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It is widely known that the most common estimators of the variance and the standard deviation based on i.i.d. data are not optimal with respect to the mean squared error. The aim of this thesis is to study and summarize the various approaches to seeking an improved estimator, which stem mainly from the innovative ideas presented by Stein (1964). Taken into consideration is the point estimator of the variance and the standard deviation. Each of the improved estimators include, in addition to their construction, a discussion regarding admissibility with respect to the MSE. Subsequently, using simple simulations for various distributions, it is examined whether obtained improvements lead to better results in practice. Powered by TCPDF (www.tcpdf.org)

Anywhere we see a prognostication system; we can run up against the need of evaluating it. For the evaluation is frequently used unsuitable methods. There are such cases where is need to correctly take effects of mistakes into consideration. And here we can use statistic based on information measure either entropy. The first part deals with this point of issue. In the second part I am trying to show possible areas of use, for example betting. I enclose practical remarks from this issue and the methods making these estimators, which are dependent on nature of issued events.