Computational Statistics Analysis (ΣΕΕ12)

Graduate Courses Outlines - Department of Mathematics

General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Graduate
Course Code	ΣΣΕ12
Semester	2
Course Title	Computational Statistics Analysis
Independent Teaching Activities	Lectures-Laboratory (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type	Specialized general knowledge
Prerequisite Courses	-
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English, reading Course)
Course Website (URL)	See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes

Students completing this course should be able to: Use R and other statistical software to implement computational statistics techniques. Be able to generate random numbers from a variety of distributions and asses their quality. Be able to apply the Jacknife, the Bootstrap and other computational statistics techniques under the appropriate settings and assumptions. Plan and implement a statistical simulation study in an efficient way. Interpret the results from a simulation study.

General Competences

Working independently Decision-making Production of free, creative and inductive thinking Criticism and self-criticism

Syllabus

This course covers the following topics and relies on heavy use of R: random number generation techniques. The jacknife, bootstrap and their theoretical properties. Cross validation, kernel density estimation, local regression. Monte Carlo simulation and its applications.

Teaching and Learning Methods - Evaluation

Delivery	Classroom (face-to-face)
Use of Information and Communications Technology	Use of ICT in communication with students
Teaching Methods	Activity Semester Workload Lectures 39 Working independently 78 Exercises-Homework 70.5 Course total 187.5
Student Performance Evaluation	Final written exam in Greek (in case of Erasmus students in English).

Attached Bibliography

• Davison, A. C., Hinkley, D. V., (1997). Bootstrap methods and their application. Cambridge University Press.
• Rizzo, M. L., (2007). Statistical computing with R. Chapman & Hall/CRC.
• Robert, C. P., Casella, G., (2009). Introducing Monte Carlo methods with R. Springer Verlag.
• Gentle, J. E., (2009). Computational Statistics, Springer.
• Givens, G.H. and Hoeting, J.A., (2012). Computational Statistics, Wiley.
• [Περιοδικό / Journal] Statistics and Computing
• [Περιοδικό / Journal] Computational Statistics.
• [Περιοδικό / Journal] Computational Statistics & Data Analysis.