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The Benchmark Dose (BMD) approach, which was suggested firstly in 1984 by K. Crump [CRUMP (1984)], is a widely used instrument in risk assessment of substances in the environment and in food. In this context, the BMD approach determines a reference point (RfP) on the statistically estimated dose-response curve, for which the risk can be determined with adequate certainty and confidence. In the next step of risk characterization a threshold is calculated, based on this RfP and toxicological considerations. The BMD approach bases upon the fit of a dose-response model on the data. For this fit a stochastic distribution of the response endpoint is taken as a basis. Ultimately, the BMD reflects the dose for which a pre-specified increase in an adverse health effect (the benchmark response) can be expected. Until now, the BMD approach has been specified only for quantal and continuous endpoints. But in risk assessment of carcinogens especially so called time-to-event data are of high interest since they contain more information on the tumor development than quantal incidence data. The goal of this diploma thesis was to extend the BMD approach to such time-to-event data.
We presented a proof for the classical stable limit laws under use of contraction method in combination with the Zolotarev metric. Furthermore, a stable limit law was proved for scaled sums of growing into sequences. This limit law was alternatively formulated for sequences of random variables defined by a simple degenerate recursion.
This work connects Markov chain imbedding technique (MCIT) introduced by M.V. Koutras and J.C. Fu with distributions concerning the cycle structure of permutations. As a final result program code is given that uses MCIT to deliver proper numerical values for these. The discrete distributions of interest are the one of the cycle structure, the one of the number of cycles, the one of the rth longest and shortest cycle and finally the length of a random chosen cycle. These are analyzed for equiprobable permutations as well as for biased ones. Analytical solutions and limit distributions are also considered to put the results on a safe, theoretical base.
Approximating Perpetuities
(2006)
A perpetuity is a real valued random variable which is characterised by a distributional fixed-point equation of the form X=AX+b, where (A,b) is a vector of random variables independent of X, whereas dependencies between A and b are allowed. Conditions for existence and uniqueness of solutions of such fixed-point equations are known, as is the tail behaviour for most cases. In this work, we look at the central area and develop an algorithm to approximate the distribution function and possibly density of a large class of such perpetuities. For one specific example from the probabilistic analysis of algorithms, the algorithm is implemented and explicit error bounds for this approximation are given. At last, we look at some examples, where the densities or at least some properties are known to compare the theoretical error bounds to the actual error of the approximation. The algorithm used here is based on a method which was developed for another class of fixed-point equations. While adapting to this case, a considerable improvement was found, which can be translated to the original method.
The synchronization of neuronal firing activity is considered an important mechanism in cortical information processing. The tendency of multiple neurons to synchronize their joint firing activity can be investigated with the 'unitary event' analysis (Grün, 1996). This method is based on the nullhypothesis of independent Bernoulli processes and can therefore not tell whether coincidences observed between more than two processes can be considered "genuine" higher- order coincidences or whether they might be caused by coincidences of lower order that coincide by chance ("chance coincidences"). In order to distinguish between genuine and chance coincidences, a parametric model of independent interaction processes (MIIP) is presented. In the framework of this model, Maximum-Likelihood estimates are derived for the firing rates of n single processes and for the rates with which genuine higher order correlations occur. The asymptotic normality of these estimates is used to derive their asymptotic variance and in order to investigate whether higher order coincidences can be considered genuine or whether they can be explained by chance coincidences. The empirical test power of this procedure for n=2 and n=3 processes and for finite analysis windows is derived with simulations and compared to the asymptotic values. Finally, the model is extended in order to allow for the analysis of correlations that are caused by jittered coincidences.