National Repository of Grey Literature 8 records found  Search took 0.00 seconds. 
Models of binary time series
Kunayová, Monika ; Zichová, Jitka (advisor) ; Cipra, Tomáš (referee)
This bachelor thesis deals with the time series of binary variables that exist in many social spheres. The indicator may denote a certain value being exceeded or a phenomenon occurring. We study a model of logistic autoregression and its properties, partial likelihood function which allows us to work with dependent data, and derive useful relationships for a practical application that consists of time series simulation and real data analysis using free software R.
Zero inflated Poisson model
Veselý, Martin ; Komárek, Arnošt (advisor) ; Hlávka, Zdeněk (referee)
This paper deals with the zero-inflated Poisson distribution. First the Poisson model is defined and generalized to a zero-inflated model. The basic properties of this generalized model are derived. After- wards the basics of the method of moments and the maximum likelihood method are described. Both of these are used to derive parameter estimates of such distribution. The feasibility of calculating the distribution of moment method estimates is analyzed. Then the asymptotic distribution of maximum likelihood estimates is derived and used to create confidence intervals. In the last chapter a numeric si- mulation of the derived asymptotic properties is performed. Special attention is paid to situations where regularity conditions are not met. 1
Parameter estimation of gamma distribution
Zahrádková, Petra ; Kulich, Michal (advisor) ; Hlávka, Zdeněk (referee)
It is well-known that maximum likelihood (ML) estimators of the two parame- ters in a Gamma distribution do not have closed forms. The Gamma distribution is a special case of a generalized Gamma distribution. Two of the three likeli- hood equations of the generalized Gamma distribution can be used as estimating equations for the Gamma distribution, based on which simple closed-form estima- tors for the two Gamma parameters are available. Intuitively, performance of the new estimators based on likelihood equations should be close to the ML estima- tors. The study consolidates this conjecture by establishing the asymptotic beha- viours of the new estimators. In addition, the closed-forms enable bias-corrections to these estimators. 1
Estimation in continuous time Markov chains
Nemčovič, Bohuš ; Prokešová, Michaela (advisor) ; Kadlec, Karel (referee)
Title: Estimation in continuous time Markov chains Author: Bohuš Nemčovič Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Michaela Prokešová, Ph.D., Department of Probability and Mathematical Statistics Abstract: In this work we deal with estimating the intensity matrices of continu- ous Markov chains in the case of complete observation and observation at selected discrete time points. To obtain an estimate we use the maximum likelihood met- hod. In the second chapter we first introduce the general EM algorithm and then adjust it for finding the intensity matrix estimate based on observations at disc- rete time points. In the last chapter we will illustrate the impact of the discrete step size on the quality of intensity matrix estimate. Keywords: Markov chains, intensity matrix, maximum likelihood estimation, EM algorithm 1
Model with Weibull responses
Konečná, Tereza ; Karpíšek, Zdeněk (referee) ; Hübnerová, Zuzana (advisor)
Tato diplomová práce se zabývá Weibullovými modely, přesněji dvouparametrickým Weibullovým rozdělením. Práce se zabývá odhady parametrů, a to čtyřmi variantami kvantilové metody, metodou maximální věrohodnosti a grafickou metodou Weibullova pravděpodobnostního grafu. Je uvedeno odvození odhadu parametrů pro jednovýběrovou analýzu rozptylu pro Weibullovo rozdělení. Jsou zde odvozeny vztahy pro model s konstantním parametrem alfa, s konstantním parametrem beta a s oběma konstantními parametry. Také jsou uvedeny testové statistiky pro rušivé parametry - skórový test, Waldův test a test založený na věrohodnostním poměru. V poslední kapitole je provedena aplikace jednotlivých představených metod. Srovnání metod je ukázáno pomocí grafů, histogramů a tabulek. Metody jsou naprogramovány v~softwaru R. Jejich funkčnost a vlastnosti jsme ověřili na dvou simulovaných souborech dat. Diplomová práce je zakončena příkladem tří simulovaných náhodných výběrů, na kterých byla provedena analýza pomocí zavedených metod.
Maximum likelihood estimators in time series
Tritová, Hana ; Pawlas, Zbyněk (advisor) ; Zikmundová, Markéta (referee)
The thesis deals with maximum likelihood estimators in time series. The reader becomes familiar with three important models for time series: autoregressive model (AR), moving average model (MA) and autoregressive moving average (ARMA). Thereafter he can find out the form of their main characteristics, e.g. population mean and variance. Then there is the derivation of parameter estimates - generally and for mentioned models of times series. There are also stated two other methods for finding estimators of AR(1) and MA(1) parameters - method of moments and least squares method. The end is dedicated to examples which compares all three methods.
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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