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k-sample problem with ordered alternative
Nováková, Martina ; Hlávka, Zdeněk (advisor) ; Pešta, Michal (referee)
In this thesis we deal with k-sample problem with ordered alternative. At the beginning of the thesis isotonic regression is introduced. We use isotonic regression for maximum likelihood estimation of ordered parameters. In the second chapter, we describe the χ2 and E 2 tests that use the knowledge of isotonic regression and are based on the likelihood ratio. The exact null hypothesis distributions of their test statistics are derived in detail. The one-sided studentized range test is also further described. At the end of the thesis, we show the use of the E 2 test on the real data. 1
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Loss reserving for individual claim-by-claim data
Bednárik, Vojtěch ; Pešta, Michal (advisor)
This thesis covers stochastic claims reserving in non-life insurance based on individual claims developments. Summarized theoretical methods are applied on data from Czech Insurers' Bureau created for educational purposes. The problem of estimation is divided into four parts: occurence process generating claims, delay of notification, times between events and payments. Each part is estimated separately based on maximum likelihood theory and final estimates allow us to obtain an estimate of future liabilities distribution. The results are very promising and we believe this method is worth of a further research. Contribution of this work is more rigorous theoretical part and application on data from the Czech market with some new ideas in practical part and simulation. 1
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Non-homogeneous Poisson process - estimation and simulation
Vedyushenko, Anna ; Pešta, Michal (advisor) ; Pawlas, Zbyněk (referee)
This thesis covers non-homogeneous Poisson processes along with estimation of the intensity (rate) function and some selected simulation methods. In Chapter 1 the main properties of a non-homogeneous Poisson process are summarized. The main focus of Chapter 2 is the general maximum likelihood estimation procedure adjusted to a non-homogeneous Poisson process, together with some recommen- dations about calculation of the initial estimates of the intensity function param- eters. In Chapter 3 some general simulation methods as well as the methods designed specially for log linear and log quadratic rate functions are discussed. Chapter 4 contains the application of the described estimation and simulation methods on real data from non-life insurance. Furthermore, the considered sim- ulation methods are compared with respect to their time efficiency and accuracy of the simulations. 1
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Analysis of Variance with Random Effects
Hamerníková, Iva ; Komárek, Arnošt (advisor) ; Pešta, Michal (referee)
The aim of this thesis is to describe and derive the test of analysis of variance with random effects. At first we introduce a summary of results from the theory of probability which will be important in future derivations. Then we define the one-way classification model with fixed effects and propose the test statistics to test the equality of group means. In the following part we define the one-way classification model with random effects and derive properties of observations in this model. Under the assumption of balanced data we define sums of squares and derive their properties, which allow us to use them to create the test statistic. Finally we will use simulations in R to verify whether the ANOVA test with random effects observes the significance level when normality assumptions are violated.
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Truncated data and stochastic claims reserving
Marko, Dominik ; Pešta, Michal (advisor) ; Mazurová, Lucie (referee)
In this thesis stochastic claims reserving under the model of randomly trun- cated data is presented. For modelling the claims, a compound Poisson process is assumed. Introducing a random variable representing the delay between oc- currence and reporting of a claim, a probability model of IBNR claims is built. The fact that some claims are incurred but not reported yet leads to truncated data. Basic results of non-parametric statistical estimation under the model of randomly truncated data are shown, which can be used to obtain an estimate of IBNR claims reserves. Theoretical background is then used for application on real data from Czech Insurers' Bureau. 36
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Structural Equation Models with Application in Social Sciences
Veselý, Václav ; Pešta, Michal (advisor) ; Maciak, Matúš (referee)
We investigate possible usage of Errors-in-Variables estimator (EIV), when esti- mating structural equations models (SEM). Structural equations modelling pro- vides framework for analysing complex relations among set of random variables where for example the response variable in one equation plays role of the predic- tor in another equation. First an overview of SEM and some common covariance based estimators is provided. Special case of linear regression model is investi- gated, showing that the covariance based estimators yield the same results as ordinary least squares. A compact review of EIV models follows, Errors-in-Variables models are re- gression models where not only response but also predictors are assumed to be measured with an error. Main contribution of this paper then lies in defining modifications of the EIV estimator to fit in the SEM framework. General opti- mization problem to estimate the parameters of structural equations model with errors-in-variables si postulated. Several modifications of two stage least squares are also proposed for future research. Equation-wise Errors-in-Variables estimator is proposed to estimate the coeffi- cients of structural equations model. The coefficients of every structural equation are estimated separately using EIV estimator. Some theoretical conditions...
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Statistical inference in varying coefficient models
Splítek, Martin ; Maciak, Matúš (advisor) ; Pešta, Michal (referee)
Tato práce se zabývá modely s promìnlivými koe cienty se za- mìøením na statistickou inferenci. Hlavní my¹lenkou tìchto modelù je pou¾ití regresních koe cientù, mìnících se v závislosti na nìjakém modi kátoru vlivu, namísto konstantních koe cientù klasické lineární regrese. Nejprve si de nujeme tyto modely a jejich odhadové procedury, kterých bylo doposud publikováno nì- kolik variant. K odhadu se pou¾ívá lokální regrese nebo rùzné druhy splajnù { vyhlazovací, polynomiální èi penalizované. Od metody odhadu se následnì od- víjí i daná statistická inference, ke které uvedeme odvozené vychýlení, rozptyl, asymptotickou normalitu, kon denèní pásma a testování hypotéz. Hlavním cílem na¹í práce je kompaktnì shrnout vybrané metody a jejich inferenci. Na závìr je navr¾ena proceduru pro výbìr promìnných.
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Hurdle models in non-life insurance
Tian, Cheng ; Pešta, Michal (advisor) ; Branda, Martin (referee)
A number of articles only present hurdle models for count data. we are motivated to present hurdle models for semi-continuous data. Because semi- continuous data is also commonly seen in non-life insurance. The thesis deals with the parameterization of various hurdle models for semi-continuous data besides for count data in non-life insurance. Two components of a hurdle model are modeled separately. A hurdle component is modeled by a logistic regression. For a semi-continuous data, a continuous component is modeled by several various regressions. Parameters of each component are estimated through maximum likelihood estimation. Model selection is mentioned before theoretical approaches are applied on the vehicle insurance data. Finally, we get some predicted values based on the fitted models. The prediction gives insurance companies a general idea on setting premium but not accurate. 1
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Loss reserving for individual claim-by-claim data
Bednárik, Vojtěch ; Pešta, Michal (advisor) ; Hurt, Jan (referee)
This thesis covers stochastic claims reserving in non-life insurance based on individual claims developments. Summarized theoretical methods are applied on data from Czech Insurers' Bureau for educational purposes. The problem of estimation is divided into four parts: oc- curence process generating claims, delay of notification, times between events and payments. Each part is estimated separately based on maximum likelihood theory and final estimates allow us to obtain an estimate of future liabilities distribution. The results are very promis- ing and we believe this method is worth of a further research. Contribution of this work is more rigorous theoretical part and application on data from the Czech market with some new ideas in practical part and simulation. 1
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Maximum likelihood theory for not i.i.d. observations
Kielkowská, Eva ; Omelka, Marek (advisor) ; Pešta, Michal (referee)
Maximum likelihood approach for independent but not identically distributed observations is studied. In the first part of the thesis, conditions for consistency and asymptotic normality of the maximum likelihood estimates for this case are stated. Uniform integrability has a major role in proving the desired properties. K-sample problem serves as an example for using the described method. The second part is focused on estimates obtained by minimizing convex functions. Convexity is a key for showing the consistency and asymptotic normality of the estimates in this case. The results can be used for maximum likelihood when observations with logconcave densities are involved. Finally, normal linear model, logistic regression and Poisson regression examples are provided to present the application of the method.
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