Computational Statistics (MAE836): Διαφορά μεταξύ των αναθεωρήσεων

Αναθεώρηση της 17:09, 23 Ιουλίου 2022

Undergraduate Courses Outlines - Department of Mathematics

General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Undergraduate
Course Code	ΜΑΕ836
Semester	8
Course Title	Computational Statistics
Independent Teaching Activities	Lectures-Laboratory (Weekly Teaching Hours: 3, Credits: 6)
Course Type	Special Background
Prerequisite Courses	-
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English, reading Course)
Course Website (URL)	-

Learning Outcomes

Learning outcomes	Students completing this course should be able to: Apply the most common methods of computational statistics generate random numbers from discrete and continuous distributions use R and other statistical software to perform statistical analysis use different methods to solve an optimization problem.
General Competences	Working independently Decision-making Production of free, creative and inductive thinking Criticism and self-criticism.

Syllabus

Using R the following topics will be discussed: Generation of random numbers from discrete and continuous distributions. Monte Carlo integration. Using simulation techniques to visualize classical results of statistical inference via simulated data (asymptotic normality of mean, power of a test etc). Density Estimation and Applications (Kernel density estimation). Methods of Resampling ς (Jackknife και Bootstrap). Numerical maximization techniques (Newton-Raphson, Fisher scoring, expectation-maximization [EM]).

Teaching and Learning Methods - Evaluation

Delivery

Classroom (face-to-face)

Use of Information and Communications Technology

-

Teaching Methods

Activity	Semester Workload
Lectures	39
Working independently	78
Exercises-Homeworks	33
Course total	150

Student Performance Evaluation

Final written exam in Greek (in case of Erasmus students in English) which concentrates on the solution of problems which are motivated by the main themes of the course.

Attached Bibliography

See Eudoxus. Additionally:

Davison, A. C., Hinkley, D. V., Bootstrap methods and their application. Cambridge University Press 1997.
Rizzo, M. L., Statistical computing with R. Chapman & Hall/CRC 2007.
Robert, C. P., Casella, G., Introducing Monte Carlo methods with R. Springer Verlag 2009

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 === Attached Bibliography ===
-Books in English:
+See [https://service.eudoxus.gr/public/departments#20 Eudoxus]. Additionally:
 * Davison, A. C., Hinkley, D. V., Bootstrap methods and their application. Cambridge University Press 1997.
 * Rizzo, M. L., Statistical computing with R. Chapman & Hall/CRC 2007.
 * Robert, C. P., Casella, G., Introducing Monte Carlo methods with R. Springer Verlag 2009
-Books in Greek:
-* Φουσκάκης Δ. Ανάλυση Δεδομένων με χρήση της R, Εκδότης Τσότρας.