The Experts below are selected from a list of 84 Experts worldwide ranked by ideXlab platform
Patrick J. Wolfe - One of the best experts on this subject based on the ideXlab platform.
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A Nonparametric Test for stationarity based on local Fourier analysis
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Prabahan Basu, Daniel Rudoy, Patrick J. WolfeAbstract:In this paper we propose a Nonparametric Hypothesis Test for stationarity based on local Fourier analysis. We employ a Test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. For the case of a white Gaussian noise process, we characterize the asymptotic distribution of this statistic under the null Hypothesis of stationarity, and use it to directly set Test thresholds corresponding to constant false alarm rates. For other cases, we introduce a simple procedure to simulate from the null distribution of interest. After validating the procedure on synthetic examples, we demonstrate one potential use for the Test as a method of obtaining a signal-adaptive means of local Fourier analysis and corresponding signal enhancement scheme.
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ICASSP - A Nonparametric Test for stationarity based on local Fourier analysis
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Prabahan Basu, Daniel Rudoy, Patrick J. WolfeAbstract:In this paper we propose a Nonparametric Hypothesis Test for stationarity based on local Fourier analysis. We employ a Test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. For the case of a white Gaussian noise process, we characterize the asymptotic distribution of this statistic under the null Hypothesis of stationarity, and use it to directly set Test thresholds corresponding to constant false alarm rates. For other cases, we introduce a simple procedure to simulate from the null distribution of interest. After validating the procedure on synthetic examples, we demonstrate one potential use for the Test as a method of obtaining a signal-adaptive means of local Fourier analysis and corresponding signal enhancement scheme.
Prabahan Basu - One of the best experts on this subject based on the ideXlab platform.
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A Nonparametric Test for stationarity based on local Fourier analysis
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Prabahan Basu, Daniel Rudoy, Patrick J. WolfeAbstract:In this paper we propose a Nonparametric Hypothesis Test for stationarity based on local Fourier analysis. We employ a Test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. For the case of a white Gaussian noise process, we characterize the asymptotic distribution of this statistic under the null Hypothesis of stationarity, and use it to directly set Test thresholds corresponding to constant false alarm rates. For other cases, we introduce a simple procedure to simulate from the null distribution of interest. After validating the procedure on synthetic examples, we demonstrate one potential use for the Test as a method of obtaining a signal-adaptive means of local Fourier analysis and corresponding signal enhancement scheme.
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ICASSP - A Nonparametric Test for stationarity based on local Fourier analysis
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Prabahan Basu, Daniel Rudoy, Patrick J. WolfeAbstract:In this paper we propose a Nonparametric Hypothesis Test for stationarity based on local Fourier analysis. We employ a Test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. For the case of a white Gaussian noise process, we characterize the asymptotic distribution of this statistic under the null Hypothesis of stationarity, and use it to directly set Test thresholds corresponding to constant false alarm rates. For other cases, we introduce a simple procedure to simulate from the null distribution of interest. After validating the procedure on synthetic examples, we demonstrate one potential use for the Test as a method of obtaining a signal-adaptive means of local Fourier analysis and corresponding signal enhancement scheme.
Daniel Rudoy - One of the best experts on this subject based on the ideXlab platform.
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A Nonparametric Test for stationarity based on local Fourier analysis
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Prabahan Basu, Daniel Rudoy, Patrick J. WolfeAbstract:In this paper we propose a Nonparametric Hypothesis Test for stationarity based on local Fourier analysis. We employ a Test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. For the case of a white Gaussian noise process, we characterize the asymptotic distribution of this statistic under the null Hypothesis of stationarity, and use it to directly set Test thresholds corresponding to constant false alarm rates. For other cases, we introduce a simple procedure to simulate from the null distribution of interest. After validating the procedure on synthetic examples, we demonstrate one potential use for the Test as a method of obtaining a signal-adaptive means of local Fourier analysis and corresponding signal enhancement scheme.
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ICASSP - A Nonparametric Test for stationarity based on local Fourier analysis
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Prabahan Basu, Daniel Rudoy, Patrick J. WolfeAbstract:In this paper we propose a Nonparametric Hypothesis Test for stationarity based on local Fourier analysis. We employ a Test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. For the case of a white Gaussian noise process, we characterize the asymptotic distribution of this statistic under the null Hypothesis of stationarity, and use it to directly set Test thresholds corresponding to constant false alarm rates. For other cases, we introduce a simple procedure to simulate from the null distribution of interest. After validating the procedure on synthetic examples, we demonstrate one potential use for the Test as a method of obtaining a signal-adaptive means of local Fourier analysis and corresponding signal enhancement scheme.
Tugrul Temel - One of the best experts on this subject based on the ideXlab platform.
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a Nonparametric Hypothesis Test via the bootstrap resampling
MPRA Paper, 2011Co-Authors: Tugrul TemelAbstract:This paper adapts an already existing Nonparametric Hypothesis Test to the bootstrap framework. The Test utilizes the Nonparametric kernel regression method to estimate a measure of distance between the models stated under the null Hypothesis. The bootstraped version of the Test allows to approximate errors involved in the asymptotic Hypothesis Test. The paper also develops a Mathematica Code for the Test algorithm.
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a Nonparametric Hypothesis Test via the bootstrap resampling
2001 Annual meeting August 5-8 Chicago IL, 2001Co-Authors: Tugrul TemelAbstract:This paper adapts an already existing Nonparametric Hypothesis Test to the bootstrap framework. The Test utilizes the Nonparametric kernel regression method to estimate a measure of distance between the models stated under the null Hypothesis. The bootstraped version of the Test allows to approximate errors involved in the asymptotic Hypothesis Test.
Adriano Zanin Zambom - One of the best experts on this subject based on the ideXlab platform.
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a Nonparametric Hypothesis Test for heteroscedasticity in multiple regression
Canadian Journal of Statistics-revue Canadienne De Statistique, 2017Co-Authors: Adriano Zanin ZambomAbstract:This article presents a new method to Test for heteroscedasticity in a general multiple Nonparametric regression model. The Test statistic is based on a high-dimensional one-way ANOVA constructed with the absolute value of the residuals, and its asymptotic distribution is derived under the null Hypothesis of homoscedasticity and local alternative. The properties of the proposed Test statistic are preserved when a correctly specified parametric mean function is used to obtain the residuals. Unlike most methods in the literature no parametric form is required for the multivariate variance function. Extensive simulations suggest that the proposed Test detects heteroscedasticity in all models considered while classical methods fail in some cases. Two real data applications are examined. The Canadian Journal of Statistics 45: 425–441; 2017 © 2017 Statistical Society of Canada
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a Nonparametric Hypothesis Test for heteroscedasticity
Journal of Nonparametric Statistics, 2016Co-Authors: Adriano Zanin ZambomAbstract:In this paper, a Hypothesis Test for heteroscedasticity is proposed in a Nonparametric regression model. The Test statistic, which uses the residuals from a Nonparametric fit of the mean function, is based on an adaptation of the well-known Levene's Test. Using the recent theory for analysis of variance when the number of factor levels goes to infinity, the asymptotic distribution of the Test statistic is established under the null Hypothesis of homocedasticity and under local alternatives. Simulations suggest that the proposed Test performs well in several situations, especially when the variance is a nonlinear function of the predictor.
