Statistical tests based on empirical processes

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In most applications of statistics, stochastic dynamic models are assumed to satisfy certain assumptions, mainly related to their probability distribution. For example, in time series, random error terms (innovations) are often assumed to be independent and/or have a density belonging to some parametric family. The general objective of the proposed research program is to further study empirical processes based on pseudo-observations, which are transformations of data from the same sample, and use them to develop tests of change-point, independence and goodness-of-fit test for models appearing mainly in actuarial science, econometrics, and statistics.