| Begge sider forrige revisjon
Forrige revisjon
Neste revisjon
|
Forrige revisjon
Neste revisjon
Begge sider neste revisjon
|
tma4315:2019h:start [2019-10-17]
jarlet [Lecture summary] |
tma4315:2019h:start [2019-10-22]
jarlet [Lecture summary] |
| ===== Tentative curriculum ===== | ===== Tentative curriculum ===== |
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| Fahrmeir et. al. (2013) [[https://link.springer.com/book/10.1007%2F978-3-642-34333-9|(freely available on springer link)]], ch. 2.1-2.4, B.4, 5.1-5.4, 5.8.2, 6, 7.1-7.3, 7.5, 7.7. This covers ordinary linear and multiple regression (mostly repetition from [[https://wiki.math.ntnu.no/tma4267|Linear statistical models]]), binary regression, Poisson and gamma regression, the exponential family and generalised linear models in general, categorical regression (includes contingency tables and log-linear models, multinomial and ordinal regression), linear mixed effects models, generalized linear mixed effects models. | Fahrmeir et. al. (2013) [[https://link.springer.com/book/10.1007%2F978-3-642-34333-9|(freely available on springer link)]], ch. 2.1-2.4, B.4, 5.1-5.4, 5.8.2, 6, 7.1-7.3, 7.5, 7.7. We will also use some material from [[http://www.maths.bris.ac.uk/~sw15190/core-statistics.pdf|Wood (2015)]], see below. |
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| | This covers ordinary linear and multiple regression (mostly repetition from [[https://wiki.math.ntnu.no/tma4267|Linear statistical models]]), binary regression, Poisson and gamma regression, the exponential family and generalised linear models in general, categorical regression (includes contingency tables and log-linear models, multinomial and ordinal regression), linear mixed effects models, generalized linear mixed effects models. |
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| Also see [[https://www.ntnu.edu/studies/courses/TMA4315#tab=omEmnet|the official ntnu course info]]. | Also see [[https://www.ntnu.edu/studies/courses/TMA4315#tab=omEmnet|the official ntnu course info]]. |
| 18/9: More on divergence of Fisher scoring algorithm. Model selection. | 18/9: More on divergence of Fisher scoring algorithm. Model selection. |
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| 20/9: Theory behind AIC (Wood sec. 4.6). Poisson regression. Fisher scoring vs. Newton Raphosn for poisson regression with non-canonical identity link (see R code for further illustrations). | 20/9: Theory behind AIC (Wood sec. 4.6). Poisson regression. Fisher scoring vs. Newton-Raphson for poisson-regression with non-canonical identity link (see R code for further illustrations). |
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| 25/9: Poisson regression continued. [[https://folk.ntnu.no/jarlet/statmod/exams/2015v/forside-eksamen-en.pdf|Example (Exam in ST2304, 2015, problem 2a-d)]]. Gamma and lognormal regression. Glms in general and IRLS (ch. 5.4, 5.8.2). | 25/9: Poisson regression continued. [[https://folk.ntnu.no/jarlet/statmod/exams/2015v/forside-eksamen-en.pdf|Example (Exam in ST2304, 2015, problem 2a-d)]]. Gamma and lognormal regression. Glms in general and IRLS (ch. 5.4, 5.8.2). |
| 16/10: Ordinal regression models. | 16/10: Ordinal regression models. |
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| 18/10: Mixed models (ch. 7) | 18/10: Introduction to mixed models (ch. 7.1 and 7.2) |
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| <color #c3c3c3>30/10: No lecture. | 23/10: Mixed model continued. ML and REML estimation (7.3). |
| | Bayesian interpretation of the restricted likelihood ([[http://faculty.dbmi.pitt.edu/day/Bioinf2132/previousDocuments/Bioinf2132-documents-2016/2016-11-22/Harville-1974.pdf|Harville 1974]]) and connections between the profile and restricted likelihood. |
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| | 25/10: Mixed models continued. BLUPs of the random effects, hypothesis testing and model selection. |
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| | <color #c3c3c3>30/10: No lecture.</color> |
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| | <color #c3c3c3>1/11: ...Generalized linear mixed models (GLMMs). Laplace approximation. REML for GLMMs.... |
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| | 8/11: |
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| | 10/11: |
| </color> | </color> |
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| ===== Recommended exercises ===== | ===== Recommended exercises ===== |
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