Course plan

Guest lecturers

John Pålsson, Analyst at If Insurance (If P&C Insurance)
Lovisa Julin and Ingrid Torstensson, Trainees at If Insurance
Mattias Villani Links to an external site., Professor at the Dep. of Statistics, Stockholm University

Course literature and supplementary reading

D. Montgomery, E. Peck, G. Vining: Introduction to Linear Regression Analysis. Wiley-Interscience, (6th Edition (2021). ISBN-10: 978-1-119-57872-7. 704 pages or 5th Edition (2012). ISBN-10: 978-1-118-62736-5. 645 pages). Acronym below: MPV.

The textbook MPV can be bought at THS Kårbokhandel, Drottning Kristinas väg 15-19. The book also has a solutions manual.

There are a number of other books that cover the topics of the course, and which we will use during the course. Here are some recommendations, which are all available freely online:

G. James, D. Witten, T. Hastie, R. Tibshirani: An introduction to Statistical Learning Links to an external site. by the publisher Springer.
A. J. Izenman: Modern Multivariate Statistical Techniques. Regression, Classification, and Manifold Learning Links to an external site. by the publisher Springer. Acronym below: Iz.
T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning Links to an external site.. Springer, 2ed Edition, 2017. Acronym below: HTF.
T. Hastie, R. Tibshirani, M Wainwright: Statistical Learning with Sparsity: The Lasso and Generalizations Links to an external site. by the publisher Chapman and Hall Books, 2016. Acronym below: HTW.
Practical regression with R Links to an external site. by Julian R. Faraway (2002) with R-code. Links to an external site.

Preliminary plan of lectures and exercises sessions

Lecturers: Timo Koski, Isaac Ren (IR). Guest lecturers from If P&C Insurance: Guest(If). Guest lecturer from Stockholm University: Mattias Villani (MV).
Problems to be solved during the exercise sessions and recommended exercises to be solved on your own are found here.

Day	Date	Time	Hall	Topic	Lecturer
1. Thu	20/1	15-17	Zoom	Lecture 1: Introduction (the course work and computer projects). Introduction of If P&C Insurance. Introduction to regression modeling. Simple linear regression: model fitting and inference. Parametric vs non-parametric regression. Read: Chapter 2 in MPV.	MF
2. Fri	21/1	15-17	Zoom	Lecture 2: Simple linear regression: inference and prediction. Read: Chapter 2 in MPV.	MF
3. Mon	24/1	8-10	Zoom	Exercise 1: Simple regression. Problem-solving at the board and applications with R.	IR
4. Tue	25/1	8-10	Zoom	Lecture 3: Multiple linear regression: matrix notations, the regression function and Least Squares (LS), Gauss-Markov theorem. Read: Chapter 3 in MPV and Chapter 5.2 in Iz.	MF
5. Wed	26/1	15-17	Zoom	Lecture 4: Multiple linear regression: inference and prediction. model fitting and properties of the estimates. Read: Chapter 3 in MPV. Project 1 handout.	MF
6. Thu	27/1	8-10 and 10-12	Zoom	Lecture 5: Prediction accuracy and model assessment. Residual analysis. Read: Chapter 4 in MPV	MF
7. Fri	28/1	10-12	Zoom	Exercise 2: Multiple regression. Problem-solving at the board and applications with R.	IR
8. Mon	31/1	15-17	Zoom	Lecture 6: Model assessment (cont. ) Transformations to correct model inadequacies. Read: Chapters 4-5 in MPV.	MF
9. Tue	1/2	8-10	Zoom	Exercise 3: Model adequacy checking, theoretical exercises and applications with R.	IR
10. Wed	2/2	13-15 and 15-17	Zoom	Lecture 7: Methods for detecting influential observations: leverage and measures of influence. Chapter 6 in MPV. Multicollinearity: sources and effects. Read: Chapter 9 in MPV.	MF
11. Fri	4/2	15-17	Zoom	Exercise 4: Diagnostic for leverage, influence, and multicollinearity. Model diagnostics with R. Read: Chapter 6 and 9 in MPV.	IR
12. Tue	8/2	8-10	Zoom	Lecture 8: Methods of dealing with multicollinearity. Methods using derived inputs directions: Principal Component Regression (PCR). Sparse PCA. Read: Chapter 9.5 in MP, Chapter 3.5 in HTF, and Chapter 8.1-8.2 in HTW.	MF
13. Wed	9/2	15-17	Zoom	Lecture 9: Shrinkage methods: ridge regression, the bias-variance trade-off, and estimating the ridge parameter. Resampling techniques for model assessment. Read: Chapter 9.5.3 in MPV, Chapter 3.4 in HTF, and Chapter 5.4 in Iz.	MF
14. Fri	11/2	10-12	Zoom	Lecture 10: Bayesian regression modeling.	MV
15. Tue	15/2	13-15 and 15-17	Zoom	Lecture 11: More about resampling techniques for model assessment. Test and training error. Bootstrapping in regression. Read: Chapter 5.4 in Iz, Chapter 7.4-7.5 in HTF, and Chapter 15.4 in MPV.	MF
16. Wed	16/2	13-15 and 15-17	Zoom	Lecture 12: Variable selection and model building. Sparse modeling in high dimensions: Lasso and model selection property, fitting Lasso models and Least-Angle regression algorithm. Cross-validation and inference with Lasso. Computation of the Lasso solution. A comparison of the selection and shrinkage methods. Read: Chapter 10 in MPV, Chapter 5.8-5.9 in Iz, Chapter 2.1-2.4.2 in HTW, and Chapter 3.6 in HTF.	MF
17. Thu	17/2	15-17	Zoom	Exercise 5: Multicollinearity, ridge and Lasso regression, principal component regression (PCR). Variable selection and model building with R.	IR
18. Fri	18/2	15-17	Zoom	Lecture 13: Models with a binary response. Introduction to logistic regression.	MF
19. Tue	22/2	15-17	Zoom	Lecture 14: Generalized Linear Models (GLM) and exponential families. GLM modeling of binary response variables using logit-link functions.	JP
20. Thu	24/2	10-12	Zoom	Lecture 15: GLM-modeling of Poisson regression. Hypotheses testing and model validation: Likelihood ratio test, Deviance and Wald test. Sparse GLM with Lasso.	JP
21. Fri	25/2	15-17	Zoom	Exercise 6: GLM-modeling with R. Introduction to project 2.	L+I
22. Tue	1/3	15-17	Zoom	Exercise 7: GLM-modeling with R.	L+I
23. Thu	3/3	10-12	Zoom	Lecture 16: Discussion of Project II and an interactive session to test your insurance pricing skills.	JP
24. Fri	4/3	15-17	Zoom	Lecture 17: Kernel regressiom	OA

Fri	14/3	08-13		Exam. Deadline for Project 1 and Project 2.	MF
Fri	8/6	08-13		Re-exam	MF