The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Feng Ding - One of the best experts on this subject based on the ideXlab platform.
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recursive least squares estimation for multivariable systems based on the maximum Likelihood Principle
International Journal of Control Automation and Systems, 2020Co-Authors: Feng Ding, Huafeng Xia, Yongqing YangAbstract:This paper studies the identification problem of multivariable controlled autoregressive moving average systems. For the case with a parameter matrix and an unmeasurable vector in the system identification model, we transform the model into several submodels based on the number of the outputs. A maximum Likelihood-based recursive least-squares algorithm is derived to identify the parameters of each submodel. A multivariable recursive extended least-squares algorithm is provided as a comparison. The effectiveness of the proposed identification algorithm is verified by simulation examples.
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maximum Likelihood recursive identification for the multivariate equation error autoregressive moving average systems using the data filtering
IEEE Access, 2019Co-Authors: Lijuan Liu, Ahmed Alsaedi, Feng Ding, Jian Pan, Tasawar HayatAbstract:The maximum Likelihood Principle has wide applications in system identification. This paper studies the maximum Likelihood identification problems of the multivariate equation-error systems with colored noise. The system is broken down into several subsystems based on the number of the outputs. The key is to transform the subsystem into a controlled autoregressive moving average model and a noise model. Based on the maximum Likelihood Principle and the data filtering technique, a filtering-based maximum Likelihood recursive generalized extended least squares algorithm is presented for estimating the parameters of these two models. For comparison, a maximum Likelihood recursive generalized extended least squares algorithm is presented. Finally, the simulation example results confirm the effectiveness of the two algorithms.
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maximum Likelihood recursive least squares estimation for multivariate equation error arma systems
Journal of The Franklin Institute-engineering and Applied Mathematics, 2018Co-Authors: Lijuan Liu, Feng Ding, Yan Wang, Cheng Wang, Tasawar HayatAbstract:Abstract This paper focuses on the parameter estimation problems of multivariate equation-error systems. A recursive generalized extended least squares algorithm is presented as a comparison. Based on the maximum Likelihood Principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification models, each of which has only a parameter vector, and a coupled subsystem maximum Likelihood recursive least squares identification algorithm is developed for estimating the parameter vectors of these submodels. The simulation example shows that the proposed algorithm is effective and has high estimation accuracy.
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data filtering based maximum Likelihood extended gradient method for multivariable systems with autoregressive moving average noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2018Co-Authors: Tasawar Hayat, Feng Ding, Feiyan Chen, Ling XuAbstract:Abstract For multivariable systems with autoregressive moving average noises, we decompose the multivariable system into m subsystems (m denotes the number of outputs) and present a maximum Likelihood generalized extended gradient algorithm and a data filtering based maximum Likelihood extended gradient algorithm to estimate the parameter vectors of these subsystems. By combining the maximum Likelihood Principle and the data filtering technique, the proposed algorithms are effective and have computational advantages over existing estimation algorithms. Finally, a numerical simulation example is given to support the developed methods and to show their effectiveness.
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the maximum Likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2017Co-Authors: Meihang Li, Feng DingAbstract:Abstract Maximum Likelihood methods are significant for parameter estimation and system modeling. This paper gives the input-output representation of a bilinear system through eliminating the state variables in it, and derives a maximum Likelihood least squares based iterative for identifying the parameters of bilinear systems with colored noises by using the maximum Likelihood Principle. A least squares based iterative (LSI) algorithm is presented for comparison. It is proved that the maximum of the Likelihood function is equivalent to minimize the least squares cost function. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems and the maximum Likelihood LSI algorithm is more accurate than the LSI algorithm.
Tasawar Hayat - One of the best experts on this subject based on the ideXlab platform.
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maximum Likelihood recursive identification for the multivariate equation error autoregressive moving average systems using the data filtering
IEEE Access, 2019Co-Authors: Lijuan Liu, Ahmed Alsaedi, Feng Ding, Jian Pan, Tasawar HayatAbstract:The maximum Likelihood Principle has wide applications in system identification. This paper studies the maximum Likelihood identification problems of the multivariate equation-error systems with colored noise. The system is broken down into several subsystems based on the number of the outputs. The key is to transform the subsystem into a controlled autoregressive moving average model and a noise model. Based on the maximum Likelihood Principle and the data filtering technique, a filtering-based maximum Likelihood recursive generalized extended least squares algorithm is presented for estimating the parameters of these two models. For comparison, a maximum Likelihood recursive generalized extended least squares algorithm is presented. Finally, the simulation example results confirm the effectiveness of the two algorithms.
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maximum Likelihood recursive least squares estimation for multivariate equation error arma systems
Journal of The Franklin Institute-engineering and Applied Mathematics, 2018Co-Authors: Lijuan Liu, Feng Ding, Yan Wang, Cheng Wang, Tasawar HayatAbstract:Abstract This paper focuses on the parameter estimation problems of multivariate equation-error systems. A recursive generalized extended least squares algorithm is presented as a comparison. Based on the maximum Likelihood Principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification models, each of which has only a parameter vector, and a coupled subsystem maximum Likelihood recursive least squares identification algorithm is developed for estimating the parameter vectors of these submodels. The simulation example shows that the proposed algorithm is effective and has high estimation accuracy.
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data filtering based maximum Likelihood extended gradient method for multivariable systems with autoregressive moving average noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2018Co-Authors: Tasawar Hayat, Feng Ding, Feiyan Chen, Ling XuAbstract:Abstract For multivariable systems with autoregressive moving average noises, we decompose the multivariable system into m subsystems (m denotes the number of outputs) and present a maximum Likelihood generalized extended gradient algorithm and a data filtering based maximum Likelihood extended gradient algorithm to estimate the parameter vectors of these subsystems. By combining the maximum Likelihood Principle and the data filtering technique, the proposed algorithms are effective and have computational advantages over existing estimation algorithms. Finally, a numerical simulation example is given to support the developed methods and to show their effectiveness.
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data filtering based multi innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum Likelihood Principle
Mathematics and Computers in Simulation, 2017Co-Authors: Feiyan Chen, Ahmed Alsaedi, Tasawar Hayat, Feng DingAbstract:This paper combines the data filtering technique with the maximum Likelihood Principle for parameter estimation of controlled autoregressive ARMA (autoregressive moving average) systems. We use an estimated noise transfer function to filter the input–output data and derive a filtering based maximum Likelihood multi-innovation extended gradient algorithm to estimate the parameters of the systems by replacing the unmeasurable variables in the information vectors with their estimates. A maximum Likelihood generalized extended gradient algorithm is given for comparison. A numerical simulation is given to support the developed methods.
Hikmet Sari - One of the best experts on this subject based on the ideXlab platform.
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a reduced complexity frequency detector derived from the maximum Likelihood Principle
IEEE Transactions on Communications, 1995Co-Authors: Georges Karam, I Jeanclaude, Hikmet SariAbstract:In addition to the conventional matched filter, implementation of Gardner's (1990) frequency detector (GFD) based on the maximum-Likelihood Principle also involves a frequency-matched filter. the present authors give a reduced-complexity frequency detector (RCFD) derived from Gardner's detector through a simple approximation to the frequency-matched filter. They analyze its steady-state jitter properties and frequency acquisition performance and compare it to the original GFD as well as to the well-known balanced quadricorrelator. The results show that performance of the RCFD is close to that of the original GFD and that this detector significantly outperforms the balanced quadricorrelator which involves a similar hardware complexity. Another contribution of the paper is the analysis of GFD and RCFD when their outputs are computed at twice the symbol rate. >
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mentation of gardner s frequency detector gfd based on the signal maximum Likelihood Principle also involves a frequency matched fd hf a reduced complexity frequency detector derived from the maximum Likelihood Principle
1995Co-Authors: Georges Karam, I Jeanclaude, Hikmet SariAbstract:filter. In this paper, we present a reduced-complexity frequency detector (RCFD) derived from Gardner’s detector through a simple approximation to the frequency-matched filter. We anaperformance and compare it to the original GFD as well as to the well-known balanced quadricorrelator. Our results show that performance of the RCFD is close to that of the original GFD and that this detector significantly outperforms the balanced lyze its steady-state jitter properties and frequency acquisition A - vco <
Deborah G Mayo - One of the best experts on this subject based on the ideXlab platform.
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rejoinder on the birnbaum argument for the strong Likelihood Principle
arXiv: Methodology, 2014Co-Authors: Deborah G MayoAbstract:Rejoinder of "On the Birnbaum Argument for the Strong Likelihood Principle" by Deborah G. Mayo [arXiv:1302.7021].
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on the birnbaum argument for the strong Likelihood Principle
Statistical Science, 2014Co-Authors: Deborah G MayoAbstract:An essential component of inference based on familiar frequentist notions, such as p-values, significance and confidence levels, is the relevant sampling distribution. This feature results in violations of a Principle known as the strong Likelihood Principle (SLP), the focus of this paper. In particular, if outcomes x* and y* from experiments E1 and E2 (both with unknown parameter θ) have different probability models f1(.), f2(.), then even though f1(x*; θ) = cf2(y*; θ) for all θ, outcomes x* and y* may have different implications for an inference about θ. Although such violations stem from considering outcomes other than the one observed, we argue this does not require us to consider experiments other than the one performed to produce the data. David Cox [Ann. Math. Statist. 29 (1958) 357.372] proposes the Weak Conditionality Principle (WCP) to justify restricting the space of relevant repetitions. The WCP says that once it is known which Ei produced the measurement, the assessment should be in terms of the properties of Ei . The surprising upshot of Allan Birnbaum's [J. Amer. Statist. Assoc. 57 (1962) 269.306] argument is that the SLP appears to follow from applying theWCP in the case of mixtures, and so uncontroversial a Principle as sufficiency (SP). But this would preclude the use of sampling distributions. The goal of this article is to provide a new clarification and critique of Birnbaum's argument. Although his argument purports that [(WCP and SP) entails SLP], we show how data may violate the SLP while holding both the WCP and SP. Such cases also refute [WCP entails SLP].
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rejoinder on the birnbaum argument for the strong Likelihood Principle
Statistical Science, 2014Co-Authors: Deborah G MayoAbstract:I am honored and grateful to have so many interesting and challenging comments on my paper. I want to thank the discussants for their willingness to jump back into the thorny quagmire of Birnbaum’s argument. To a question raised in the paper “Does it matter?”, these discussions show the answer is yes. The enlightening connections to contemporary projects are especially valuable in galvanizing future efforts to address foundational issues in statistics. As long-standing as Birnbaum’s result has been, Birnbaum himself went through dramatic shifts in a short period of time following his famous (1962) result. More than of historical interest, these shifts provide a unique perspective on the current problem. Already in the rejoinder to Birnbaum (1962), he is worried about criticisms (by Pratt, 1962) pertaining to applying WCP to his constructed mathematical mixtures (what I call Birnbaumization), and hints at replacing WCP with another Principle (Irrelevant Censoring). Then there is a gap until around 1968 at which point Birnbaum declares the SLP plausible “only in the simplest case, where the parameter space has but two” predesignated points [Birnbaum (1968), page 301]. He tells us in Birnbaum (1970a, page 1033) that he has pursued the matter thoroughly, leading to “rejection of both the Likelihood concept and various proposed formalizations of prior information.” The basis for this shift is that the SLP permits interpretations that “can be seriously misleading with high probability” [Birnbaum (1968), page 301]. He puts forward the “confidence concept” (Conf) which takes from the Neyman–Pearson (N–P) approach “techniques for systematically appraising and bounding the probabilities (under respective hypotheses) of seriously misleading interpretations of data” while supplying it an evidential interpretation [Birnbaum (1970a), page 1033].
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on the birnbaum argument for the strong Likelihood Principle
arXiv: Methodology, 2013Co-Authors: Deborah G MayoAbstract:An essential component of inference based on familiar frequentist notions, such as $p$-values, significance and confidence levels, is the relevant sampling distribution. This feature results in violations of a Principle known as the strong Likelihood Principle (SLP), the focus of this paper. In particular, if outcomes $\mathbf{x}^*$ and $\mathbf{y}^*$ from experiments $E_1$ and $E_2$ (both with unknown parameter $\theta$) have different probability models $f_1(\cdot),f_2(\cdot)$, then even though $f_1(\mathbf{x}^*;\theta)=cf_2(\mathbf{y}^*;\theta)$ for all $\theta$, outcomes $\mathbf{x}^*$ and $\mathbf{y}^*$ may have different implications for an inference about $\theta$. Although such violations stem from considering outcomes other than the one observed, we argue this does not require us to consider experiments other than the one performed to produce the data. David Cox [Ann. Math. Statist. 29 (1958) 357-372] proposes the Weak Conditionality Principle (WCP) to justify restricting the space of relevant repetitions. The WCP says that once it is known which $E_i$ produced the measurement, the assessment should be in terms of the properties of $E_i$. The surprising upshot of Allan Birnbaum's [J. Amer. Statist. Assoc. 57 (1962) 269-306] argument is that the SLP appears to follow from applying the WCP in the case of mixtures, and so uncontroversial a Principle as sufficiency (SP). But this would preclude the use of sampling distributions. The goal of this article is to provide a new clarification and critique of Birnbaum's argument. Although his argument purports that [(WCP and SP) entails SLP], we show how data may violate the SLP while holding both the WCP and SP. Such cases also refute [WCP entails SLP].
E R Podolyak - One of the best experts on this subject based on the ideXlab platform.
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programs for signal recovery from noisy data using the maximum Likelihood Principle i general description
Computer Physics Communications, 1993Co-Authors: V I Gelfgat, E L Kosarev, E R PodolyakAbstract:Abstract A program package for nonnegative signal recovery from noisy experimental data distorted by the measuring device is presented. Noise may have a Gaussian, binomial or Poissonian distribution at each experimental point. Three examples are given which demonstrate the usage of the package: (a) reduction to an arbitrary instrumental function, which depends only on the difference of its argument (this is the typical spectroscopic problem); (b) the expansion of an exponentially decaying curve which has a continuous spectrum of decrements (this problem has applications in time dependent fluorescence, nuclear physics etc.); and (c) the ultrasoft X-ray spectrum recovery from absorption measurements. These programs are based on the maximum Likelihood Principle which allows the maximum value of resolution enhancement according to Shannon's information theory to be achieved.
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programs for signal recovery from noisy data using the maximum Likelihood Principle ii program implementation
Computer Physics Communications, 1993Co-Authors: V I Gelfgat, E L Kosarev, E R PodolyakAbstract:Abstract Three subroutines intended for nonnegative signal recovery from noisy experimental data distorted by the measuring device are presented. The use of these subroutines is illustrated by a test program.