Thesis/Dissertation: Bc. Kateřina Konečná, učo 270073: Nonparametric Conditional Density Estimation
Master's thesis
Nonparametric Conditional Density Estimation
Neparametrické odhady podmíněné hustoty
Abstract
Jádrové odhady podmíněné hustoty patří mezi neparametrické metody, na jejichž kvalitu má zásadní vliv šířka vyhlazovacího parametru. Pro odhad podmíněné hustoty je známo několik metod - v této práci je největší pozornost věnována Nadaraya-Watsonovu odhadu podmíněné hustoty, z dalších metod jsou zmíněny 2-krokové odhady, lokálně-lineární a RNW (re-weighted Nadaraya-Watson) odhady. Dále jsou uvedeny …more
Abstract
Kernel conditional density estimation belongs to nonparametric methods, smoothing parameter called bandwidth plays a principal role of their quality. Several methods are known in conditional density estimation - in this thesis the greatest attention is paid to Nadaraya-Watson estimator, 2-step estimator, local-linear and RNW (re-weighted Nadaraya-Watson) estimators are mentioned too. Formulas assesing …more
Thesis description
11/5/2012 10:33, doc. Mgr. Jan Koláček, Ph.D., UČO 19999
- Entered/Edited 13/6/2012 10:07, Irena Mitášová
- Record made 31/1/2011 11:11, Bc. Romana Němcová
- Accessible from: 11/5/2012 09:13, Irena Mitášová
- Thesis/dissertation received 11/5/2012 09:13, Irena Mitášová
Supervisor
Literature
- WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701.
- ROSENBLATT, M. Conditional probability density and regression estimates. Multivariate Analysis II. 1969, p. 25-31.
- HOLMES, Michael and Alexander GRAY. Fast kernel conditional density estimation: A dual-tree Monte Carlo approach. Computational Statistics & Data Analysis. Amsterdam: Elsevier, 2010, vol. 54, No 7, p. 1707-1718. ISSN 0167-9473.
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