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109
A general method for constructing pseudoGaussian tests
 J. Japan Statist. Soc
, 2008
"... A general method for constructing pseudoGaussian tests—reducing to traditional Gaussian tests under Gaussian densities but remaining valid under nonGaussian ones—is proposed. This method provides a solution to several open problems in classical multivariate analysis. One of them is the test of t ..."
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Cited by 9 (4 self)
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A general method for constructing pseudoGaussian tests—reducing to traditional Gaussian tests under Gaussian densities but remaining valid under nonGaussian ones—is proposed. This method provides a solution to several open problems in classical multivariate analysis. One of them is the test
PseudoGaussian tests for common principal components
, 2008
"... The socalled Common Principal Components (CPC) Model, in which the covariance matrices Σi of m populations are assumed to have identical eigenvectors, was introduced by Flury (1984). While Gaussian parametric inference methods (Gaussian maximum likelihood estimation; Gaussian likelihood ratio testi ..."
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Cited by 4 (4 self)
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testing) are fully developed for this model, relatively little has been done to extend their validity beyond the Gaussian case. In this paper, we show how Flury (1984)’s Gaussian likelihood ratio test (LRT) for the hypothesis of CPC can be modified into a pseudoGaussian test which remains valid under
Davy PAINDAVEINEA general Method for Constructing PseudoGaussian Tests
, 2007
"... A general method for constructing pseudoGaussian tests—reducing to traditional Gaussian tests under Gaussian densities but remaining valid under nonGaussian ones—is proposed. This method provides a solution to several open problems in classical multivariate analysis. One of them is the test of hom ..."
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A general method for constructing pseudoGaussian tests—reducing to traditional Gaussian tests under Gaussian densities but remaining valid under nonGaussian ones—is proposed. This method provides a solution to several open problems in classical multivariate analysis. One of them is the test
A Chernoff–Savage result for shape. On the nonadmissibility of pseudoGaussian methods
 J. Multivariate Anal
, 2006
"... Chernoff and Savage (1958) established that, in the context of univariate location models, Gaussianscore rankbased procedures uniformly dominate—in terms of Pitman asymptotic relative efficiencies—their pseudoGaussian parametric counterparts. This result, which had quite an impact on the success ..."
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Cited by 10 (8 self)
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distribution. The Pitman nonadmissibility of the pseudoGaussian maximum likelihood estimator for shape and that of the pseudoGaussian likehood ratio test of sphericity follow.
Sparse Gaussian processes using pseudoinputs
 Advances in Neural Information Processing Systems 18
, 2006
"... We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudoinput points, which we learn by a gradient based optimization. We take M ≪ N, where N is the number of real data points, and hence obtain a sparse regression method which has O( ..."
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Cited by 229 (13 self)
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We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudoinput points, which we learn by a gradient based optimization. We take M ≪ N, where N is the number of real data points, and hence obtain a sparse regression method which has O
EROIC: a BiCMOS pseudogaussian shaping amplifier for highresolution Xray spectroscopy
, 2003
"... Abstract We present the design and complete characterization of a fifthorder pseudogaussian shaping amplifier with 1 ms shaping time. The circuit is optimized for the readout of signals coming from Silicon Drift Detectors for highresolution Xray spectroscopy. The novelty of the designed chip st ..."
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Abstract We present the design and complete characterization of a fifthorder pseudogaussian shaping amplifier with 1 ms shaping time. The circuit is optimized for the readout of signals coming from Silicon Drift Detectors for highresolution Xray spectroscopy. The novelty of the designed chip
in Heterokurtic Elliptical Common Principal Components Models
"... The socalled Common Principal Components (CPC) Model, in which the covariance matrices Σi of m populations are assumed to have identical eigenvectors, was introduced by Flury (1984), who develops Gaussian parametric inference methods for this model (Gaussian maximum likelihood estimation and Gaussi ..."
RankBased Optimal Tests for Random Effects in Panel Data
"... cCentER, Tilburg University, and dDépartement de Mathématique, Universite ́ Libre de Bruxelles. We consider the problem of detecting unobserved heterogeneity, that is, the problem of testing the absence of random individual effects in a n × T panel. We establish a local asymptotic normality proper ..."
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property (with respect to intercept, regression coefficient, scale parameter σ2, and the parameter of interest σ2u, for fixed density f1), when n tend to infinity and T is fixed. This result allows for developing asymptotically optimal parametric procedures for σ2u under specified densitiesf1. The pseudoGaussian
Davy PAINDAVEINERankBased Optimal Tests for Random Effects in Panel Data
"... We consider the problem of detecting unobserved heterogeneity, that is, the problem of testing the absence of random individual effects in a n × T panel. We establish a local asymptotic normality property (with respect to intercept, regression coefficient, scale parameter σ2, and the parameter of in ..."
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of interest σ2 u, for fixed density f1), when n tend to infinity and T is fixed. This result allows for developing asymptotically optimal parametric procedures for σ2 u under specified densitiesf1. The pseudoGaussian tests (optimal under Gaussian densities but valid under nonGaussian ones) are investigated
Optimal tests for homogeneity of covariance, scale, and shape
 J. Multivariate Anal
, 2008
"... The assumption of homogeneity of covariance matrices is the fundamental prerequisite of a number of classical procedures in multivariate analysis. Despite its importance and long history, however, this problem so far has not been completely settled beyond the traditional and highly unrealistic cont ..."
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Cited by 8 (5 self)
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specified mtuple of radial densities f = (f1,..., fm). Combined with an estimation of the m densities f1,..., fm, these procedures can be used to construct adaptive tests for the problem. Adaptive tests however typically require very large samples, and pseudoGaussian tests—namely, tests that are locally
Results 1  10
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109