Abstract
Let be a real-valued continuous-time jointly stationary processes and let tj be a renewal point process on [0,00], with finite mean rate independent of (Y,X). Given the observations and a measurable function, we estimate the multivariate probability density and the regression function of given X(0) = xo, X(T) = XI, …, X(r m ) = x m for arbitrary lags m. We present consistency and asymptotic normality results for appropriate estimates of f and r.
Keywords
- Regression Function
- Asymptotic Normality
- Asymptotic Normality Result
- Local Polynomial Fitting
- Multivariate Probability Density
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Research partially supported by NSF Grant DMS-97-03876
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To the memory of Stamatis: A lifelong friend, research collaborator, and colleague
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Masry, E. (1998). Multivariate Probability Density and Regression Functions Estimation of Continuous-Time Stationary Processes from Discrete-Time Data. In: Karatzas, I., Rajput, B.S., Taqqu, M.S. (eds) Stochastic Processes and Related Topics. Trends in Mathematics. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-2030-5_17
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DOI: https://doi.org/10.1007/978-1-4612-2030-5_17
Publisher Name: Birkhäuser, Boston, MA
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