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Dynamic model for estimation of radon concentration in buildings
Vaňková, Barbora ; Brabec, Marek (advisor) ; Komárek, Arnošt (referee)
Title: Dynamic model for estimation of radon concentration in buildings Author: Barbora Vaňková Department: Department of probability and mathematical statistics Supervisor: Ing. Marek Brabec, Ph.D. Supervisor's e-mail address: mbrabec@cs.cas.cz Abstract: In the present work there is described the method for estimation of functi- onal data from discrete values and basic methods of functional data analysis. 1
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Stochastic modellinf of epidemics
Vencálek, Ondřej ; Brabec, Marek (referee) ; Antoch, Jaromír (advisor)
Nazev prace: Stochasticke modelovani epidemii Autor: Oridfej Venealek Katedra: Katedra pravdepodobnosti a matenmticke sta.t.i.stiky Vedouci diplomove prace: Prof. RNDr. .Jaromi'r Antoch. CSe. e-mail vedoucfho: Jaromir.Antoch'3mff.cuni.c-/ Abstrakt: Tato diplomova prace se zabyva modelovanmi prevalence chfipky v Ceske repnb- lice v letech 2001 az 2003. Vychazi z dat laskave ZHpujccnych Statnhn zdravotm'm ustavem. Z hlediska epiclemiologie jde o observaeni deskriptivuf studii: zabyva se rozdelenhn poctu riernocnych chfipko\ v prnbehu sledovaneho ubdobi v ceske populaci. Matematiekym podkla- dem pro modelovaih je nelinearni hierarehicky model. Funkem tvar zavislo.sti prevalence na case vyehazt z teorie rustovyeh kfivek. Klicova slova: prevalence, sledovane obdobi, riistove kfivky, hierarehicky model Title: Stochastic- Modelling of Rpidemy Autor: (Jndrej Vencalek Department: Department of Probability and Mathematical Statistics Supervisor: Prof. RNDr. Jaronn'r Antoch, CSc. Supervisor's e-mail address: Jaromir.Antochl'0.|inff.ei.mi.cz Abstract: This diploma tbesis deals with modelling of prevalence of influenza in the C/ech Republic in the period from 2001 till 2003. It is based on data which were khidly lent by Statin' zdravotni nstav. This work is observational descriptive study from the view of epidemiology:...
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Clustering and regression analysis of micro panel data
Sobíšek, Lukáš ; Pecáková, Iva (advisor) ; Komárek, Arnošt (referee) ; Brabec, Marek (referee)
The main purpose of panel studies is to analyze changes in values of studied variables over time. In micro panel research, a large number of elements are periodically observed within the relatively short time period of just a few years. Moreover, the number of repeated measurements is small. This dissertation deals with contemporary approaches to the regression and the clustering analysis of micro panel data. One of the approaches to the micro panel analysis is to use multivariate statistical models originally designed for crosssectional data and modify them in order to take into account the within-subject correlation. The thesis summarizes available tools for the regression analysis of micro panel data. The known and currently used linear mixed effects models for a normally distributed dependent variable are recapitulated. Besides that, new approaches for analysis of a response variable with other than normal distribution are presented. These approaches include the generalized marginal linear model, the generalized linear mixed effects model and the Bayesian modelling approach. In addition to describing the aforementioned models, the paper also includes a brief overview of their implementation in the R software. The difficulty with the regression models adjusted for micro panel data is the ambiguity of their parameters estimation. This thesis proposes a way to improve the estimations through the cluster analysis. For this reason, the thesis also contains a description of methods of the cluster analysis of micro panel data. Because supply of the methods is limited, the main goal of this paper is to devise its own two-step approach for clustering micro panel data. In the first step, the panel data are transformed into a static form using a set of proposed characteristics of dynamics. These characteristics represent different features of time course of the observed variables. In the second step, the elements are clustered by conventional spatial clustering techniques (agglomerative clustering and the C-means partitioning). The clustering is based on a dissimilarity matrix of the values of clustering variables calculated in the first step. Another goal of this paper is to find out whether the suggested procedure leads to an improvement in quality of the regression models for this type of data. By means of a simulation study, the procedure drafted herein is compared to the procedure applied in the kml package of the R software, as well as to the clustering characteristics proposed by Urso (2004). The simulation study demonstrated better results of the proposed combination of clustering variables as compared to the other combinations currently used. A corresponding script written in the R-language represents another benefit of this paper. It is available on the attached CD and it can be used for analyses of readers own micro panel data.
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Popis modelu TDD, verze 3.5
Konár, Ondřej ; Brabec, Marek ; Kasanický, Ivan ; Malý, Marek ; Pelikán, Emil
Zpráva obsahuje popis tvorby a použití modelu TDD pro odhad spotřeby zemního plynu zákazníků s měřením typu C. Součástí zprávy je metodika použití modelu TDD operátorem trhu, dále metodika použití TDD provozovatelem distribuční soustavy (PDS), popis aktualizace modelu TDD a popis předávaných souborů s parametry. Model je otestován na reálných datech ze zákaznického kmene distribuèní spoleènosti RWE GasNet a na datech z mimořádných průběhových měření. Dokument zahrnuje stav ke dni 15.10.2014.
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