Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Charrelation matrices are similar in structure (and in additional properties) to correlation matrices, and are closely related to Hessians of the log-characteristic function at selected “processing-points” away from the origin. Charrelation-based estimation methods re shown to offer significant improvement over second-order (correlation-based) methods when the latter are suboptimal. Hover, judicious selection of the processing-points is required in order to achieve such improvement. In the context of estimating the parameters of an autoregressive process,  present here a method for proper data-driven selection of the processing-points, finding the one which minimizes the predicted mean square estimation error. The resulting performance improvement over classical competing methods is demonstrated in simulation.
['Alon Slapak', 'Arie Yeredor']
I am writing a research paper on a idea Charrelation-based estimation of the parameters of non-Gaussian autoregressive processes
[{"from": "human", "value": "I am writing a research paper on a idea Charrelation-based estimation of the parameters of non-Gaussian autoregressive processes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Charrelation matrices are similar in structure (and in additional properties) to correlation matrices, and are closely related to Hessians of the log-characteristic function at selected \u201cprocessing-points\u201d away from the origin. Charrelation-based estimation methods re shown to offer significant improvement over second-order (correlation-based) methods when the latter are suboptimal. Hover, judicious selection of the processing-points is required in order to achieve such improvement. In the context of estimating the parameters of an autoregressive process,  present here a method for proper data-driven selection of the processing-points, finding the one which minimizes the predicted mean square estimation error. The resulting performance improvement over classical competing methods is demonstrated in simulation."}]