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Bounded length sequential intervals
Lapšanská, Alica ; Hušková, Marie (advisor) ; Hlávka, Zdeněk (referee)
The Bachelor's thesis concerns the construction of bounded length sequential intervals with predetermined confidence. This paper analyses some methods, which solve this problem. In the first part we deal with a special case of random sample from normal population. For a known variance we use knowledge from nonsequential theory of interval estimation. We describe Stein's two-stage procedure for an unknown variance. Furthermore, we determine expected value of total sample range for various interval lengths. The second part generally considers a random sample from population with unknown finite variance. We present modified Stein's procedure and sequential Wald's procedure. Finally using simulation, we endeavor to find out a distribution of random variable, which corresponds to the sample range in case of unknown variance. We do this for all of the three mentioned procedures.
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Multistage Stochastic Programming Problems - Decomposition
Lapšanská, Alica ; Kaňková, Vlasta (advisor) ; Lachout, Petr (referee)
The thesis deals with a multistage stochastic model and its application to a number of practical problems. Special attention is devoted to the case where a random element follows an autoregressive sequence and the constraint sets correspond to the individual probability constraints. For this case conditions under which is the problem well-defined are specified. Further, the approximation of the problem and its convergence rate under the empirical estimate of the distribution function is analyzed. Finally, an example of the investment in financial instruments is solved, which is defined as a two-stage stochastic programming problem with the probability constraint and a random element following an autoregressive sequence. Powered by TCPDF (www.tcpdf.org)
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Bounded length sequential intervals
Lapšanská, Alica ; Hušková, Marie (advisor) ; Hlávka, Zdeněk (referee)
The Bachelor's thesis concerns the construction of bounded length sequential intervals with predetermined confidence. This paper analyses some methods, which solve this problem. In the first part we deal with a special case of random sample from normal population. For a known variance we use knowledge from nonsequential theory of interval estimation. We describe Stein's two-stage procedure for an unknown variance. Furthermore, we determine expected value of total sample range for various interval lengths. The second part generally considers a random sample from population with unknown finite variance. We present modified Stein's procedure and sequential Wald's procedure. Finally using simulation, we endeavor to find out a distribution of random variable, which corresponds to the sample range in case of unknown variance. We do this for all of the three mentioned procedures.
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