STAT 635: Foundations of Statistics

Instructor:	Meng Li
Email:	meng at rice.edu	Office hours:	TBD or by appointment (DCH 2081)
TA:	TBD
Email:		Office hours:	TBD
Lectures:	Twice a week	Location:	TBD

 

Syllabus

Schedule

Resource

 

Course Description

Statistical inference is the de facto tool in data science to carry out hypothesis testing and draw conclusions under uncertainty. With increasingly diverse stakeholders relying on inference as data- driven solutions, to study, decipher, and articulate its strength and limitation become more important than ever. In this course, we will discuss fundamental issues in statistical inference, partly in response to a range of daunting challenges posed by modern data science such as reproducibility and interpretability at large scales. A particular focus is on delineating the deviation between Bayesian and frequentist paradigms, and the reconciliation between them when possible, from both theoretical and practical standpoints. We will center our discussion around the inference problem at hand and lean towards a pragmatic rather than philosophical approach.

A sample of topics includes the use of p-values vs. Bayes factors, frequentist properties of Bayesian procedures for both parametric and nonparametric models, Bernstein von-Mises phenomena, variable/feature selection, post-selection inference, false discovery control.

Prerequisites

We will assume familiarity with the basics of hypothesis testing, point estimation, and interval estimation in both frequentist and Bayesian domains. Recommended prerequisites include: STAT 532/533 or equivalent courses on classical statistical inference, and STAT525 or equivalent courses on Bayesian inference.

Textbooks

There is no course text. Outside readings will be assigned.

Computing

This course will have an applied emphasis. You will be expected to implement introduced methods to analyze real or simulated data. We do not require specific software or packages for homework, but all demonstrations from lectures use R.

Grade Policies

• Homework and Class participation (50%): Reading materials and problem sets will be assigned regularly over the course of the term. Students may present selected papers.
• Final Project (50%): You will complete a project in the second half of the course.

Late Policy

Students may not request extensions, instead, we have the following policies in case something unexpected arises:

• Your lowest assignment grade will be dropped.
• Late homework will be accepted within five days of the due date with a 10-percentage point deduction per day (including weekends).

Rice Honor Code

In this course, all students will be held to the standards of the Rice Honor Code, a code that you pledged to honor when you matriculated at this institution. If you are unfamiliar with the details of this code and how it is administered, you should consult the Honor System Handbook at http://honor.rice.edu/honor-system-handbook/. This handbook outlines the University’s expectations for the integrity of your academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process.

Disability Support Services

If you have a documented disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with Disability Support Services (Allen Center, Room 111 / / x5841) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.