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Motivated by an example from remote sensing of gas emission sources, wederive two novel change point procedures for multivariate time series where, incontrast to classical change point literature, the changes are not required tobe aligned in the different components of the time series. Instead the changepoints are described by a functional relationship where the precise shapedepends on unknown parameters of interest such as the source of the gasemission in the above example. Two different types of tests and thecorresponding estimators for the unknown parameters describing the changelocations are proposed. We derive the null asymptotics for both tests underweak assumptions on the error time series and show asymptotic consistency underalternatives. Furthermore, we prove consistency for the correspondingestimators of the parameters of interest. The small sample behavior of themethodology is assessed by means of a simulation study and the above remotesensing example analyzed in detail.

A novel change point approach for the detection of gas emission sources using remotely contained concentration data