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Modeling progression of HIV disease
Žohová, Ivana ; Kulich, Michal (advisor) ; Zvára, Karel (referee)
In the present work we study modeling of HIV disease progression via multistate Markov model. The difficulty in this approach is how to define HIV disease states. These are usually defined in terms of CD4+ T lymphocyte counts, but this marker is a subject to biological fluctuation and, in real life, measurement errors as well. Estimating the model on such a data will lead to intensity estimates depending on frequency of observations. That is why we usually smooth the data before fitting the Markov model. In this work we studied two different approaches - linear mixed-effects model and local polynomial kernel estimator. All modeling is performed on real data and also an illustrative simulation example is included. Another issue considered in this work is determination of sero-conversion time. The sero-conversion distribution is derived based on time of last negative observation, first positive observation and last performed measurement.
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Introduction to Nonparametric Methods
Prelecová, Natália ; Kulich, Michal (advisor) ; Navrátil, Radim (referee)
Title: Introduction to Nonparametric Methods Author: Natália Prelecová Department: Department of Probability and Mathematical Statistics Supervisor: doc. Mgr. Michal Kulich,Ph.D., Department of Probability and Mathematical Statistics Abstract: The aim of this thesis is to introduce basic nonparametric methods. Nonparametric methods embrace a large class of statistical procedures which do not assume specific data distribution such as normal distribution. They often re- present the only available means of examining specific types of data, for example ranks or counts. Weaker assumptions of these methods make them less powerful than their parametric counterparts. This thesis describes in detail four nonparametric tests- the Ordinary Sign Test, the Wilcoxon Signed-rank Test, the Mann-Whitney Test and finally the Two- sample Wilcoxon Test. The structure of their description will entail the following: the formulation of assumptions, null hypothesis and alternatives, the construction of the test statistic and the definition of rejection regions. The most essential prob- lems, such as the problem of ties, will be also covered. The basic characteristics of the Linear Rank Statistics will be also explained, followed by the Two-sample Wilcoxon test. Keywords: nonparametrical, hypothesis, ranks, consistency, statistic
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Parameter Estimation under Two-phase Stratified and Cluster Sampling
Šedová, Michaela ; Kulich, Michal (advisor) ; Picek, Jan (referee) ; Omelka, Marek (referee)
Title: Parameter Estimation under Two-phase Stratified and Cluster Sampling Author: Mgr. Michaela Šedová Department: Department of Probability and Mathematical Statistics Supervisor: Doc. Mgr. Michal Kulich, Ph.D. Abstract: In this thesis we present methods of parameter estimation under two-phase stratified and cluster sampling. In contrast to classical sampling theory, we do not deal with finite population parameters, but focus on model parameter inference, where the ob- servations in a population are considered to be realisations of a random variable. However, we consider the sampling schemes used, and thus we incorporate much of survey sampling theory. Therefore, the presented methods of the parameter estimation can be understood as a combination of the two approaches. For both sampling schemes, we deal with the concept where the population is considered to be the first-phase sample, from which a sub- sample is drawn in the second phase. The target variable is then observed only for the subsampled subjects. We present the mean value estimation, including the statistical prop- erties of the estimator, and show how this estimation can be improved if some auxiliary information, correlated with the target variable, is observed for the whole population. We extend the method to the regression problem....
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Least Squares Alternatives
Gerthofer, Michal ; Pešta, Michal (advisor) ; Kulich, Michal (referee)
In the present thesis we deal with the linear regression models based on least squares. These methods are discussed in two groups. The first one focuses on three primary aproaches devided by occurrence of errors in variables. The traditional approach penalizes only the misfit in the de- pendent variable part and is called the ordinary least squares (OLS). An opposite case to the OLS is represented by the data least squares (DLS), which allow corrections only in the explanatory variables. Consecutively, we concentrate ourselves on the total least squares approach (TLS) mi- nimizing the squares of errors in the values of both dependent and independent variables. Finally, we give attention to next group of methods whit high breakdown point, which deal with signifi- cance of the individual observations (least weighted squares) and elimination of outlying obser- vations (least trimmed squares). The main purpose of this work is to describe and compare these models, their assumptions, characteristics, properties of estimates and show them on real data. 1
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Confidence intervals for the correlation coefficient
Farda, Martin ; Kulich, Michal (advisor) ; Omelka, Marek (referee)
The main goal of the thesis is to introduce methods used for the construction of confidence intervals for correlation coefficient in detail and to show their performances on various examples. In the first chapter is an introduction of basic properties of correlation coefficient and Fisher's z-transformation. The second chapter is about a method based on generalized pivotal quantities. It also contains an explanation why is an assumption of bivariate normal distribution necessary for this method. In the third chapter there is a description of two methods based on empirical likelihood. These methods are approptiate also for non-normal bivariate distributions. In the last chapter are all mentioned methods applied on several examples and compared with each other. 1
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Verification of linear mixed model assumptions
Krnáč, Ľuboš ; Kulich, Michal (advisor) ; Hudecová, Šárka (referee)
1 AbstraktEN The diploma thesis deals with linear mixed effects models. In the first chap- ter, we discuss parameter estimation and hypothesis testing in the linear mixed effects models. The second chapter is dedicated to graphical diagnostics. We look at the suitable diagnostic plots for residuals and random effects estimates. It is closely described, how the violations of assumptions affect the diagnostic plots. In the third chapter we have consequences of the violations of assumptions on the parameter estimates and results of hypothesis testing for fixed effects. 1
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Regression analysis of current status data
Filipová, Anna ; Kulich, Michal (advisor) ; Komárek, Arnošt (referee)
Survival analysis often includes dealing with data that are censored. This thesis focuses on censoring in the form of current status data. We discuss seve- ral methods of regression analysis of current status data and focus mainly on a method that assumes that the time to event follows the additive hazards mo- del. Under the assumption of proportional hazards for the monitoring time, this method does not require knowing the baseline hazard function and allows us to use the theory and software which were developed for Cox model. We also pre- sent a modification of this method, a two-step estimator, and show that it is asymptotically normal and has the advantage of lower asymptotic variance.
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Testing equality of means by confidence intervals
Jandl, Vojtěch ; Kulich, Michal (advisor) ; Pešta, Michal (referee)
We deal with testing the equality of means using confidence intervals. Firstly, we introduce the methods of testing that have already been published. The advantage of these methods is that one can present the underlying confidence intervals alongside the result of the test without doing further calculations. In the second part we discuss the necessary assumptions and by that we extend the Noguchi's method to discrete distribu- tions. Also, we derive a generalization of the Noguchi's method for testing the equality of other parameters than means, based on the assumption of asymptotic normality of their consistent estimates. Lastly, we conduct a simulation study in order to compare the methods we discussed. We found out that the Noguchi's method is a worthy alternative to the often-used Welch test bearing the advantage of being able to present extra visual output in the form of the underlying confidence intervals. In comparison to other methods the Noguchi's method yields better results in the case of unequal or small sample sizes. Unlike other methods it can also be used for testing in the paired sample case. 1
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Semiparametric additive risk model
Zavřelová, Adéla ; Kulich, Michal (advisor) ; Maciak, Matúš (referee)
Cox proportional hazard model is often used to estimate the effect of covariates on hazard for censored event times. In this thesis we study the semiparametric models of additive risk for censored data. In this model the hazard is given as a sum of unknown baseline hazard function and a product of covariates and coefficients. Further the general additive-multiplicative model is assumed. In this model the effect of a covariate can be either multiplicative, additive or both at the same time. We focuse on determining the effect of a covariate in the general model. This model can be used to test for the multiplicative or addtive effect of a covariate on the hazard.
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Testing independence in two-by-two tables
Obukhov, Andrey ; Omelka, Marek (advisor) ; Kulich, Michal (referee)
The main purpose of this work is to describe three well-known statistical tests of independence in two-by-two contingency tables. We will deeply study chi- squared test of independence, Fisher's exact test and Barnard's test and apply them on examples. Also we will describe, in general, categorical variables, which are often analysed using a multinomial distribution. At the end we will apply tests on the examples, using data simulated from a multinomial and binomial distribution. 1
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