Statistical Inference Methods
- Credits: 10
- Ending: Examination
- Range: 16sP
- Semester: summer
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Completion of the course will expand and deepen knowledge of the methodological foundations and applications of parametric and nonparametric Statistical Inference so that students will properly apply them in subsequent scientific work and they will use them appropriately in various areas of economic practice.
In particular, students will acquire the following knowledge and abilities:
− to solve practical tasks of the Statistical Inference using statistical programming packages and Excel spreadsheet processor.
− They will master terminology, established symbols, underlying assumptions of the application in the various fields of economic practice.
− The methodological basis and techniques for application and correct presentation, and interpretation of the results of the inferential techniques in statistical analyses (in Time series analysis, Statistical Modelling for Risk Assessment, etc.).
Students will acquire in particular the following skills:
- to present their results of developed project with the explanation of practical problems of economic practice using diverse available statistical software for statistical inference applications.
- Students will deeper understanding of techniques, their steps and principles of application of the methods based on several methodological bases (moments, quantile, L-moments) in various fields of statistics,
- They will develop the ability to apply inference methods and properly present the results, with emphasis on the correct use of symbols and correct interpretation of solved real economic and social problems in practice.
Students will acquire the following competencies:
− Students will master the methods of inference statistics: from the tasks formulation, as well as obtained methodological assumptions to knowledge of the methods and skills of techniques of application and results of applied research presentation;
− Students will also improve their writing and presentation skills, in particular, they will use appropriate symbols and terminology, proper tasks formulation, correct techniques in software applications and the right interpretation of the results of inference methods in empirical research and economic practice.
Indicative content
This course is designed to deepen knowledge and skills of Statistical inference.
Support literature
1. Boos, D. D. , Stefanski, L. A. (2013). Essential Statistical Inference: Theory and Methods. Springer Science+Business Media New York. Hardcover ISBN978-1-4614-4817-4. eBook ISBN978-1-4614-4818-1. DOI https://doi.org/10.1007/978-1-4614-4818-1.
2. Cox, D.R. (2006). Principles of Statistical Inference, Cambridge University Press, USA. 236pp. ISBN: 0521685672.
3. Gillard, J. (2020). A First Course in Statistical Inference. Springer International Publishing. 164 pp. eBook ISBN: 978-3-030-39561-2. DOI: 10.1007/978-3-030-39561-2.
4. Held, L., Sabanés Bové, D. (2014). Applied Statistical Inference. Springer-Verlag Berlin Heidelberg. eBook ISBN: 978-3-642-37887-4. DOI: 10.1007/978-3-642-37887-4.
5. Hogg, R., Elliot Tanis, E. (2014). Probability and Statistical Inference, Global Edition. Pearson Education Limited. 560pp. ISBN: 1292062355.
6. Jindrová, P., Sipková, Ľ. (2014). Statistical Tools for Modeling Claim Severity. In European Financial Systems 2014 : proceedings of the 11th International Scientific Conference: June 12-13, 2014 Lednice, Czech Republic. - Brno : Masaryk University, 2014. ISBN 978-80-210-7153-7, p. 288-294 online. Dostupné na :
7. Pacáková, V., Sipková, Ľ. (2011). Probability models of claim amounts. In Contemporary problems of transformation process in the Central and East European countries : proceedings of the 17th Ukrainian-Polish-Slovak scientific seminar held on September 22-24, 2010. - Lviv : Lviv Academy of Commerce Publishing House, s. 63-70.
8. Pacáková, V., Sipková, Ľ., Sodomová, E. (2011). Modelling with generalized lambda distributions. In Przestrzenno-czasowe modelowanie i prognozowanie zjawisk gospodarczych. - Kraków : Akademia Ekonomiczna w Krakowie, 2006. ISBN 83-7252-306-1, s. 263-275.
9. Sipková, Ľ., Boháčová, H., Sipko, J. (2011). Quantile models of losses in property insurance. In Studia ubezpieczeniowe : zarządzanie ryzykiem i finansami. - Poznań : Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu. ISSN 1689-7374, s. 297-307.
10. Sipková, Ľ. (2006). Quantile-based perspective on common statistical ideas. In Education of quantitative mathematical-statistical methods at the universities of economics referring to future needs : 13th Slovak-Polish-Ukrainian scientific seminar. - Bratislava : Faculty of Economic Informatics University of Economics in Bratislava. ISBN 978-80-225-2329-5, s. 105-116.
11. Wasserman, L. (2010). All of Statistics: A Concise Course in Statistical Inference. Publisher : Springer, 462 pp. ISBN-13: 978-1441923226. ISBN-10: 1441923225.
Literature will be continuously updated with the latest scientific and professional titles.
Syllabus
Lectures: 1. Introduction to statistical inference methods. Classification into parametric and nonparametric methods of statistical inference. Their use according to the areas of their application in individual scientific areas of statistics. Basic concepts, symbols, random selection, probability distributions of discrete and continuous random variables, their shapes and parameters. 2. Hypothesis tests, the most powerful test, the Neyman-Pearson lemma, likelihood ratio tests, type I and II errors. The selected nonparametric methods – tests of homogeneity, test for independence. The rank tests of central tendency for independent and dependent two and more samples. Methodological foundations based on the likelihood, with a deeper explanation of concepts and procedures for parameter estimation and tests of statistical hypotheses. Sampling characteristics and sample probability distributions. Inductive judgments about the parameters of the distribution of economic random variables - point and interval estimates and tests of hypotheses about the parameters. 3. Modelling tasks in Statistical induction. Inductive conclusions about the shape of the probability distribution of an economic random variable (parametric and nonparametric tests on the shape of the distribution). Four forms of defining continuous probability distributions. Graphical and numerical analysis of the properties of empirical and theoretical distributions on a moment and quantile basis. Basics of mixture-models. 4. Methods of identification, estimation and verification of models of probability distributions of economic variables according to real data. Computationally oriented tasks: Bootstrap. Sampling. Methods of distributional simulation and use of Monte Carlo simulations in practice.
Requirements to complete the course
40 % semester project processed in statistical software or free software environment with project oral presentation during the lecture.
60 % written final exam
Student workload
Total study load (in hours): 260 hours
Distribution of study load
Lectures participation: 16 hours
Preparation for lectures: 34 hours
Elaboration of Semester project: 70 hours
Preparation for final exam: 140 hours
Language whose command is required to complete the course
Slovak
Date of approval: 10.02.2023
Date of the latest change: 03.02.2022