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Tests of statistical hypotheses in measurement error models
Navrátil, Radim ; Jurečková, Jana (advisor) ; Hušková, Marie (referee) ; Kalina, Jan (referee)
The behavior of rank procedures in measurement error models was studied - if tests and estimates stay valid and applicable when there are some measurement errors involved and if not how to modify these procedures to be able to do some statistical inference. A new rank test for the slope parameter in regression model based on minimum distance esti- mator and an aligned rank test for an intercept were proposed. The (asymptotic) bias of R-estimator in measurement error model was also investigated. Besides measurement errors the problem of heteroscedastic model errors was considered - regression rank score tests of heteroscedasticity with nuisance regression and tests of regression with nuisance heterosce- dasticity were proposed. Finally, in location model tests and estimates of shift parameter for various measurement errors were studied. All the results were derived theoretically and then demonstrated numerically with examples or simulations.
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Statistical applications of urn models
Navrátil, Radim ; Pawlas, Zbyněk (advisor) ; Omelka, Marek (referee)
This work shows various applications of urn models in practice. First, basic properties of the occupancy distribution are derived together with its asymptotic approximation. This model is applied and generalized in the theory of database systems for records search from a given database. An application to random texts is mentioned, namely the computation of the expected number of missing and common words in random texts. There are presented exact formulas, their asymptotic approximations and the approximations via occupancy distribution. Then, some urn models, which are used in the randomized response theory for finding out respondents' answers to sensitive questions, are described. These models are compared according to their accuracy and respondents' goodwill to answer. Finally, two non-parametric tests of empty boxes are derived, one for the hypothesis whether a random sample comes from a given population and the second for the hypothesis whether two independent random samples come from the same population. The powers of these tests are compared with commonly used tests for these hypotheses.
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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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Tests of statistical hypotheses in measurement error models
Navrátil, Radim
The behavior of rank procedures in measurement error models was studied - if tests and estimates stay valid and applicable when there are some measurement errors involved and if not how to modify these procedures to be able to do some statistical inference. A new rank test for the slope parameter in regression model based on minimum distance esti- mator and an aligned rank test for an intercept were proposed. The (asymptotic) bias of R-estimator in measurement error model was also investigated. Besides measurement errors the problem of heteroscedastic model errors was considered - regression rank score tests of heteroscedasticity with nuisance regression and tests of regression with nuisance heterosce- dasticity were proposed. Finally, in location model tests and estimates of shift parameter for various measurement errors were studied. All the results were derived theoretically and then demonstrated numerically with examples or simulations.
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|
|
Tests of statistical hypotheses in measurement error models
Navrátil, Radim
The behavior of rank procedures in measurement error models was studied - if tests and estimates stay valid and applicable when there are some measurement errors involved and if not how to modify these procedures to be able to do some statistical inference. A new rank test for the slope parameter in regression model based on minimum distance esti- mator and an aligned rank test for an intercept were proposed. The (asymptotic) bias of R-estimator in measurement error model was also investigated. Besides measurement errors the problem of heteroscedastic model errors was considered - regression rank score tests of heteroscedasticity with nuisance regression and tests of regression with nuisance heterosce- dasticity were proposed. Finally, in location model tests and estimates of shift parameter for various measurement errors were studied. All the results were derived theoretically and then demonstrated numerically with examples or simulations.
|
|
|
Tests of statistical hypotheses in measurement error models
Navrátil, Radim ; Jurečková, Jana (advisor) ; Hušková, Marie (referee) ; Kalina, Jan (referee)
The behavior of rank procedures in measurement error models was studied - if tests and estimates stay valid and applicable when there are some measurement errors involved and if not how to modify these procedures to be able to do some statistical inference. A new rank test for the slope parameter in regression model based on minimum distance esti- mator and an aligned rank test for an intercept were proposed. The (asymptotic) bias of R-estimator in measurement error model was also investigated. Besides measurement errors the problem of heteroscedastic model errors was considered - regression rank score tests of heteroscedasticity with nuisance regression and tests of regression with nuisance heterosce- dasticity were proposed. Finally, in location model tests and estimates of shift parameter for various measurement errors were studied. All the results were derived theoretically and then demonstrated numerically with examples or simulations.
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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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Statistical applications of urn models
Navrátil, Radim ; Omelka, Marek (referee) ; Pawlas, Zbyněk (advisor)
This work shows various applications of urn models in practice. First, basic properties of the occupancy distribution are derived together with its asymptotic approximation. This model is applied and generalized in the theory of database systems for records search from a given database. An application to random texts is mentioned, namely the computation of the expected number of missing and common words in random texts. There are presented exact formulas, their asymptotic approximations and the approximations via occupancy distribution. Then, some urn models, which are used in the randomized response theory for finding out respondents' answers to sensitive questions, are described. These models are compared according to their accuracy and respondents' goodwill to answer. Finally, two non-parametric tests of empty boxes are derived, one for the hypothesis whether a random sample comes from a given population and the second for the hypothesis whether two independent random samples come from the same population. The powers of these tests are compared with commonly used tests for these hypotheses.
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