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Title: Robust Monitoring Procedures for Dependent Data Author: Ondřej Chochola Department: Department of Probability and Mathematical Statistics Supervisor: Prof. RNDr. Marie Hušková, DrSc. Supervisor's e-mail address: huskova@karlin.mff.cuni.cz Abstract: In the thesis we focus on sequential monitoring procedures. We extend some known results towards more robust methods. The robustness of the procedures with respect to outliers and heavy-tailed observations is introduced via use of M-estimation instead of classical least squares estimation. Another extension is towards dependent and multivariate data. It is assumed that the observations are weakly dependent, more specifically they fulfil strong mixing condition. For several models, the appropriate test statistics are proposed and their asymptotic properties are studied both under the null hypothesis of no change as well as under the alternatives, in order to derive proper critical values and show consistency of the tests. We also introduce retrospective change-point procedures, that allow one to verify in a robust way the stability of the historical data, which is needed for the sequential monitoring. Finite sample properties of the tests need to be also examined. This is done in a simulation study and by application on some real data in the capital asset...

Made available in DSpace on 2010-04-20T20:08:22Z (GMT). No. of bitstreams: 0 Previous issue date: 1995-03-27T00:00:00Z / Trata da determinação das taxas de desconto ajustadas ao risco para avaliação, pelo método do fluxo de caixa descontado, do valor econômico de estatais brasileiras privatizadas no período 1991-1992. Utiliza conceitos financeiros como valor presente líquido, eficiência de mercado, capital asset pricing model e risco sistemático, que são aplicados a dados do mercado de capitais e demonstrativos financeiros das empresas. Com as metodologias de regressão linear simples múltipla, desenvolve uma equação que relaciona índices fundamentais com os coeficientes de risco sistemático (betas) das empresas de uma amostra, que por sua vez serve para estimar os coeficientes de risco das estatais.