**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

**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/).