Wiki source code of Hierarchical models, spring 2008
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1 | = Hierarchical models, spring 2008 = | ||
2 | |||
3 | === Lecturer === | ||
4 | |||
5 | [[dos. Mervi Eerola>>doc:mathstatHenkilokunta.Eerola, Mervi]] | ||
6 | |||
7 | === Scope === | ||
8 | |||
9 | 10 op | ||
10 | |||
11 | === Type === | ||
12 | |||
13 | Advanced studies. | ||
14 | |||
15 | === Prerequisites === | ||
16 | |||
17 | Basic course on linear models, preferably also generalized linear models. Basis courses in statistics are sufficient to understand the interpretation and use of the models, but not the theoretical aspects. | ||
18 | |||
19 | === Lectures === | ||
20 | |||
21 | Period IV, first lecture Wed 12.3. The language of the course is English, unless all participants understand Finnish. | ||
22 | Lectures: Wed 14-16, B120, Thu 14-16, B120 | ||
23 | |||
24 | === Course status === | ||
25 | |||
26 | Optional course for undergraduates or graduates in statistics, especially in biometry, psychometry or econometry. | ||
27 | Part of the course is suitable also for researchers applying hierarchical models and needing to understand the | ||
28 | methodological background and interpretation of these models. | ||
29 | |||
30 | === Outline of the course === | ||
31 | |||
32 | The course emphasises the general theory of hierarchical modelling and the role of latent variables in it. Many models, which are | ||
33 | widely used in biometry, psychometry and econometry, can be classified as special cases of the general theory. Examples are | ||
34 | generalized linear mixed models, panel analysis, latent class models, item response models and structural equation models. | ||
35 | Common to all of them is dependence among the obervations, which is modelled with latent variables. | ||
36 | |||
37 | The following themes will be covered: | ||
38 | ~1. Why hierarchical modelling? | ||
39 | 2. Modelling dependent data; Recap of GLM and GLMM; Modelling of latent responses | ||
40 | 3. Classical latent variable models | ||
41 | 4. Theoretical framework for latent variable hierarchical models | ||
42 | 5. Identifiability and model equivalence | ||
43 | 6. Estimation; likelihood inference vs Bayesian inference | ||
44 | 7. Predicting the value of the latent variables | ||
45 | 8. Model comparison and diagnostics | ||
46 | |||
47 | The theory is illustrated by real-life examples analysed by modules of R, or by gllamm, a free additional program for analysing | ||
48 | hierarchical models in Stata. | ||
49 | |||
50 | === Practicals === | ||
51 | |||
52 | The contents and hours of practicals will be decided in the beginning of the course. The [[BSCW>>url:https://kampela.it.helsinki.fi/pub/bscw.cgi/?op=login||shape="rect"]] web environment is used to | ||
53 | distribute practicals and other course material. | ||
54 | |||
55 | === Material === | ||
56 | |||
57 | The course is based on the following books: | ||
58 | |||
59 | Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation | ||
60 | models. Boca Raton, FL: Chapman & Hall/ CRC Press | ||
61 | Demidenko, E. (2004). Mixed models; theory and applications. Hoboken, NJ: Wiley. | ||
62 | Gelman, A. and Hill, J. (2006). Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge University Press. | ||
63 | |||
64 | Additional course material will be provided. |