Řídké regresní modely
Thesis title in Czech: | Řídké regresní modely |
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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![]() |
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. |