National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Transformation Models
Pejřimovský, Pavel ; Hušková, Marie (advisor) ; Antoch, Jaromír (referee)
This thesis deals with a finding ideal transformation which can model data well. We focus on transformations which we know up to a parametr. We need to estimate the parametr of the transformation. The main approach of study transformation is in linear regression and in nonparametric regression. In both cases we focus on estimating the transformation parametr and properties of this estimator such as consistency and asymptotic normality. We show in linear regression that the aprroach of least squares do not work properly. Instead of this we use a generalized moment method which can estimate parametr of transformation and also a regression coefficients. We show also a different solution for our problem in specific transformation called Box-Cox. For this situation we make a simulation study for estimators and standard deviations. The standard deviation are obtained by bootstrap method. In nonparametric regression we use profile likelihood to estimate transformation parametr. We also construct an estimator of density of error terms. In both cases we know the asymptotic distribution.
Comments on the application of analytical transformation models and FEM models in the framework of the SBRA Method
Marek, Pavel ; Guštar, M.
The paper turns attention to the main differences between the individual models and between the approaches to the reliability assessment as well between the results.

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