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Course Syllabus

2023/2024

Nonparametric Theory and Data Analysis

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Mago-Lego
Delivered by: International laboratory for Applied Network Research
When: 3, 4 module
Open to: students of all HSE University campuses
Language: English
ECTS credits: 6
Contact hours: 40

Course Syllabus

Full Syllabus

Abstract

This course handles various methods of solving popular statistical tasks like probability density estimation, describing the dependence structures via regression models, and providing statistical tests. All methods considered in this course require only few assumptions about the probabilistic properties of the model from which the data were obtained. For instance, they forgo the assumption that the original distribution is normal. In this course, we show the implementation of considered appoaches in statistical software (preferably in the R-language), and demonstrate how these methods can be used for the solution of some real-world problems.
Learning Objectives

Learning Objectives

  • The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to criticize constructively and determine existing issues with applied linear models in published work
  • Have the skill to meaningfully develop an appropriate model for the research question
  • Have the skill to work with statistical software, required to analyze the data.
  • Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
  • Be able to state the problem of the probability density estimation and estimate the distribution function
  • Be able to work with major statistical programs, especially R, so that they can use them and interpret their output
  • Have an understanding of the basic principles of nonparametric methods
  • Know modern extensions to applied statistical analysis
  • Know the basic principles behind working with all types of data for building nonparametric models
  • Know the theoretical foundation of nonparametric analysis
Course Contents

Course Contents

  • Probability density estimation
  • Nonparametric regression
  • Nonparametric tests
  • Bonus lectures
Assessment Elements

Assessment Elements

  • blocking Сumulative mark for the work during the module
  • blocking Final test
Interim Assessment

Interim Assessment

  • 2023/2024 4th module
    0.7 * Final test + 0.3 * Сumulative mark for the work during the module
Bibliography

Bibliography

Recommended Core Bibliography

  • Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning: Data Mining, Inference, and Prediction. – Springer, 2009. – 745 pp.

Recommended Additional Bibliography

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008