STAT 403 Lectures

Resampling Inference
STAT 403 Winter Quarter 2025

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Course Notes and Lectures

On this page you will find slides for the lectures notes, and occasionally additional notes. In general, each file represents the template of a "chapter", and is numbered with Roman numerals, e.g. Lecture template II. These templates are used during the 20 2-hour lectures in the quarter. During each lecture, I write on the slides extensively. The slides for each lecture, with all the annotations, are posted under the name l-.pdf. For example, in Winter 2020, slides from Lecture template II were used in l3-apr7.pdf, and l4-apr9.pdf.

Lectures and slides templates

Lecture 0 Probability recap. (slides template)
Appendix: the bias of the sample variance

Lecture I. CDF and EDF. (slides template)

Lecture II. Monte Carlo (slides template)

Lecture III. Importance sampling and rejection sampling (slides template)

Lecture IV. Bootstrap (slide templates)

Lecture V Bootstrap for linear and logistic regression(slide templates)
Lecture VIII, STAT 391, Wi25 Linear and logistic regression (course notes)
Lecture VIII, STAT 391, Wi25 Linear and logistic regression (slides)

Lecture VI Bag of little bootstraps, conformal prediction (slides)
This paper and this paper describe BLB.

Lecture VII Missing data and imputation (slides template- TBedited)

Course notes

l1-mar31 What is resampling inference?

l2-apr2 Review of probability

l3-apr4 Convergence or r.v.'s, estimators, their bias, variance

l4-apr7 Sample mean, sample variance as estimators. CDF and EDF

l5-apr11 Properties of EDF. Sampling from inverse F. MC integration.

l6-apr14 MC integration, importance sampling

l7-apr16 Importance sampling, rejection sampling

l8-apr18 Rejection sampling

l9-apr21 Bayesian Optimization

l10-apr23 The bootstrap

l11-apr25 Bootstrap for regression

l12-apr28 Jackknife, Bag of Little Boostraps (BlB)

l13-may2 Conformal prediction: jackknife+

Extra course notes

Lecture "3". MLE, MSE, linear and logistic regression

Lecture "7". Kernel density estimation

Lecture "8". Non-parametric regression

Lecture "9". Handling missing data