Kernel autocorrelogram for time-deformed processes |
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Institution: | 1. Materials Program, Department of Chemical and Materials Engineering, University of Kentucky, Lexington, 40506, KY United States of America;2. School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University,Shanghai 201209, China;3. School of Aerospace Engineering and Applied Mechanics, Tongji University, No.1239 Siping Road, Shanghai 200092, China |
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Abstract: | The purpose of the paper is to propose an autocorrelogram estimation procedure for irregularly spaced data which are modelled as subordinated continuous time-series processes. Such processes, also called time-deformed stochastic processes, have been proposed in a variety of contexts. Before entertaining the possibility of modelling such time series, one is interested in examining simple diagnostics and data summaries. With continuous-time processes this is a challenging task which can be accomplished via kernel estimation. The paper develops the conceptual framework, the estimation procedure and its asymptotic properties. An illustrative empirical example is also provided. |
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