This thesis concerns dependence issues arising from nonparametric change-point analysis based on weighted approximations. We will establish new approximation results under strong mixing conditions. Based on coupling methods, approximations for weighted tied-down partial sum processes by standardized Brownian bridge processes will be derived. Moreover, we will present some new "backward" strong invariance principles for linear processes with strongly mixing errors. As a consequence, we are able to establish Darling-Erdös type limit theorems for weighted tied-down partial sum processes within a financial time series framework
