STA 2212H: Mathematical Statistics II
January 9 to April 2 2024
Tuesday 10.10 am - 1.00 pm Eastern
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Office Hours
Monday 7.00--8.00 pm (Zoom), andTuesday 4.00--5.00 pm (Hydro 9124)
Teaching Assistant
Junhao ZhuWeek 12 April 2
Week 11 March 26
Week 10 March 19
- Slides
- Slides with scribbles
- Link to Peng Ding's course notes from his Causal Inference course at UC Berkeley
- Link to Hernan and Robins book What If?
Week 9 March 12
- Slides
- Slides with scribbles
- Koenker 2017 A review paper on quantile regression, with many references to earlier papers
Week 8 March 5
- Slides
- Slides with scribbles
- Wikipedia page on diagnostic testing
Week 7 February 27
- Slides
- Slides with scribbles
- BMJ meta-analysis on exercise and depression
Week 6 February 13
- Slides
- Slides with scribbles
- PNAS paper on active learning
- Testing in All of Statistics is in Chapter 10; if you're reading the modules, it's pages 151--175 and 176-200.
Week 5 February 6
- Slides
- Slides with scribbles
- Monsarrat and Vergnes (2018) Effect sizes paper
- Exponential confidence intervals my R code for simulating
- Posterior for length of a normal vector my R code for plotting
Week 4 January 30
- Slides
- Slides with scribbles
- Bayes Rules book by Johnson et al
- Paper on handwriting vs typing in Frontiers of Psychology
Week 3 January 23
- Slides
- Slides (with scribbles)
- Behavior Therapy publication on uncertainty and anxiety
Week 2 January 16
- Slides
- Slides (with scribbles)
- Two papers from Science on personalized medicine: News article and research paper
- Paper from Statistics in Medicine on prediction accuracy (emphasizing choice of hyper-parameters)
Week 1 January 9
- Slides
- Slides with scribbles
- Likelihood cheatsheet
- Project Introduction
- This longer version of the likelihood cheatsheet has the details for proving asymptotic normality
Course Information
Syllabus
Texts
- Required
- Mathematical Statistics by K. Knight; Cambridge University Press
Hard copy available at publisher's website - All of Statistics by L. Wasserman. e-copy here or see course page on Quercus
- Statistical Models by A.C. Davison.
- Mathematical Statistics by K. Knight; Cambridge University Press
- For reference
- Computer Age Statistical Inference by B. Efron and T. Hastie
- Statistical Inference by G. Casella and R.L. Berger
Computing
I will always refer to the R computing package and I highly recommend the RStudio environment. You will need to install both of these on your laptop. I am using Version 4.1.1 of R, and Version 1.4.1717 of Rstudio. You can download R from https://cran.r-project.org/ and the free Desktop Version of Rstudio from https://rstudio.com/products/rstudio/\#rstudio-desktop.I also strongly recommend using R Markdown to prepare your homework, but you can use LateX or Word if you must. For questions involving computing you will need to submit working code. This is easy in R Markdown, but R scripts will also be accepted. Neat homework makes it easier on the grader, and a happy grader is a generous grader.