Lectures in STK 3100/4100 autumn 2018

Below are given plans for the coming lectures and an overview of the material that has been covered in the course so far. Section numbers, etc refer to the book by Alan Agresti.

Plans for coming lectures

Week 50 (December Monday 10th 13:15-15:00 Aud 4 V Bjerknes' house):

Additional exercise 28.

Exam 2015: Problem  2.

Overview of past lectures

Week 49 (December 6th):

Exam 2012: Problems 1 and 2.

Additional exercise 27.

Week 48 (November 28th):

Exam 2017: Problems 1, 2, 3 and 4.

Week 47 (November 21st ):

Recapitulation of the main points in the curriculum. (Slides)

Week 46 (November 14th):

Chapter 9: Prediction of random effects for normal linear mixed models  (section 9.3.2). Marginal and generalized linear mixed models (sections 9.1.2 and 9.1.3), binomial and Poisson GLMMs (sections 9.4.1, 9.4.2, and 9.7), ML-estimation for GLMMs (sections 9.5.1 and 9.5.2), marginal models (sections 9.6.3 and 9.6.4). (Slides. R code for prediction of random effects for fecal fat dataR code for prediction of random effects for FEV data. R code for attitude to abortion data.)

Week 45 (November 7th):

Chapter 9: Normal linear mixed models and marginal linear models (sections 9.2, 9.3.1, 9.3.3, and 9.6.1). (Slides. R code for fecal fat data. R code for smoking prevention data. R code for FEV data.)

Week 44 (October 31st):

Chapters 7 and 8: Overdispersion and negative binomial GLMs  (sections 7.3.1, 7.3.2, 7.3.3, and 7.3.4), zero-inflated GLMs (sections 7.4.1, 7.5.1, and 7.5.2), quasi-likelihood methods and variance inflation [sections 8.1.1, 8.1.2, 8.1.3, 8.2.4 (only to middle of page 276), 8.3.1, 8.3.2, 8.3.3, and 8.3.4].  (Slides. R code for crab data. R code for rat data.)

Week 43 (October 24th):

Chapters 6 and 7: More on the baseline-category logit model (sections 6.1.3, and 6.1.4), cumulative logit model (sections 6.2.1, 6.2.2, and 6.3.3), Poisson GLMs (sections 7.1.1, 7.1.3, 7.1.4, and 7.1.6). (Slides 18-23 from last week and these slides. R code for cumulative logit model. R code for Poisson regression for grouped dataR code for Poisson regression for ungrouped data.)

Week 42 (October 15th)

Chapters 5 and 6: Link functions for binomial data (sections 5.6.1, 5.6.3, and 5.7.2), various topics of GLMs for binomial data (sections 5.2.2, 5.2.4, and 5.2.5), baseline-category logit models for multinomial responses (sections 6.1.1, 6.1.2, and 6.3.2). (Sides 1-17 from these slides. R code on link functions for binomial data. R code on prediction and ROC curves. R code on baseline-category logit models for multinomial responses.)

Week 41 (October 10th)

Chapter 4: Fischer scoring and iteratively reweighted least squares (sections 4.5.2, 4.5.4, and 4.5.5), selecting explanatory variables for a GLM (sections 4.6.1, 4.6.2, and 4.6.3), normal linear models and gamma GLMs (sections 4.7.1, 4.7.2, 4.7.3). (Slides 7-12 from the new slides to week 40 and these slides. R code to selection of explanatory variablesR code to normal linear model and gamma GLMs.)

Week 40 (October 4th):

Chapters 4 and 5: Deviance and likelihood ratio testing (sections 4.4.1, 4.4.2, 4.4.3, 5.5.1, and 5.5.2), Pearson chi-square statistics (sections 4.4.4, 5.5.1, and 5.5.2),  deviance and Pearson residuals (section 4.4.6), Newton-Raphson method (section 4.5.1). (Slides 11-20 from week 39, slides 1-6 from these slides, R code).

Week 39 (September 26th):

Chapters 4 and 5: Confidence intervals (sections 4.3.5 and 4.3.6), delta method and fitted values (section 4.2.5), deviance and sum of squares (sections 4.4.1, 4.4.2, and 5.5.1). (Slides 1-17 from these slides. R code.)

Week 38 (September 19th):

Chapters 4 and 5: Likelihood and approximate distributions for GLMs (section 4.2, except 4.2.5 and 4.2.6; section 4.5.5; sections 5.3.1 and 5.3.2). Hypothesis testing (section 4.3, except 4.3.5 and 4.3.6). Interpretation of parameters in logistic regression (section 5.2.1). (Slides. R code.) 

Week 37 (September 12th):

Chapter 3: Hypothesis testing for linear normal models (sections 3.2.2 and 3.2.5). (Slides 15-23 to chapter 3, slides 24-27 will be made into an exercise.)

Chapters 4 and 5: Logistic regression model (section 5.1) and exponential dispersion family for GLMs (section 4.1). (Slides. R code)

Week 36 (September 5th):

Chapter 2: Projections and decompositions of sums of squares for linear models (section 2.3, except 2.3.4), estimation of error variance (section 2.4.1).  (Slides 5-15 from the new slides to week 35.)

Chapter 3: Distribution theory for normal variates (section 3.1, only page 85 in 3.1.4 and except 3.1.5), hypothesis testing for one-way layout (section 3.2.1). (Slides 1-14 from these slides. R code.)

Week 35 (August 29th):

Chapter 2: More on least squares model fitting (sections 2.1.4 and 2.1.5), projections of data onto model spaces (section 2.2, except 2.2.4), projections and decomposition of sums of squares for linear models (section 2.3). (Slides 9-23 from week 34, slide 1-4 of these slides, R code

Week 34 (August 22nd and 23rd):

Chapter 1: Introduction to generalized linear models (section 1.1; except 1.1.6), model matrices and model spaces (section 1.3), identifiability of model parameters (section 1.4.1).  Section 1.2 contains material that should be known from earlier courses, and it will not be discussed at the lectures. But the students should read section 1.2 on their own. (Slides, R code for data examples, R code for illustration of model matrices)

Chapter 2: Least squares model fitting (sections 2.1.1, 2.1.2, and 2.1.3). (Slides; only slides 1-8 were covered by the lectures, the remaining slides will be discussed in week 35). 

Published Aug. 13, 2018 1:39 PM - Last modified Dec. 6, 2018 7:47 PM