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Statistical Analysis of Sample with Small Size
Holčák, Lukáš ; Hübnerová, Zuzana (referee) ; Karpíšek, Zdeněk (advisor)
This diploma thesis is focused on the analysis of small samples where it is not possible to obtain more data. It can be especially due to the capital intensity or time demandingness. Where the production have not a wherewithall for the realization more data or absence of the financial resources. Of course, analysis of small samples is very uncertain, because inferences are always encumbered with the level of uncertainty.
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Confidence Intervals for Quantiles
Horejšová, Markéta ; Kulich, Michal (advisor) ; Hlávka, Zdeněk (referee)
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are explained. Among nonparametric approaches, a special emphasis is dedicated to a recent method based on a multinomial distribution for calculating the overall confidence level of confidence intervals for all quantiles of interest using an efficient recursive algorithm, which is also described. Furthermore, a method based on Kolmogorov-Smirnov statistic or an asymptotic method using empirical distribution function and order statistics for quantile estimate are presented. A special parametric method for several quantiles of a normally distributed population is introduced along with a few of its modifications. Subsequently, a simulation is run to test the real coverage of the described theoretical methods. Powered by TCPDF (www.tcpdf.org)
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Confidence intervals for parameters of multinomial distribution
Bárnetová, Kamila ; Anděl, Jiří (advisor) ; Omelka, Marek (referee)
Title: Confidence intervals for parameters of multinomial distribution Author: Kamila Bárnetová Department: Department of Probability and Mathematical Statistics Supervisor: prof. RNDr. Jiří Anděl, DrSc., Department of Probability and Mathematical Statistics Abstract: Confidence intervals for parameters for binomial and multinomial distribution are described in this thesis. These intervals can be used in practice, for exemple- pre-election estimates. The first two chapter are devoted to derivation of these intervals. Simulations and comparison of several selected methods can be found in the last chapter. Based on the simulations, we consider it appropriate, to choose to calculate confidence intervals for parameters of multinomial distribution intervals based on Bonferroniho inequality, or their modifications. These intervals are easy to calculate, while their coverage probability is at least 0.89. Keywords: confidence interval, multinomial distribution, binomial distribution, Bonferroni inequality
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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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Confidence Intervals for Quantiles
Horejšová, Markéta ; Kulich, Michal (advisor) ; Hlávka, Zdeněk (referee)
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are explained. Among nonparametric approaches, a special emphasis is dedicated to a recent method based on a multinomial distribution for calculating the overall confidence level of confidence intervals for all quantiles of interest using an efficient recursive algorithm, which is also described. Furthermore, a method based on Kolmogorov-Smirnov statistic or an asymptotic method using empirical distribution function and order statistics for quantile estimate are presented. A special parametric method for several quantiles of a normally distributed population is introduced along with a few of its modifications. Subsequently, a simulation is run to test the real coverage of the described theoretical methods. Powered by TCPDF (www.tcpdf.org)
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Confidence intervals for parameters of multinomial distribution
Bárnetová, Kamila ; Anděl, Jiří (advisor) ; Omelka, Marek (referee)
Title: Confidence intervals for parameters of multinomial distribution Author: Kamila Bárnetová Department: Department of Probability and Mathematical Statistics Supervisor: prof. RNDr. Jiří Anděl, DrSc., Department of Probability and Mathematical Statistics Abstract: Confidence intervals for parameters for binomial and multinomial distribution are described in this thesis. These intervals can be used in practice, for exemple- pre-election estimates. The first two chapter are devoted to derivation of these intervals. Simulations and comparison of several selected methods can be found in the last chapter. Based on the simulations, we consider it appropriate, to choose to calculate confidence intervals for parameters of multinomial distribution intervals based on Bonferroniho inequality, or their modifications. These intervals are easy to calculate, while their coverage probability is at least 0.89. Keywords: confidence interval, multinomial distribution, binomial distribution, Bonferroni inequality
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Profile likelihood
Pejřimovský, Pavel ; Omelka, Marek (advisor) ; Jurečková, Jana (referee)
This thesis deals with a statistical method called pro le likelihood. We use it in estimating the unknown parameters in the presence of interfering parameters, compiling con dence intervals or testing hypotheses for example. Pro le likelihood is directly derived from the maximum likelihood method, which is one of the fundamental methods of mathematical statistics in estimating the unknown parameters. The maximum likelihood method is the base for asymptotic tests and their possible generalization on tests with nuisance parameters. In this work we show that there is some connection between the pro le likelihood and tests with nuisance parameters . It also illustrates the usage pro le credibility of the classic examples of the normal distribution. Powered by TCPDF (www.tcpdf.org)
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Interval estimates for binomial proportion
Borovský, Marko ; Zvára, Karel (advisor) ; Sečkárová, Vladimíra (referee)
The subject of this thesis is the point estimate and interval estimates of the binomial proportion. Interval estimation of the probability of success in a binomial distribution is one of the most basic and crucial problems in statistical practice. The thesis is divided into three chapters. The first chapter is about maximum- likelihood estimation for a binomial proportion. Futhermore, we will describe several methods of the construction of confidence intervals. In the end, we will compare all intervals in term of the actual coverage probability and expected length. 1
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Statistical Analysis of Sample with Small Size
Holčák, Lukáš ; Hübnerová, Zuzana (referee) ; Karpíšek, Zdeněk (advisor)
This diploma thesis is focused on the analysis of small samples where it is not possible to obtain more data. It can be especially due to the capital intensity or time demandingness. Where the production have not a wherewithall for the realization more data or absence of the financial resources. Of course, analysis of small samples is very uncertain, because inferences are always encumbered with the level of uncertainty.
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