Office: ?

email: mevans@utstat.utoronto.ca

- I will list some possible projects here although you are also free to pick something else. Consult with me, however, before making a final selection.
The idea is to see how far you can get in implementing at least some aspects of the approach to statistical reasoning being presented in the course in such a context.
I will continue to add to this list as the course progresses.
- One of Cox's Challenge Problems as listed in Fraser, Reid and Lin (2018) When should modes of inference disagree? Some simple but challenging examples. Ann. Applied Stat., 12, 2, 750-770. Cox created a list of 8 problems that pose challenges for various approaches to inference. The problems are listed and discussed in this paper and you could pick one of these.
- Estimate the mean (or variance) when sampling from a suitably conditioned normal distrbution with unkown mean and variance when the measurements are always positive.
- Inferences for a proportion when the proportion is constrained to a subinterval of [0,1].
- Bias calculations, model checking and checking for prior-data conflict for negative-binomial.
- BFF 6.5 -- Virtual Workshop on Bayesian, Fiducial, and Frequentist Statistical Inference, Friday Feb. 4

- Lecture 1

Read Chapter 1 in the book. - Lecture 2

Read 2.1, 2.2, 2.3.2, 2.3.3, 2.3.5, 2.3.6, 2.4 in the book. - Lecture 3

Read Chapter 3. This material will be covered over the next 2 weeks. - Lecture 4

Read Chapter 4. This material will be covered over the next 2 weeks. - Lecture 5

- Lecture 6

- `
- Fieller's Problem (updated April 9 - fixed a problem on p. 9 concerning computing the bias in favor for estimation here.)
- Formula for prior (and posterior of psi). (updated March 16)

- Generating from conditional prior of nu given psi. (updated April 9, fixed the problem with G0 and changed G1 a bit so the algorithm no longer divides by z0 which causes things to fail when z0=0 - thanks to Miaoshiqi and Siyue for pointing this out)

The book is available electronically through the University of Toronto library.

- The choice of the model for the problem together with the specification of the quantity of interest.
- An elicitation procedure for the prior.
- Specification of data collection aspects for the measuremant and control of bias due to the choices made for 1 and 2.
- Model checking procedure based on the observed data.
- Checking for prior-data conflict based on the observed data and how to choose a less informative prior when conflict observed.
- Inferences (estimation and hypothesis assessment) for quantities of interest based on a measure of evidence.