… ) and μ ̂ i j = 1 j − i + 1 ∑ t = i j X t is the mean of the observations X i , … , X j . Consequently
the null … for monitoring changes in the parameter θ j , where θ ̂ i j = θ ( F ̂ i j ) denotes the …

… to replace the factor (m + j) in 9 by the size of … j) is retained as it allocates smaller weights
to the case when the post-change estimator θ ^ m + j + 1 m + k contains greater uncertainty as j …

This paper investigates the problem of detecting relevant change points in the mean vector,
say $\mu_{t}=(\mu_{t,1},\ldots ,\mu_{t,d})^{T}$ of a high dimensional time series $(Z_{t})_{t\in \…

… The fact that the correlations ρi,j are sufficiently small for a large distance |i−j| is crucial to
obtain the desired extreme value convergence. Both parts, (SD1) and (SD2), are in line with …

… RSW ( x , ϑ ˆ j , γ ˆ j , k ˆ j ) of the RSW function from the data points { ( x i , E i ) | i = 1 , … , j }
. For this purpose we determine the estimates k ˆ j , ϑ ˆ j , γ ˆ j using the least squares method …

We propose two new trading strategies which are based on a mathematical hypothesis testing
procedure identifying change points in the volatility structure of equity indices. In the first …

In this paper optimal designs for regression problems with spherical predictors of arbitrary
dimension are considered. Our work is motivated by applications in material sciences, where …

… For the definition of the data generating processes, we use the models (V1) - (V3) introduced
in Section 5.2 but with a different process {εj}j∈Z = {(εj,1,εj,2) }j∈Z. In this section {εj}j∈Z is …

… i as the distance between i and j grows. Here, our considerations will benefit from the
influence function, which is another … j i denotes the estimator of the parameter from the …

… By definition of the empirical quantile max1≤i<j≤n … i ≤ maxn t=1 Xt for all i, j ∈ {1,...,n},
we observe that the term on the left-hand side of (A.19) is bounded by … We consider the …