Biostat 202B - UCLA (Winter 2018)
- Instructor : Donatello Telesca
- Lecture: MW 1:00 pm to 2:50 am- PUB HLT 51-279
- Donatello Office Hours: M 3:30pm - 4:30pm - Biostatistics Library
- Teaching Assistant: Bingling Wang
- TA Office Hours: T 3:00PM - 4:00PM - PUB HLT A1-228
- HW 1 (Assignment1.pdf) [Due 1/17] - [Solution to computing problem: .Rmd, .pdf]
- HW 2 (Assignment2.pdf) [Due 1/24]
- HW 3 (Assignment3.pdf)[Due 1/31]
- HW 4 (Assignment4.pdf, Assignment4.tex)[Due 2/07][Information Matrix Estimation (Example)]
- Practice midterm problems [No need to hand in]
- HW 5 (Assignment5.pdf, Assignment5.tex)[Due 2/21]
- HW 6 (Assignment6.pdf, Assignment6.tex)[Due 3/05]
- HW 7 (Assignment7.pdf, Assignment7.tex)[Due 3/12]
Schedule of Lectures
- (1/08) Introduction - Inferential Principles (CB 7.3)
- (1/10) Convergence Concepts (CB 5.5)
- (1/15) No Class - MLK Jr. Holiday
- (1/17) Slutsky + CLT (CB 5.5.3, CB 5.5.4) [HW 1 due]
- (1/22) Delta Method + Non-Parametric estimation of a CDF (Read ahead CB 5.5.4, CB 5.4) [Notes.pdf]
- (1/24) Order Statistics + HW discussion [HW 2 due]
- (1/29) Introduction to Point Estimation - MLE (Read ahead: CB 7.1, 7.2.1, 7.2.2)
- (1/31) MLE Theory - Fisher Information [HW 3 due]
- (2/05) Principles of Numerical Optimization + Examples
- (2/07) Optimality in Frequentist Estimation (CB 6.1, 6.2, 7.32, 7.33) [Notes.pdf][HW 4 due]
- (2/12) Midterm
- (2/14) Optimality in Frequentist Estimation + Exponential Family (Read ahead: CB 7.32, CB 7.33, Aldrich 1997)
- (2/19) No Class - Presidents Day Holiday
- (2/21) Principles of Bayesian Estimation (Read Ahead CB 7.2.3, 9.2.4) [HW 5 due]
- (2/26) Hypothesis Testing and Critical Rationalism (Read Ahead CB 8.1,8.2)
- (2/28) Hypothesis Testing and Decision Theory (Read Ahead CB 8.3))
- (3/05) Hypothesis Testing - LRT - p values
- (3/07) Interval Estimation
- (3/12) Bootstrap Theory
- (3/14) Prediction and Machine Learning
Syllabus and competencies: Syllabus.pdf
8 HW Assignments 20%
Midterm (02/12) 30%
Final (03/23) 50%
- G Casella and RL Berger. Statistical Inference. Second Edition. Duxbury. [Required]
- JB Kadane. Principles of Uncertainty. CRC Press [A good base reference for Bayesian Inference]
- T Ferguson. A Course in Large Sample Theory. Chapman & Hall. [Contained volume on asymptotics]
- AW Van der Vaart. Asymptotic Statistics. [Advanced asymptotics]
- CP Robert. The Bayesian Choice. [Advanced Bayesian Theory]
(Journal Articles and other Miscellaneous References)
- Aldrich J (1997) R.A. Fisher and the Making of Maximum Likelihood 1912-1922. Statistical Science, Vol 12, No 3. pp 162-176.
[Hypothesis Testing and Philosophy of Science]
- Mayo, D. and Spanos, A. (2006). Severe testing as a basic concept in a Neyman-Pearson philosophy of induction. British Journal for the Philosophy of Science, 57, pp 323–357.
- Popper, K. (1959). The Logic of Scientific Discovery. Basic Books, New York.
- Howson, C. and Urbach, P. (2005). Scientific Reasoning: The Bayesian Approach. 3rd ed. Open Court, Chicago, IL.
- Jeffreys, H. (1939). Theory of Probability. 1st ed. Cambridge University Press, Cambridge.
[Critiques of P-values as Measures of Evidence]
- Berger JO and Selke T (1987). Testing a point null hypothesis: The irreconciliability of p values and evidence. JASA, Vol.82, 397, pp 112-122.
- Johnson VE (2013). Revised Standard for Statistical Evidence. PNAS, Vol 110, No.48, pp 19313-19317.
[Critiques of the Neyman Person paradigm and the Fisher-Neyman debate on hypothesis testing]
- Perlman M and Wu L (1999). The emperor's new test. Statistical Science, Vol.14, 4, pp 355-369.