We consider problems of quantifying and monitoring accuracy and precision of measurement in mass spectrometry, particularly in contexts where there is unavoidable day-to-day/period-to-period changes in instrument sensitivity. First we consider the issue of estimating instrument sensitivity based...

In many applications one is interested to detect certain (known) patterns in the mean of a process with smallest delay. Using an asymptotic framework which allows to capture that feature, we study a class of appropriate sequential nonparametric kernel procedures under local nonparametric...

Markov Chains are introduced by only assuming some knowledge of the notion of probability. The modelling of a situation in a context of biology gives the opportunity to students to approach various concepts of probability theory themselves.

The problem of constructing standardized maximin D-optimal designs for weighted polynomial regression models is addressed. In particular it is shown that, by following the broad approach to the construction of maximin designs introduced recently by Dette, Haines and Imhof (2003), such designs...

An attractive nonparametric method to detect change-points sequentially is to apply control charts based on kernel smoothers. Recently, the strong convergence of the associated normed delay associated with such a sequential stopping rule has been studied under sequences of out-of-control models....

Problem: Cigarette smoking is the most important risk factor of transitional cell carcinoma of the urinary bladder. The effect of the glutathione S- transferases M1 (GSTM1) and M3 (GSTM3) on the influence of this risk factor was investigated. Methods: A total of 293 bladder cancer patients from...

We investigate the OLS-based estimator s2 of the disturbance variance in the standard linear regression model with cross section data when the disturbances are homoskedastic, but spatially correlated. For the most popular model of spatially autoregressive disturbances, we show that s2 can be...

The paper considers the Markov-Switching GARCH(1,1)-model with time-varying transition probabilities. It derives su±cient conditions for the square of the process to display long memory and provides some additional intuition for the empirical observation that estimated GARCH-parameters often...

It has long been known that the estimated persistence parameter in the GARCH(1,1) - model is biased upwards when the parameters of the model are not constant throughout the sample. The present paper explains the mechanics of this behavior for a particular class of estimates of the model...