024-11-29 Fri> P7 Binomial and Poisson GLMs ( sheet; solution)
<2024-12-02 Mon> P8 Contingency tables; regularization ( sheet; solution)
<2024-12-04 Wed> L16 Review and look forward

Further readings