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

Homework


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


Coursework
8 HW Assignments 20%  
Midterm (02/12) 30%
Final [03/23, PUB HLT 41-235, 8:00AM - 11:00AM] 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]


(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/).