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Statistical Classification by means of generalized linear models
Sladká, Vladimíra ; Mrázková, Eva (referee) ; Michálek, Jaroslav (advisor)
The goal of this thesis is introduce the theory of generalized linear models, namely probit and logit model. This models are especially used for medical data processing. In our concrete case these mentioned models are applied to data file obtained in teaching hospital Brno. The aim is statically analyzed immune response of child patients in dependence of twelve selected types of genes and find out which combinations of these genes influence septic state of patients.
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Consumer Behaviour
Babáková, Andrea ; Bartošová, Jitka (advisor) ; Bína, Vladislav (referee)
The thesis focuses on investigating and searching for differences between consumer behavior of Czech and Slovak households depending on chosen socioeconomic factors. For the purpose of this thesis source materials are taken mainly from researches realized by Czech and Slovak Statistical Office, specifically from survey sampling Household Budget Study 2009. Data are analyzed through the use of comprehensive characteristics of location, variability and box diagrams. Generalized linear models are used for modeling dependence of consumer behavior on chosen quantitative and qualitative factors. Household consumer behavior is expressed through economic index - propensity to consume - which is determined by the share of total monthly income and expenditures.
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Modeling development of incurred value of claim
Kantorová, Petra ; Zimmermann, Pavel (advisor) ; Hrevuš, Jan (referee)
This diploma project is focused on the estimation of incurred value of claim and probability of the claim remaining opened (not settled) in the specific stage of the insurance settlement process. The change of incurred value of claim means the change of settlement process stage. Generalized linear model is used for modelling these changes. Classical linear regression model also belongs into this theory, which is its special case, just with stricter premises. Generalized linear model among others allows solving the problem of heteroscedasticity in the unusual way using joint model. This model is applied in the practical part of this piece of work. Logistic regression is the part of the generalized linear model theory, which helps to model the probability of the claim remaining opened in this piece of work. The model outcome is presented in graphic way, especially the graphs containing probability that levels of given claim will occur in certain range.
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Application of generalized linear model for mixture distributions
Pokorný, Pavel ; Malá, Ivana (advisor) ; Pavelka, Roman (referee)
This thesis is intent on using mixtures of probability distributions in generalized linear model. The theoretical part is divided into two parts. In the first chapter a generalized linear model (GLM) is defined as an alternative to the classical linear regression model. The second chapter describes the mixture of probability distributions and estimate of their parameters. At the end of the second chapter, the previous theories are connected into the finite mixture generalized linear model. The last third part is practical and shows concrete examples of these models.
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