Approaches when faced with special conditions in regression, and consequences of ignoring these conditions.
Check out the About page for a description of the course.
2018/2019 Instructor: Vincenzo Coia
2018/2019 TA's: Kateryna Tyshchenko, Sunny Tseng, Julian Ho
By the end of the course, students are expected to:
- Describe the risk and value of making parametric assumptions in regression.
- Fit model functions that represent probabilistic quantities besides the mean.
- Identify situations where standard linear regression is sub-optimal, and apply alternative regression methods that better address the situation.
- Link the bias-variance tradeoff to the fundamental tradeoff of machine learning.
| Deliverable | Weight | Deadline | Submit to... |
|---|---|---|---|
| lab assignment 1 | 15% | February 9, 2019 at 18:00 | github.ubc.ca |
| lab assignment 2 | 15% | February 16, 2019 at 18:00 | github.ubc.ca |
| quiz 1 | 20% | February 25, 2019 from 14:00-14:32 | canvas (write in your lab room) |
| lab assignment 3 | 15% | March 3, 2019 at 15:00 | github.ubc.ca |
| lab assignment 4 | 15% | March 9, 2019 at 18:00 | github.ubc.ca |
| quiz 2 | 20% | March 12, 2019 from 11:00-11:32 | canvas (write in DMP 301) |
Note: Topics covered are conditional on time available.
| Lecture | Date | Topic |
|---|---|---|
| 1 | 2019-02-04 | Model functions in regression |
| 2 | 2019-02-06 | Regression on restricted scales: GLM and transformations |
| 3 | 2019-02-11 | Regression beyond the mean Part I: variance, quantiles |
| 4 | 2019-02-13 | Regression beyond the mean Part II: probabilistic forecasts, robust regression |
| 5 | 2019-02-25 | Regression on censored response data: survival analysis |
| 6 | 2019-02-27 | Regression on ordinal response data: proportional odds model |
| 7 | 2019-03-04 | Regression in many groups: mixed effects models |
| 8 | 2019-03-06 | Regression when data are missing |
If time remains, here are some topics we could cover:
- Regression in between linear and non-parametric: Generalized Additive Models
- Copula Regression
- Non-identifiability
- Heavy-tailed distributions
Readings listed in lectures:
- (Lec 1) About the course
- (Lec 1) Tasks that motivate Regression
- (Lec 1) The Types and Value of Parametric Assumptions
- (Lec 1) The Restricted Range Problem: Transformations
- (Lec 2) Fitting GLM's in R
- (Lec 3) Quantile Regression
- (Lec 4) Probabilistic Forecasting
General Resources:
List under construction
- Julian J. Faraway. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition (Chapman & Hall/CRC Texts in Statistical Science), 2016.
- For survival analysis: David G. Kleinbaum, Mitchel Klein (2012) Survival analysis: a self-learning text, 3rd edition
- Non-technical explanation of survival analysis, with a nice succinct summary along the side of each page.
- Recommends epidemiological background, but we will avoid those parts.
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