Thesis (Selection of subject)Thesis (Selection of subject)(version: 390)
Thesis details
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Řídké regresní modely
Thesis title in Czech: Řídké regresní modely
Thesis title in English: Sparse regression model
Key words: regresný model|regularizace|odhadování s penalizaci|řídke odhady
English key words: regression model|regularization|penalized estimation|sparse estimates
Academic year of topic announcement: 2022/2023
Thesis type: diploma thesis
Thesis language: čeština
Department: Department of Probability and Mathematical Statistics (32-KPMS)
Supervisor: doc. RNDr. Matúš Maciak, Ph.D.
Author: hidden - assigned and confirmed by the Study Dept.
Date of registration: 26.10.2022
Date of assignment: 26.10.2022
Confirmed by Study dept. on: 23.01.2023
Date and time of defence: 10.06.2024 08:30
Date of electronic submission:02.05.2024
Date of submission of printed version:02.05.2024
Date of proceeded defence: 10.06.2024
Opponents: prof. RNDr. Ivan Mizera, CSc.
 
 
 
Guidelines
Cieľom práce je popísať niektoré jednoduché regresné modely postavené a odhadované na princípe riedkých parametrov (resp. odhadov)
a regularizovanej minimalizácie vhodnej objektivnej funkcie (t.j., odhadovanie s penalizačným členom).
Autor/autorka sa oboznámi so základnou problematikou, teoretickými vlastnosťami aj praktickou implementáciou niektorých základných
postupov.
References
[1] Fan, J., Lv, J. and Qi, L. (2011). "Sparse high-dimensional models in economics". Annual Review of Economics 3:291–317.

[2] Harchaoui, Z. and Lévy-Leduc, C. (2010). "Multiple Change-Point Estimation With a Total Variation Penalty". Journal of the American Statistical Association, 105(492):1480-1493.

[2] Krämer, N., Schäfer, J., and Boulesteix, A.L. (2009). "Regularized estimation of large-scale gene association networks using graphical Gaussian models". BMC Bioinformatics , 10(2009): 1--24.

[3] Tibshirani, R. (1996). "Regression Shrinkage and Selection Via the Lasso". Journal of the Royal Statistical Society: Series B (Methodological), 58:267--288.
Preliminary scope of work in English
The author of the thesis will discuss some basic regression models based on sparse parameter estimates and regularized estimation (minimization with a penalty term).
Some basic statistical properties of the sparse estimates will be discussed and derived. Empirical comparisons will be provided as well.
 
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