Temat: robust regression - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Interactive evolutionary multiobjective optimization driven by robust ordinal regression
Autorzy:: Branke, J.
Greco, S.
Słowiński, R.
Zielniewicz, P.
Powiązania:: https://bibliotekanauki.pl/articles/200702.pdf
Data publikacji:: 2010
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: evolutionary multiobjective optimization
interactive procedure
robust ordinal regression
Opis:: This paper presents the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), which combines an evolutionary multiobjective optimization with robust ordinal regression within an interactive procedure. In the course of NEMO, the decision maker is asked to express preferences by simply comparing some pairs of solutions in the current population. The whole set of additive value functions compatible with this preference information is used within a properly modified version of the evolutionary multiobjective optimization technique NSGA-II in order to focus the search towards solutions satisfying the preferences of the decision maker. This allows to speed up convergence to the most preferred region of the Pareto-front.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2010, 58, 3; 347-358
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Robust Estimation in VaR Modelling - Univariate Approaches using Bounded Innovation Propagation and Regression Quantiles Methodology
Autorzy:: Ratuszny, Ewa
Powiązania:: https://bibliotekanauki.pl/articles/483341.pdf
Data publikacji:: 2013
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: Robust estimation
quantile regression
CAViaR
ARMA-GARCH models
Opis:: In the paper we present robust estimation methods based on bounded innovation propagation filters and quantile regression, applied to measure Value at Risk. To illustrate advantage connected with the robust methods, we compare VaR forecasts of several group of instruments in the period of high uncertainty on the financial markets with the ones modelled using traditional quasi-likelihood estimation. For comparative purpose we use three groups of tests i.e. based on Bernoulli trial models, on decision making aspect, and on the expected shortfall.
Źródło:: Central European Journal of Economic Modelling and Econometrics; 2013, 5, 1; 35-63
2080-0886
2080-119X
Pojawia się w:: Central European Journal of Economic Modelling and Econometrics
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 3.

Tytuł:: Generalized ordered linear regression with regularization
Autorzy:: Łęski, J.
Henzel, N.
Powiązania:: https://bibliotekanauki.pl/articles/201591.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: linear regression
IRLS
OWA
conjugate gradient optimization
robust methods
Opis:: Linear regression analysis has become a fundamental tool in experimental sciences. We propose a new method for parameter estimation in linear models. The 'Generalized Ordered Linear Regression with Regularization' (GOLRR) uses various loss functions (including the o-insensitive ones), ordered weighted averaging of the residuals, and regularization. The algorithm consists in solving a sequence of weighted quadratic minimization problems where the weights used for the next iteration depend not only on the values but also on the order of the model residuals obtained for the current iteration. Such regression problem may be transformed into the iterative reweighted least squares scenario. The conjugate gradient algorithm is used to minimize the proposed criterion function. Finally, numerical examples are given to demonstrate the validity of the method proposed.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 481-489
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

Artykuł

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