|
|
Asymptotic properties of the weighted least squares estimate
Gajdošík, Vladislav ; Zvára, Karel (referee) ; Víšek, Jan Ámos (advisor)
This diploma thesis dissertate about consistency and asymptotic representation of the least weighted squares estimator (LWS). In preface we mention reasons for data processing with robust statistical methods and differencies between LWS estimator and other methods (the least squares estimator, the least trimmed squares estimator). In the following sections we show proofs of lemmas about consistency and assymptotic representation of the least weighted squares estimator. Compared to the similar results published before we have concluded ours based on different conditions. Impulse for this thesis were new results about uniform convergence of empirical function mentioned in work from prof. Jan Ámos Víšek - Kolmogorov-Smirnov statistics in multiple regression from year 2006 (see Víšek (2006a)).
|
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
|
Mathematical Models for LGD
Rychnovský, Michal ; Zvára, Karel (referee) ; Charamza, Pavel (advisor)
The aim of the present work is to describe possible models for LGD estimation and to test them on the real data. Besides common linear and logistic regression models we aim to describe the methods using running and censored observations - based on the Cox model and the two-step regression. This work first briefly outlines the principle of the capital requirement according to the Basel II. Then, individual methods are described and finally applied to the real banking data.
|
|
|
Extraction of information from multidimensional data and its visualization
Vansa, Tibor ; Zvára, Karel (referee) ; Ranocha, Pavel (advisor)
The presented thesis aims to describe and compare methods of extraction of information from high dimensional data, with both continuous and discrete variables. It is focused on principal component analysis, factor analysis, multidimensional scaling and correspondence analysis. The thesis includes generalization of multidimensional scaling and correspondence analysis to case with more random variables. Mutual relations of presented methods are studied. Presented methods are applied on real data and their implementation in statistical softwares R and SPSS is desribed.
|
guest ::
RSS feed.