Many modern processes are capable of generating rich and complex data records not readily analyzed by traditional techniques. A single observation from a process might consist of n pairs of bivariate data that can be described via some functional relation (for example, a sequence of radar reflection signals measured over time). Or, each observation in a process may be a sample of data from some distribution. Methods are proposed here for detecting changes in such sequences from some known or estimated nominal state. Additionally, estimates of the degree of change (scale, location, etc.) are desirable and discussed. The proposed methods are designed to take advantage of all available data in a sequence. This can become unwieldy for long sequences of large-sized observations, so dimension reduction techniques are needed. In order for these methods to be as widely applicable as possible, we make limited distributional assumptions and so we propose new nonparametric tools to implement these estimators.