The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Wei-bin Chang - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric Estimation of interaction functions for two-type pairwise interaction point processes
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: J.a. Gubner, Wei-bin ChangAbstract:Nonparametric Estimation of interaction functions for two-type pairwise interaction point processes is addressed. Such a problem is known to be challenging due to the intractable normalizing constant present in the density function. It is shown that the means of the marked interpoint distance functions embedded in the two-type pairwise interaction point process converge to the means of an inhomogeneous Poisson process. This suggests a simple and effective Nonparametric Estimation method. An example is presented to illustrate the efficacy of our method. Our results can be generalized to multitype point processes in a straightforward manner, although the notation is more involved.
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ICASSP - Nonparametric Estimation of interaction functions for two-type pairwise interaction point processes
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: J.a. Gubner, Wei-bin ChangAbstract:Nonparametric Estimation of interaction functions for two-type pairwise interaction point processes is addressed. Such a problem is known to be challenging due to the intractable normalizing constant present in the density function. It is shown that the means of the marked interpoint distance functions embedded in the two-type pairwise interaction point process converge to the means of an inhomogeneous Poisson process. This suggests a simple and effective Nonparametric Estimation method. An example is presented to illustrate the efficacy of our method. Our results can be generalized to multitype point processes in a straightforward manner, although the notation is more involved.
J.a. Gubner - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric Estimation of interaction functions for two-type pairwise interaction point processes
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: J.a. Gubner, Wei-bin ChangAbstract:Nonparametric Estimation of interaction functions for two-type pairwise interaction point processes is addressed. Such a problem is known to be challenging due to the intractable normalizing constant present in the density function. It is shown that the means of the marked interpoint distance functions embedded in the two-type pairwise interaction point process converge to the means of an inhomogeneous Poisson process. This suggests a simple and effective Nonparametric Estimation method. An example is presented to illustrate the efficacy of our method. Our results can be generalized to multitype point processes in a straightforward manner, although the notation is more involved.
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ICASSP - Nonparametric Estimation of interaction functions for two-type pairwise interaction point processes
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: J.a. Gubner, Wei-bin ChangAbstract:Nonparametric Estimation of interaction functions for two-type pairwise interaction point processes is addressed. Such a problem is known to be challenging due to the intractable normalizing constant present in the density function. It is shown that the means of the marked interpoint distance functions embedded in the two-type pairwise interaction point process converge to the means of an inhomogeneous Poisson process. This suggests a simple and effective Nonparametric Estimation method. An example is presented to illustrate the efficacy of our method. Our results can be generalized to multitype point processes in a straightforward manner, although the notation is more involved.
M. S. Ermakov - One of the best experts on this subject based on the ideXlab platform.
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Minimax Nonparametric Estimation on Maxisets
Journal of Mathematical Sciences, 2020Co-Authors: M. S. ErmakovAbstract:We study Nonparametric Estimation of a signal in Gaussian white noise on maxisets. We point out minimax estimators in the class of all linear estimators and strong asymptotically minimax estimators in the class of all estimators. We show that balls in Sobolev spaces are maxisets for the Pinsker estimators.
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On minimax Nonparametric Estimation of signal in Gaussian noise
arXiv: Statistics Theory, 2017Co-Authors: M. S. ErmakovAbstract:For the problem of Nonparametric Estimation of signal in Gaussian noise we point out the strong asymptotically minimax estimators on maxisets for linear estimators (see \cite{ker93,rio}). It turns out that the order of rates of convergence of Pinsker estimator on this maxisets is worse than the order of rates of convergence for the class of linear estimators considered on this maxisets. We show that balls in Sobolev spaces are maxisets for Pinsker estimators.
Li Gang - One of the best experts on this subject based on the ideXlab platform.
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THE Nonparametric Estimation OF THE NEXT FAILURE TIME
Applied Mathematics and Mechanics-english Edition, 1997Co-Authors: Li GangAbstract:The Nonparametric Estimation of the next failure time is considered in this paper. The estimator given in the paper has a.s. convergence under some proper conditions. The asymptotic normality of the estimator is also discussed.
W. Greblicki - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric Estimation of a Class of Nonlinear Time Series Models
Nonparametric Functional Estimation and Related Topics, 1991Co-Authors: M. Pawlak, W. GreblickiAbstract:The problem of Estimation of nonlinear time series models which are a composition of nonlinear elements and linear stochastic processes is considered. The compositions studied include the cascade and parallel connections. The problem of Nonparametric Estimation of underlying nonlinearities is examined. It is resolved by solving Fredholm’s integral equations of the second kind arising in the Estimation problem. As a result, the Nonparametric orthogonal series estimates of nonlinearities are derived and their asymptotic as well as some small sample properties are established.
