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Change-point and model estimation with heteroskedastic noise and unknown model structure


Publication Type:

Conference/Workshop Paper


9th International Conference on Control, Decision and Information Technologies




In this paper, we investigate the problem of modeling time-series as a process generated through (i) switching between several independent sub-models; (ii) where each sub-model has heteroskedastic noise, and (iii) a polynomial bias, describing nonlinear dependency on system input. First, we propose a generic nonlinear and heteroskedastic statistical model for the process. Then, we design Maximum Likelihood (ML) parameters estimation method capable of handling het- heteroscedasticity and exploiting constraints on model structure. We investigate solving the intractable ML optimization using population-based stochastic numerical methods. We then find possible model change-points that maximize the likelihood without over-fitting measurement noise. Finally, we verify the usefulness of the proposed technique in a practically relevant case study, the execution time of odometry estimation for a robot operating radar sensor, and evaluate the different proposed procedures using both simulations and field data.


author = {Anas Al-hashimi and Thomas Nolte and Alessandro Papadopoulos},
title = {Change-point and model estimation with heteroskedastic noise and unknown model structure},
pages = {2126--2132},
month = {July},
year = {2023},
booktitle = {9th International Conference on Control, Decision and Information Technologies},
url = {}