Least Absolute Shrinkage and Selection Operator Method
| Název práce v češtině: | Regresní metoda lasso |
|---|---|
| Název v anglickém jazyce: | Least Absolute Shrinkage and Selection Operator Method |
| Akademický rok vypsání: | 2016/2017 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Institut ekonomických studií (23-IES) |
| Vedoucí / školitel: | RNDr. Michal Červinka, Ph.D. |
| Řešitel: | skrytý - zadáno vedoucím/školitelem |
| Datum přihlášení: | 09.11.2016 |
| Datum zadání: | 09.11.2016 |
| Datum a čas obhajoby: | 14.06.2017 09:00 |
| Místo konání obhajoby: | Opletalova - Opletalova 26, O105, Opletalova - místn. č. 105 |
| Datum odevzdání elektronické podoby: | 17.05.2017 |
| Datum proběhlé obhajoby: | 14.06.2017 |
| Oponenti: | PhDr. Marek Rusnák, Ph.D. |
| Kontrola URKUND: | |
| Zásady pro vypracování |
| The lasso (Least Absolute Shrinkage and Selection Operator) [1] is a method used to estimate important variables in models which work with high dimensional data. The lasso is a penalized regression technique which uses l1-norm (absolute value) penalization and it is based on minimization of the least-squares objective function which includes l1-penalty term. This technique performs both regularization and variable selection. We introduce related penalization techniques, namely, the so-called best subset selection method, ridge regression method and elastic net method.
The main goal of this bachelor thesis is to illuminate application of the lasso method when analyzing real economic data. We will employ the R software for numerical experiments. We shall compare the lasso estimator with several other types of estimators based on minimization of mean squared error. |
| Seznam odborné literatury |
| [1] Tibshirani, R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Vol.58, No.1, 267-288, 1996.
[2] Jacob, L., Obozinski, G., and Vert, J.P. Group lasso with overlap and graph lasso. Proceeding ICML ’09 Proceedings of the 26th Annual International Conference on Machine Learning, 2009. [3] Buehlmann, P., Geer, S. Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Berlin Heidelberg, 2011. [4] Hastie, T., Tibshirani, R., Wainwright, M. Statistical Learning with Sparsity: The Lasso and Generalizations. Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 2015. [5] Belloni, A., Chernoyhukov, V., Hansen, Ch. High-Dimensional Methods and Inference on Structural and Treatment Effects. Journal of Economic perspectives, vol.28, no.2, 2014. [6] Zou, H. The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association, Volume 101, Issue 476, 2006. |
| Předběžná náplň práce |
| Outline:
1. Introduction 2. Lasso regression method 3. Modifications of lasso and theoretical comparison 4. Data analysis and numerical comparison 5. Conclusion |
| Předběžná náplň práce v anglickém jazyce |
| Outline:
1. Introduction 2. Lasso regression method 3. Modifications of lasso and theoretical comparison 4. Data analysis and numerical comparison 5. Conclusion |