Biostat 202B - UCLA (Winter 2019)


  • Instructor : Donatello Telesca

  • Lecture: MW 1:00 pm to 2:50 pm- PUB HLT 51-279

  • Donatello Office Hours: M 3:00pm - 4:30pm - Biostatistics Library

  • Teaching Assistant: Zizhao Zhang

  • TA Office Hours: TBD - PUB HLT A1-228


Homework

Assignments are due in class on Wednesday.


Schedule of Lectures

  • (1/07) Convergence Concepts (CB 5.5)

  • (1/09) Convergence Concepts [Continuous transformations and Slutzky] (CB 5.5)

  • (1/14) Error Propagation [Stochastic Taylor expansions and the delta method] (CB 5.5)

  • (1/16) Order Statistics - HW1 Review (CB 5.4)

  • (1/21) Martin Luther King, Jr, holiday

  • (1/23) Principles of Data Reduction (CB 6.2)

  • (1/28) MLE

  • (1/30) MLE

  • (2/04) MLE

  • (2/06) M Estimation

  • (2/11) Midterm

  • (2/13) Exponential Family

  • (2/18) Presidents’ Day holiday

  • (2/20) Hypothesis Testing

  • (2/24) Hypothesis Testing

  • (2/26) Hypothesis Testing

  • (3/04) Interval Estimation

  • (3/06) Bootstrap

  • (3/11) Bayesian

  • (3/13) Bayesian

Syllabus and competencies: Syllabus.pdf


Coursework
8 HW Assignments 20%  
Midterm (02/11) 30%
Final [03/20, PUB HLT 41-235, 11:30AM - 2:30PM] 50%


Reading List

(Recommended Textbooks)

  • 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]

  • GA Young and RL Smith. Essentials of Statistical Inference. Cambridge University Press. [Summary of theoretical results.]


(Journal Articles and other Miscellaneous References)

[MLE History]

  • 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.


Computing
Recommended computing for Biostat 202B will be based on the R programming language.
For more information please visit (http://cran.r-project.org/). A recommended platform for R programming and analysis is RStudio (https://www.rstudio.com/).