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Holger Dette - One of the best experts on this subject based on the ideXlab platform.
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some comments on goodness of fit tests for the Parametric Form of the copula based on l2 distances
Journal of Multivariate Analysis, 2010Co-Authors: Axel Bucher, Holger DetteAbstract:In a recent paper Fermanian (2005) [9] studied a goodness-of-fit test for the Parametric Form of a copula, which is based on an L^2-distance between a Parametric and a nonParametric estimate of the copula density. In the present paper we investigate the asymptotic properties of the proposed test statistic under fixed alternatives. We also study the impact of different estimates for the parameters of the finite-dimensional family of copulas specified by the null hypothesis and illustrate the perFormance of a Parametric bootstrap procedure for the approximation of the critical values.
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a test for the Parametric Form of the variance function in a partial linear regression model
Journal of Statistical Planning and Inference, 2008Co-Authors: Holger Dette, Mareen MarchlewskiAbstract:Abstract We consider the problem of testing for a Parametric Form of the variance function in a partial linear regression model. A new test is derived, which can detect local alternatives converging to the null hypothesis at a rate n - 1 / 2 and is based on a stochastic process of the integrated variance function. We establish weak convergence to a Gaussian process under the null hypothesis, fixed and local alternatives. In the special case of testing for homoscedasticity the limiting process is a scaled Brownian bridge. We also compare the finite sample properties with a test based on an L 2 -distance, which was recently proposed by You and Chen [2005. Testing heteroscedasticity in partially linear regression models. Statist. Probab. Lett. 73, 61–70].
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a martingale transForm goodness of fit test for the Form of the conditional variance
arXiv: Statistics Theory, 2008Co-Authors: Holger Dette, Benjamin HetzlerAbstract:In the common nonParametric regression model the problem of testing for a specific Parametric Form of the variance function is considered. Recently Dette and Hetzler (2008) proposed a test statistic, which is based on an empirical process of pseudo residuals. The process converges weakly to a Gaussian process with a complicated covariance kernel depending on the data generating process. In the present paper we consider a standardized version of this process and propose a martingale transForm to obtain asymptotically distribution free tests for the corresponding Kolmogorov-Smirnov and Cram\'{e}r-von-Mises functionals. The finite sample properties of the proposed tests are investigated by means of a simulation study.
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testing the Parametric Form of the volatility in continuous time diffusion models a stochastic process approach
Journal of Econometrics, 2008Co-Authors: Holger Dette, Mark PodolskijAbstract:We present new tests for the Form of the volatility function which are based on stochastic processes of the integrated volatility. We prove weak convergence of these processes to centered processes whose conditional distributions are Gaussian. In the case of testing for a constant volatility the limiting process are standard Brownian bridges. As a consequence an asymptotic distribution free test and bootstrap tests (for testing of a general Parametric Form) can easily be implemented. It is demonstrated that the new tests are more than the currently available procedures. The new approach is also demonstrated by means of a simulation study.
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a new test for the Parametric Form of the variance function in non Parametric regression
Journal of The Royal Statistical Society Series B-statistical Methodology, 2007Co-Authors: Holger Dette, Natalie Neumeyer, Ingrid Van KeilegomAbstract:In the common non-Parametric regression model the problem of testing for the Parametric Form of the conditional variance is considered. A stochastic process based on the difference between the empirical processes that are obtained from the standardized non-Parametric residuals under the null hypothesis (of a specific Parametric Form of the variance function) and the alternative is introduced and its weak convergence established. This result is used for the construction of a Kolmogorov-Smirnov and a Cramer-von Mises type of statistic for testing the Parametric Form of the conditional variance. The consistency of a bootstrap approximation is established, and the finite sample properties of this approximation are investigated by means of a simulation study. In particular the new procedure is compared with some of the currently available methods for this problem. Copyright 2007 Royal Statistical Society.
Thang Nguyen - One of the best experts on this subject based on the ideXlab platform.
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arbitrary pole placement with the extended kautsky nichols van dooren Parametric Form
International Journal of Control, 2016Co-Authors: Robert Schmid, Lorenzo Ntogramatzidis, Thang NguyenAbstract:ABSTRACTWe consider the classic problem of pole placement by state feedback. The well-known eigenstructure assignment algorithm of Kautsky, Nichols, and van Dooren is extended to obtain a Parametric Formula for the pole-placing feedback matrix that can deliver any desired closed-loop eigenvalues, with any desired multiplicities.
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robust arbitrary pole placement with the extended kautsky nichols van dooren Parametric Form
Mediterranean Conference on Control and Automation, 2014Co-Authors: Robert Schmid, Thang Nguyen, Lorenzo NtogramatzidisAbstract:We consider the classic problem of pole placement by state feedback. Our recent work [1] offered an eigenstructure assignment algorithm to obtain a novel Parametric Form for the pole-placing gain matrix to deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. The method was adapted from the classic eigenstructure assignment algorithm of Kautsky, Nichols and van Dooren [2]. In this paper we employ this Parametric Formula to introduce an unconstrained nonlinear optimisation algorithm to obtain a gain matrix that delivers any desired pole placement with optimal robustness.
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arbitrary pole placement with the extended kautsky nichols van dooren Parametric Form with minimum gain
Advances in Computing and Communications, 2014Co-Authors: Robert Schmid, Thang Nguyen, Lorenzo NtogramatzidisAbstract:We consider the classic problem of pole placement by state feedback. We revisit the well-known eigenstructure assignment algorithm of Kautsky, Nichols and van Dooren [1] and extend it to obtain a novel Parametric Form for the pole-placing feedback matrix that can deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. This Parametric Formula is then employed to introduce an unconstrained nonlinear optimisation algorithm to obtain a feedback matrix that delivers the desired pole placement with minimum gain.
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robust pole placement with moore s algorithm
IEEE Transactions on Automatic Control, 2014Co-Authors: Robert Schmid, Amit Pandey, Thang NguyenAbstract:We consider the classic problem of pole placement by linear state feedback. We adapt the Moore eigenstructure assignment algorithm to obtain a novel Parametric Form for the pole-placing gain matrix, and introduce an unconstrained nonlinear optimization algorithm to obtain a gain matrix that will deliver robust pole placement. Numerical experiments indicate the algorithm's perFormance compares favorably against several other notable robust pole placement methods from the literature.
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robust pole placement with moore s algorithm
arXiv: Optimization and Control, 2013Co-Authors: Robert Schmid, Amit Pandey, Thang NguyenAbstract:We consider the classic problem of pole placement by state feedback. We adapt the Moore eigenstructure assignment algorithm to obtain a novel Parametric Form for the pole-placing gain matrix, and introduce an unconstrained nonlinear optimization algorithm to obtain a gain matrix that will deliver robust pole placement. Numerical experiments indicate the algorithm's perFormance compares favorably against several other notable robust pole placement methods from the literature.
Ingrid Van Keilegom - One of the best experts on this subject based on the ideXlab platform.
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goodness of fit tests for the error distribution in nonParametric regression
Computational Statistics & Data Analysis, 2010Co-Authors: Cedric Heuchenne, Ingrid Van KeilegomAbstract:Suppose the random vector (X,Y) satisfies the regression model Y=m(X)[email protected](X)@e, where m(@?) is the conditional mean, @s^2(@?) is the conditional variance, and @e is independent of X. The covariate X is d-dimensional (d>=1), the response Y is one-dimensional, and m and @s are unknown but smooth functions. Goodness-of-fit tests for the Parametric Form of the error distribution are studied under this model, without assuming any Parametric Form for m or @s. The proposed tests are based on the difference between a nonParametric estimator of the error distribution and an estimator obtained under the null hypothesis of a Parametric model. The large sample properties of the proposed test statistics are obtained, as well as those of the estimator of the parameter vector under the null hypothesis. Finally, the finite sample behavior of the proposed statistics, and the selection of the bandwidths for estimating m and @s are extensively studied via simulations.
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a new test for the Parametric Form of the variance function in non Parametric regression
Journal of The Royal Statistical Society Series B-statistical Methodology, 2007Co-Authors: Holger Dette, Natalie Neumeyer, Ingrid Van KeilegomAbstract:In the common non-Parametric regression model the problem of testing for the Parametric Form of the conditional variance is considered. A stochastic process based on the difference between the empirical processes that are obtained from the standardized non-Parametric residuals under the null hypothesis (of a specific Parametric Form of the variance function) and the alternative is introduced and its weak convergence established. This result is used for the construction of a Kolmogorov-Smimov and a Cramer-von Mises type of statistic for testing the Parametric Form of the conditional variance. The consistency of a bootstrap approximation is established, and the finite sample properties of this approximation are investigated by means of a simulation study. In particular the new procedure is compared with some of the currently available methods for this problem.
Robert Schmid - One of the best experts on this subject based on the ideXlab platform.
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arbitrary pole placement with the extended kautsky nichols van dooren Parametric Form
International Journal of Control, 2016Co-Authors: Robert Schmid, Lorenzo Ntogramatzidis, Thang NguyenAbstract:ABSTRACTWe consider the classic problem of pole placement by state feedback. The well-known eigenstructure assignment algorithm of Kautsky, Nichols, and van Dooren is extended to obtain a Parametric Formula for the pole-placing feedback matrix that can deliver any desired closed-loop eigenvalues, with any desired multiplicities.
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robust arbitrary pole placement with the extended kautsky nichols van dooren Parametric Form
Mediterranean Conference on Control and Automation, 2014Co-Authors: Robert Schmid, Thang Nguyen, Lorenzo NtogramatzidisAbstract:We consider the classic problem of pole placement by state feedback. Our recent work [1] offered an eigenstructure assignment algorithm to obtain a novel Parametric Form for the pole-placing gain matrix to deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. The method was adapted from the classic eigenstructure assignment algorithm of Kautsky, Nichols and van Dooren [2]. In this paper we employ this Parametric Formula to introduce an unconstrained nonlinear optimisation algorithm to obtain a gain matrix that delivers any desired pole placement with optimal robustness.
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arbitrary pole placement with the extended kautsky nichols van dooren Parametric Form with minimum gain
Advances in Computing and Communications, 2014Co-Authors: Robert Schmid, Thang Nguyen, Lorenzo NtogramatzidisAbstract:We consider the classic problem of pole placement by state feedback. We revisit the well-known eigenstructure assignment algorithm of Kautsky, Nichols and van Dooren [1] and extend it to obtain a novel Parametric Form for the pole-placing feedback matrix that can deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. This Parametric Formula is then employed to introduce an unconstrained nonlinear optimisation algorithm to obtain a feedback matrix that delivers the desired pole placement with minimum gain.
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robust pole placement with moore s algorithm
IEEE Transactions on Automatic Control, 2014Co-Authors: Robert Schmid, Amit Pandey, Thang NguyenAbstract:We consider the classic problem of pole placement by linear state feedback. We adapt the Moore eigenstructure assignment algorithm to obtain a novel Parametric Form for the pole-placing gain matrix, and introduce an unconstrained nonlinear optimization algorithm to obtain a gain matrix that will deliver robust pole placement. Numerical experiments indicate the algorithm's perFormance compares favorably against several other notable robust pole placement methods from the literature.
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robust pole placement with moore s algorithm
arXiv: Optimization and Control, 2013Co-Authors: Robert Schmid, Amit Pandey, Thang NguyenAbstract:We consider the classic problem of pole placement by state feedback. We adapt the Moore eigenstructure assignment algorithm to obtain a novel Parametric Form for the pole-placing gain matrix, and introduce an unconstrained nonlinear optimization algorithm to obtain a gain matrix that will deliver robust pole placement. Numerical experiments indicate the algorithm's perFormance compares favorably against several other notable robust pole placement methods from the literature.
F W Stecker - One of the best experts on this subject based on the ideXlab platform.
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erratum intergalactic photon spectra from the far ir to the uv lyman limit for 0 z 6 and the optical depth of the universe to high energy gamma rays apj 648 774 2006
The Astrophysical Journal, 2007Co-Authors: F W Stecker, M Malkan, Sean T ScullyAbstract:Table 1 in our paper had erroneous numbers for the coefficients fitting the Parametric Form for the optical depth of the universe to -rays, . The correct values for these parameters as described in the original text are given in the table below for various redshifts for the baseline model (upper row) and fast evolution (lower row) for each individual redshift. The Parametric approximation holds for 10 2 10 and E P 2 TeV for all redshifts, but also up to 10 TeV for z 1.
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intergalactic photon spectra from the far ir to the uv lyman limit for 0 z 6 and the optical depth of the universe to high energy gamma rays
The Astrophysical Journal, 2006Co-Authors: F W Stecker, M Malkan, Sean T ScullyAbstract:Table 1 in our paper had erroneous numbers for the coefficients fitting the Parametric Form for the optical depth of the universe to gamma-rays; tau. The correct values for these parameters as described in the original text are given in the table for various redshifts for the baseline model (upper row) and fast evolution (lower row) for each individual redshift.