Instructor Information

Christine Nickel

Cell Phone: 703-732-6824

Course Information

Course Description

Computational statistics is a branch of mathematical sciences concerned with efficient methods for obtaining numerical solutions to statistically formulated problems. This course will introduce students to a variety of computationally intensive statistical techniques and the role of computation as a tool of discovery. Topics include numerical optimization in statistical inference [expectation-maximization (EM) algorithm, Fisher scoring, etc.], random number generation, Monte Carlo methods, randomization methods, jackknife methods, bootstrap methods, tools for identification of structure in data, estimation of functions (orthogonal polynomials, splines, etc.), and graphical methods. Additional topics may vary. Coursework will include computer assignments.

Prerequisites

Multivariate calculus, familiarity with basic matrix algebra and EN.625.603 Statistical Methods and Data Analysis.

Course Goal

Provide a background in the computationally intensive tools and methodologies relevant to statistical analysis and the visualization of complex data.

Course Objectives

  • Introduce and understand modern computational methods used in statistics.  Included are methods for simulation, estimation and visualization of statistical data.
  • Understand the role of computation as a tool of discovery in data analysis.

  • Be able to appropriately apply computational methodologies to real world statistical problems. 

When This Course is Typically Offered

The course is offered every spring and fall online.

Syllabus

  • Fisher Scoring
  • EM Algorithm
  • Random Number Generation
  • Monte Carlo Methods
  • Jackknife Methods
  • Bootstrap Methods
  • Kernel Estimation
  • Bivariate Smoothing
  • Splines
  • Viewing Data

Student Assessment Criteria

Homework 35%
Project 15%
Midterm Exam 20%
Final Exam 20%
Discussion 10%

Computer and Technical Requirements

This course is on computational methods and many of the assignments will require the use of a computer.  An introduction to the statistical programming language R will be presented as part of the course and students will be required to complete their assignments in R.

Participation Expectations

Homework will be assigned throughout the semester.  No late homework will be accepeted without prior permission from the instructor. Additionally, students will be expected to participate in discussion boards throughout the semeseter, but will not be required to contribute weekly.

Textbooks

Textbook information for this course is available online through the MBS Direct Virtual Bookstore.

Course Notes

There are notes for this course.

Term Specific Course Website

http://blackboard.jhu.edu

(Last Modified: 12/16/2019 03:29:33 PM)