The Experts below are selected from a list of 118692 Experts worldwide ranked by ideXlab platform

Ryszard Klempous - One of the best experts on this subject based on the ideXlab platform.

  • Wavelet Amendment of Polynomial Models in Hammerstein Systems Identification
    IEEE Transactions on Automatic Control, 2009
    Co-Authors: Przemyslaw Sliwinski, Jerzy W. Rozenblit, Michael W. Marcellin, Ryszard Klempous
    Abstract:

    A new wavelet algorithm for on-line improvement of an existing Polynomial Model of nonlinearity in a Hammerstein system is proposed and its properties are examined. The algorithm employs wavelet bases on interval. Convergence of the resulting assembly, comprising the parametric Polynomial Model and a nonparametric wavelet add-on, to the system nonlinearity is shown. Rates of convergence for uniformly smooth and piecewise smooth nonlinearities with discontinuities are both established.

Peter C B Phillips - One of the best experts on this subject based on the ideXlab platform.

  • sequentially testing Polynomial Model hypotheses using power transforms of regressors
    Journal of Applied Econometrics, 2018
    Co-Authors: Jin Seo Cho, Peter C B Phillips
    Abstract:

    Summary We provide a methodology for testing a Polynomial Model hypothesis by generalizing the approach and results of Baek, Cho, and Phillips (Journal of Econometrics, 2015, 187, 376–384; BCP), which test for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We use the BCP quasi-likelihood ratio test and deal with the new multifold identification problem that arises under the null of the Polynomial Model. The approach leads to convenient asymptotic theory for inference, has omnibus power against general nonlinear alternatives, and allows estimation of an unknown Polynomial degree in a Model by way of sequential testing, a technique that is useful in the application of sieve approximations. Simulations show good performance in the sequential test procedure in both identifying and estimating unknown Polynomial order. The approach, which can be used empirically to test for misspecification, is applied to a Mincer (Journal of Political Economy, 1958, 66, 281–302; Schooling, Experience and Earnings, Columbia University Press, 1974) equation using data from Card (in Christofides, Grant, and Swidinsky (Eds.), Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp, University of Toronto Press, 1995, 201-222) and Bierens and Ginther (Empirical Economics, 2001, 26, 307–324). The results confirm that the standard Mincer log earnings equation is readily shown to be misspecified. The applications consider different datasets and examine the impact of nonlinear effects of experience and schooling on earnings, allowing for flexibility in the respective Polynomial representations.

  • sequentially testing Polynomial Model hypotheses using power transforms of regressors
    2016
    Co-Authors: Jin Seo Cho, Peter C B Phillips
    Abstract:

    We provide a methodology for testing a Polynomial Model hypothesis by extending the approach and results of Baek, Cho, and Phillips (2015; Journal of Econometrics; BCP) that tests for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We examine and generalize the BCP quasi-likelihood ratio test dealing with the multifold identification problem that arises under the null of the Polynomial Model. The approach leads to convenient asymptotic theory for inference, has omnibus power against general nonlinear alternatives, and allows estimation of an unknown Polynomial degree in a Model by way of sequential testing, a technique that is useful in the application of sieve approximations. Simulations show good performance in the sequential test procedure in identifying and estimating unknown Polynomial order. The approach, which can be used empirically to test for misspecification, is applied to a Mincer (1958, 1974) equation using data from Card (1995). The results confirm that Mincer’s log earnings equation is easily shown to be misspecified by including nonlinear effects of experience and schooling on earnings, with some flexibility required in the respective Polynomial degrees.

G T Zhou - One of the best experts on this subject based on the ideXlab platform.

  • digital baseband predistortion of nonlinear power amplifiers using orthogonal Polynomials
    International Conference on Acoustics Speech and Signal Processing, 2003
    Co-Authors: Raviv Raich, Hua Qian, G T Zhou
    Abstract:

    The Polynomial Model is commonly used in predistorter design. However, the conventional Polynomial Model exhibits numerical instabilities when high-order terms are included. We introduce a novel set of orthogonal Polynomial basis functions for predistorter Modeling. Theoretically, the conventional and the orthogonal Polynomial Models are "equivalent", and thus should have the same performance. In practice, however, the two approaches can perform quite differently in the presence of quantization noise and with finite precision processing. Simulation results show that the orthogonal Polynomials can alleviate the numerical instability problem associated with the conventional Polynomials and generally yield better predistortion linearization performance.

Jin Seo Cho - One of the best experts on this subject based on the ideXlab platform.

  • sequentially testing Polynomial Model hypotheses using power transforms of regressors
    Journal of Applied Econometrics, 2018
    Co-Authors: Jin Seo Cho, Peter C B Phillips
    Abstract:

    Summary We provide a methodology for testing a Polynomial Model hypothesis by generalizing the approach and results of Baek, Cho, and Phillips (Journal of Econometrics, 2015, 187, 376–384; BCP), which test for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We use the BCP quasi-likelihood ratio test and deal with the new multifold identification problem that arises under the null of the Polynomial Model. The approach leads to convenient asymptotic theory for inference, has omnibus power against general nonlinear alternatives, and allows estimation of an unknown Polynomial degree in a Model by way of sequential testing, a technique that is useful in the application of sieve approximations. Simulations show good performance in the sequential test procedure in both identifying and estimating unknown Polynomial order. The approach, which can be used empirically to test for misspecification, is applied to a Mincer (Journal of Political Economy, 1958, 66, 281–302; Schooling, Experience and Earnings, Columbia University Press, 1974) equation using data from Card (in Christofides, Grant, and Swidinsky (Eds.), Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp, University of Toronto Press, 1995, 201-222) and Bierens and Ginther (Empirical Economics, 2001, 26, 307–324). The results confirm that the standard Mincer log earnings equation is readily shown to be misspecified. The applications consider different datasets and examine the impact of nonlinear effects of experience and schooling on earnings, allowing for flexibility in the respective Polynomial representations.

  • sequentially testing Polynomial Model hypotheses using power transforms of regressors
    2016
    Co-Authors: Jin Seo Cho, Peter C B Phillips
    Abstract:

    We provide a methodology for testing a Polynomial Model hypothesis by extending the approach and results of Baek, Cho, and Phillips (2015; Journal of Econometrics; BCP) that tests for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We examine and generalize the BCP quasi-likelihood ratio test dealing with the multifold identification problem that arises under the null of the Polynomial Model. The approach leads to convenient asymptotic theory for inference, has omnibus power against general nonlinear alternatives, and allows estimation of an unknown Polynomial degree in a Model by way of sequential testing, a technique that is useful in the application of sieve approximations. Simulations show good performance in the sequential test procedure in identifying and estimating unknown Polynomial order. The approach, which can be used empirically to test for misspecification, is applied to a Mincer (1958, 1974) equation using data from Card (1995). The results confirm that Mincer’s log earnings equation is easily shown to be misspecified by including nonlinear effects of experience and schooling on earnings, with some flexibility required in the respective Polynomial degrees.

Przemyslaw Sliwinski - One of the best experts on this subject based on the ideXlab platform.

  • Wavelet Amendment of Polynomial Models in Hammerstein Systems Identification
    IEEE Transactions on Automatic Control, 2009
    Co-Authors: Przemyslaw Sliwinski, Jerzy W. Rozenblit, Michael W. Marcellin, Ryszard Klempous
    Abstract:

    A new wavelet algorithm for on-line improvement of an existing Polynomial Model of nonlinearity in a Hammerstein system is proposed and its properties are examined. The algorithm employs wavelet bases on interval. Convergence of the resulting assembly, comprising the parametric Polynomial Model and a nonparametric wavelet add-on, to the system nonlinearity is shown. Rates of convergence for uniformly smooth and piecewise smooth nonlinearities with discontinuities are both established.