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Course detail

Probability and Statistics III

Course unit code: FSI-SP3
Academic year: 2016/2017
Type of course unit: compulsory
Level of course unit: Master's (2nd cycle)
Year of study: 1
Semester: summer
Number of ECTS credits:
Learning outcomes of the course unit:
Students acquire needed knowledge from important parts of mathematical statistics, which will enable them to evaluate and develop stochastic models of technical phenomena and processes based on these methods and realize them on PC.
Mode of delivery:
90 % face-to-face, 10 % distance learning
Prerequisites:
Rudiments of probability theory and mathematical statistics, linear models.
Co-requisites:
Not applicable.
Recommended optional programme components:
Not applicable.
Course contents (annotation):
This course is concerned with the following topics: theory of estimation, maximum likelihood, method of moments, bayesian methods of estimation, testing statistical hypotheses, nonparametric methods, exponential family of distribution, asymptotic tests.
Recommended or required reading:
Anděl, J. Základy matematické statistiky. Matfyzpress. Praha 2005
Lehmann, E.L., Casella G.: Theory of Point Estimation. New York: Springer. 2003 (EN)
Hogg, V.R., McKean J.W. and Craig A.T. Introduction to Mathematical Statistics. Seventh Edition, 2012. Macmillan Publishing Co., INC. New York. ISBN-13: 978-0321795434
Militký, J.: Statistické techniky v řízení jakosti. Pardubice : TriloByte, 1996.
Planned learning activities and teaching methods:
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes:
Course-unit credit requirements: active participation in seminars, mastering the subject matter, passing both written exams and semester assignment acceptance. Preparing and defending a project. Evaluation according to the number of points from the project: excellent (90 - 100 points), very good (80 - 89 points), good (70 - 79 points), satisfactory (60 - 69 points), sufficient (50 - 59 points), failed (0 - 49 points).
Language of instruction:
Czech
Work placements:
Not applicable.
Course curriculum:
Not applicable.
Aims:
The course objective is to make students majoring in Mathematical Engineering acquainted with methods of estimation theory, asymptotic approach to statistical hypotheses testing and prepare students for independent applications of these methods for statistical analyse of real data
Specification of controlled education, way of implementation and compensation for absences:

Attendance at seminars is controlled and the teacher decides on the compensation for absences.

Type of course unit:

Lecture: 26 hours, optionally
Teacher / Lecturer: doc. RNDr. Libor Žák, Ph.D.
Syllabus: Unbiased and consistent estimates
Regular family of distributions, Rao - Cramér theorem, efficient estimates
Fisher information and Fisher information matrix
Sufficient statistics, Neuman factorization criterion
Rao - Blackwell theorem and its applications
Method of moments, maximum likelihood method
Bayes approach
Testing statistical hypotheses
Principles of nonparametric methods
Exponential family of distribution
Asymptotic tests based on likelihood function
Tests with nuisance parameters, examples
Tests of hypotheses on parameters
seminars in computer labs: 13 hours, compulsory
Teacher / Lecturer: doc. RNDr. Libor Žák, Ph.D.
Syllabus: Survey of probability distributions, graphs of densities in MATLAB
Unbiased and consistent estimates - examples of estimates and verification of their properties
Computation of the lower bound for variance of unbiased estimates
Determination of Fisher information and Fisher information matrix for given distributions
Applications of Neuman factorization criterion
Findings estimates by Rao - Blackwell theorem
Estimator’s determination by method of moments and by maximum likelihood method
Estimator’s determination by Bayes method
Project setting - finding parameters estimates for given distribution - application at least two approaches, verification properties of the estimates and their numerical computation
Verification of exponential family for given distribution
Application of asymptotic tests based on likelihood function
Tests with nuisance parameters, estimates of parameters for Weibull and gamma distribution
Tests of hypotheses on parameters of generalized linear model

The study programmes with the given course