The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Antonio Napolitano - One of the best experts on this subject based on the ideXlab platform.
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Asymptotic normality of statistical-function Estimators for generalized almost-cyclostationary processes
2005 13th European Signal Processing Conference, 2005Co-Authors: Antonio NapolitanoAbstract:The problem of estimating second-order statistical functions of generalized almost-cyclostationary (GACS) processes is addressed. The class of such nonstationary processes includes, as a special case, the almost-cyclostationary (ACS) processes. ACS processes filtered by Doppler channels and communications signals with time-varying parameters are further examples. It is shown that, for GACS processes, the cyclic correlogram is an asymptotically Normal mean-square Consistent Estimator of the cyclic autocorrelation function. Thus, well-known results for ACS processes can be obtained as a special case of the results of this paper.
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Mean-square consistency of statistical-function Estimators for generalized almost-cyclostationary processes
2004 12th European Signal Processing Conference, 2004Co-Authors: Antonio NapolitanoAbstract:In this paper, the problem of estimating second-order statistical functions for generalized almost-cyclostationary (GACS) processes is addressed. The class of such nonstationary processes includes, as a special case, the almost-cyclostationary (ACS) processes. ACS processes filtered by some linear time-variant channels are further examples. It is shown that, for GACS processes, the cyclic correlogram is a mean-square Consistent Estimator of the cyclic autocorrelation function. Moreover, well-known consistency results for ACS processes can be obtained by specializing the results of this paper.
Shun-ichi Amari - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Network Parameters in Semiparametric Stochastic Perceptron
Neural Computation, 1994Co-Authors: Motoaki Kawanabe, Shun-ichi AmariAbstract:It was reported (Kabashima and Shinomoto 1992) that Estimators of a binary decision boundary show asymptotically strange behaviors when the probability model is ill-posed or semiparametric. We give a rigorous analysis of this phenomenon in a stochastic perceptron by using the estimating function method. A stochastic perceptron consists of a neuron that is excited depending on the weighted sum of inputs but its probability distribution form is unknown here. It is shown that there exists no √n-Consistent Estimator of the threshold value h, that is, no Estimator h that converges to h in the order of 1/ √n as the number n of observations increases. Therefore, the accuracy of estimation is much worse in this semiparametric case with an unspecified probability function than in the ordinary case. On the other hand, it is shown that there is a √n-Consistent Estimator ŵ of the synaptic weight vector. These results elucidate strange behaviors of learning curves in a semiparametric statistical model.
Jeffrey K Mackiemason - One of the best experts on this subject based on the ideXlab platform.
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a simple Consistent Estimator for disturbance components in financial models
The Review of Economics and Statistics, 1990Co-Authors: James A Levinsohn, Jeffrey K MackiemasonAbstract:Many recent papers have estimated components of the disturbance term in the "market model" of equity returns. In particular, several studies of regulatory changes and other policy events have decomposed the event effects in order to allow for heterogeneity across firms. In this paper we demonstrate that the econometric method applied in some papers yields biased and inConsistent estimates of the model parameters. We demonstrate the consistency of a simple and easily-implemented alternative method.
Keiji Matsumoto - One of the best experts on this subject based on the ideXlab platform.
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The Asymptotic Efficiency of the Consistent Estimator, Berry-Uhlmann’s Curvature, and Quantum Information Geometry
Quantum Communication Computing and Measurement 2, 2020Co-Authors: Keiji MatsumotoAbstract:It is pointed out that Berry-Uhlmann’s parallelism plays key role in the first order asymptotic theory of quantum statistical estimation, and that the parallelism is closely related to Nagaoka’s quantum information geometry, which is already successfully applied to quantum estimation theory.
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the asymptotic efficiency of the Consistent Estimator berry uhlmann s curvature and quantum information geometry
2002Co-Authors: Keiji MatsumotoAbstract:It is pointed out that Berry-Uhlmann’s parallelism plays key role in the first order asymptotic theory of quantum statistical estimation, and that the parallelism is closely related to Nagaoka’s quantum information geometry, which is already successfully applied to quantum estimation theory.
Christoph Stahl - One of the best experts on this subject based on the ideXlab platform.
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a strong Consistent least squares Estimator in a linear fuzzy regression model with fuzzy parameters and fuzzy dependent variables
Fuzzy Sets and Systems, 2006Co-Authors: Christoph StahlAbstract:In this paper a linear fuzzy regression model with fuzzy independent variables and fuzzy parameters is discussed. This is an extension of the ordinary linear regressions models by integrating physical and/or epistemical vaqueness to the dependent variables and as a consequence to the parameters. Within this paper the least-squares method is used to obtain an estimate for the fuzzy parameters in a statistical sense. Furthermore, we give a statistical justification of the proposed method by proving that the extended least-squares Estimator is a strong Consistent Estimator.
