Overview
Topics include generalized linear models; methods for handling incomplete data and censored data; survival analysis and methods for analysis of multilevel (including longitudinal) data. Applications to real world data.
About this paper
| Paper title | Regression Models for Complex Data |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,393.95 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- STAT 401 or (STAT 270 and STAT 310)
- Contact
- Teaching staff
- Textbooks
Recommended reading:
- Diggle, P., Heagerty, P., Liang K.Y., Zeger, S.(2002) Analysis of Longitudinal Data Oxford University Press, Oxford.
- McCullagh and Nelder (1989) Generalized linear models, Chapman and Hall.
- Fiztmaurice, Laird and Ware. Applied longitudinal analysis 2nd edition.
- Kalbfleish and Prentice. The statistical analysis of failure time data. 2nd edition.
- Collett. Modelling survival data in medical research (3rd edition).
- Dirk Moore, Applied Survival analysis using R.
- Dobson and Barnett (2008) An Introduction to generalized linear models, Chapman and Hall (3rd edition).
- Graduate Attributes Emphasised
Communication, Critical Thinking, Interdisciplinary perspective, Lifelong learning, Information Literacy, Research, Self motivation, Scholarship, Teamwork
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will be able to:
- Develop an appropriate statistical model for a research question, selecting from a range of regression models (generalised linear models, models for time to event data and multilevel models)
- Describe the characteristics of each type of regression model, including parameter estimation and interpretation, inference and model assumptions
- Carry out a statistical analysis using an appropriate regression model
- Provide clear and succinct written and oral reports on statistical methods and the results of analyses
- Assessment details
F = max{E, 0.6E + 0.3A + 0.1P}, where E (exam mark), A (assignments mark), and P (project mark) are out of 100.
Overview
Topics include generalized linear models; methods for handling incomplete data and censored data; survival analysis and methods for analysis of multilevel (including longitudinal) data. Applications to real world data.
About this paper
| Paper title | Regression Models for Complex Data |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- STAT 401 or (STAT 270 and STAT 310)
- Contact
- Teaching staff
- Textbooks
Recommended reading:
- Diggle, P., Heagerty, P., Liang, K. Y., & Zeger, S. (2002). Analysis of Longitudinal Data. Oxford University Press.
- McCullagh, P. & Nelder, J. A. (1989) Generalized Linear Models. Chapman and Hall.
- Fiztmaurice, G. M., Laird, N. M., & Ware, J. H. (2011). Applied Longitudinal Analysis (2nd ed.). Wiley.
- Kalbfleish, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
- Collett, D. (2014). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.
- Moore, D. (2016). Applied Survival Analysis Using R. (Springer).
- Dobson, A. J & Barnett, A. G. (2008). An Introduction to Generalized Linear Models (3rd ed.). Chapman and Hall.
- Graduate Attributes Emphasised
Communication, Critical Thinking, Interdisciplinary perspective, Lifelong learning, Information Literacy, Research, Self motivation, Scholarship, Teamwork
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will be able to:
- Develop an appropriate statistical model for a research question, selecting from a range of regression models (generalised linear models, models for time to event data and multilevel models).
- Describe the characteristics of each type of regression model, including parameter estimation and interpretation, inference and model assumptions.
- Carry out a statistical analysis using an appropriate regression model.
- Provide clear and succinct written and oral reports on statistical methods and the results of analyses.