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Sequential monitoring for change in scale

Ondřej Chochola (2008)

Kybernetika

We propose a sequential monitoring scheme for detecting a change in scale. We consider a stable historical period of length m . The goal is to propose a test with asymptotically small probability of false alarm and power 1 as the length of the historical period tends to infinity. The asymptotic distribution under the null hypothesis and consistency under the alternative hypothesis is derived. A small simulation study illustrates the finite sample performance of the monitoring scheme.

Stability estimating in optimal sequential hypotheses testing

Evgueni I. Gordienko, Andrey Novikov, Elena Zaitseva (2009)

Kybernetika

We study the stability of the classical optimal sequential probability ratio test based on independent identically distributed observations X 1 , X 2 , when testing two simple hypotheses about their common density f : f = f 0 versus f = f 1 . As a functional to be minimized, it is used a weighted sum of the average (under f 0 ) sample number and the two types error probabilities. We prove that the problem is reduced to stopping time optimization for a ratio process generated by X 1 , X 2 , with the density f 0 . For τ * being the corresponding...

Statistical Inference about the Drift Parameter in Stochastic Processes

David Stibůrek (2013)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

In statistical inference on the drift parameter a in the Wiener process with a constant drift Y t = a t + W t there is a large number of options how to do it. We may, for example, base this inference on the properties of the standard normal distribution applied to the differences between the observed values of the process at discrete times. Although such methods are very simple, it turns out that more appropriate is to use the sequential methods. For the hypotheses testing about the drift parameter it is more...

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