The Experts below are selected from a list of 4800 Experts worldwide ranked by ideXlab platform
Pyung Soo Kim - One of the best experts on this subject based on the ideXlab platform.
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brief a receding horizon unbiased fir filter for discrete time state space models
Automatica, 2002Co-Authors: Wook Hyun Kwon, Pyung Soo Kim, Soohee HanAbstract:This paper concerns with a new linear finite impulse response (FIR) filter called the receding horizon unbiased FIR (RHUF) filter for the state estimation in discrete-time state space models. To obtain the RHUF filter, linearity, Unbiasedness and FIR structure will be required beforehand in addition to a performance criteria of minimum variance. The RHUF filter is obtained by directly solving an optimization problem with the Unbiasedness constraint. The RHUF filter has time-invariance and deadbeat properties. The RHUF filter is represented in both a batch form and an iterative form. It is shown that the RHUF filter is equivalent to the existing receding horizon Kalman FIR (RHKF) filter whose optimality is not clear to understand. The former is more systematic and logical, while the latter is heuristic due to handling of infinite covariance of the initial state information.
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receding horizon unbiased fir filters for continuous time state space models without a priori initial state information
IEEE Transactions on Automatic Control, 2001Co-Authors: Wook Hyu Kwo, Pyung Soo KimAbstract:A receding horizon unbiased finite-impulse response filter (RHUFF) is proposed for continuous-time state space models. Linearity, Unbiasedness, finite-impulse response (FIR) structure, and independence of the initial state information will be required in advance, in addition to a performance index of minimum variance. The proposed RHUFF is obtained by directly minimizing the performance index with the Unbiasedness constraint. The proposed RHUFF is represented first in a standard FIR form and then in an iterative form. It is shown that the RHUFF is equivalent to the existing receding horizon (RH) Kalman FIR filter. The former is more systematic and logical, while the latter is heuristic due to the handling of infinite covariance of the initial state information.
Soohee Han - One of the best experts on this subject based on the ideXlab platform.
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brief a receding horizon unbiased fir filter for discrete time state space models
Automatica, 2002Co-Authors: Wook Hyun Kwon, Pyung Soo Kim, Soohee HanAbstract:This paper concerns with a new linear finite impulse response (FIR) filter called the receding horizon unbiased FIR (RHUF) filter for the state estimation in discrete-time state space models. To obtain the RHUF filter, linearity, Unbiasedness and FIR structure will be required beforehand in addition to a performance criteria of minimum variance. The RHUF filter is obtained by directly solving an optimization problem with the Unbiasedness constraint. The RHUF filter has time-invariance and deadbeat properties. The RHUF filter is represented in both a batch form and an iterative form. It is shown that the RHUF filter is equivalent to the existing receding horizon Kalman FIR (RHKF) filter whose optimality is not clear to understand. The former is more systematic and logical, while the latter is heuristic due to handling of infinite covariance of the initial state information.
Timo Wollmershäuser - One of the best experts on this subject based on the ideXlab platform.
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Evidence from the Ifo World Economic Survey
2005Co-Authors: Steffen Henzel, Timo WollmershäuserAbstract:This paper presents a new methodology for the quantification of qualitative survey data. Traditional conversion methods, such as the probability approach of Carlson and Parkin (1975) or the time-varying parameters model of Seitz (1988), require very restrictive assumptions concerning the expectations formation process of survey respondents. Above all, the Unbiasedness of expectations, which is a necessary condition for rational-ity, is imposed. Our approach avoids these assumptions. The novelty lies in the way the boundaries inside of which survey respondents expect the variable under consideration to remain unchanged are determined. Instead of deriving these boundaries from the sta-tistical properties of the reference time-series (which necessitates the Unbiasedness as-sumption), we directly queried them from survey respondents by a special question in the Ifo World Economic Survey. The new methodology is then applied to expectations about the future development of inflation obtained from the Ifo World Economic Survey
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An Alternative to the Carlson-Parkin Method for the Quantification of Qualitative Inflation Expectations: Evidence from the Ifo World Economic Survey
2024Co-Authors: Steffen Henzel, Timo WollmershäuserAbstract:This paper presents a new methodology for the quantification of qualitative survey data. Traditional conversion methods, such as the probability approach of Carlson and Parkin (1975) or the time-varying parameters model of Seitz (1988), require very restrictive assumptions concerning the expectations formation process of survey respondents. Above all, the Unbiasedness of expectations, which is a necessary condition for rationality, is imposed. Our approach avoids these assumptions. The novelty lies in the way the boundaries inside of which survey respondents expect the variable under consideration to remain unchanged are determined. Instead of deriving these boundaries from the statistical properties of the reference time-series (which necessitates the Unbiasedness assumption), we directly queried them from survey respondents by a special question in the Ifo World Economic Survey. The new methodology is then applied to expectations about the future development of inflation obtained from the Ifo World Economic Survey.Inflation expectations, survey data, quantification methods.
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Quantifying Inflation Expectations with the Carlson-Parkin Method: A Survey-based Determination of the Just Noticeable Difference
2024Co-Authors: Steffen Henzel, Timo WollmershäuserAbstract:This paper presents a new methodology for the quantification of qualitative survey data. Traditional conversion methods, such as the probability approach of Carlson and Parkin (1975) or the time-varying parameters model of Seitz (1988), require very restrictive assumptions concerning the expectations formation process of survey respondents. Above all, the Unbiasedness of expectations, which is a necessary condition for rationality, is imposed. Our approach avoids this assumptions. The novelty lies in the way the boundaries inside of which survey respondents expect the variable under consideration to remain unchanged are determined. Instead of deriving these boundaries from the statistical properties of the reference time-series (which necessitates the Unbiasedness assumption), we directly queried them from survey respondents by a special question in the Ifo World Economic Survey. The new methodology is then applied to expectations about the future development of inflation obtained from the Ifo World Economic Survey.Inflation expectations, Survey data, Quantification methods
Masashi Sugiyama - One of the best experts on this subject based on the ideXlab platform.
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covariate shift adaptation by importance weighted cross validation
Journal of Machine Learning Research, 2007Co-Authors: Masashi Sugiyama, Matthias Krauledat, Klausrobert MullerAbstract:A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase. However, this assumption is not satisfied, for example, when the outside of the training region is extrapolated. The situation where the training input points and test input points follow different distributions while the conditional distribution of output values given input points is unchanged is called the covariate shift. Under the covariate shift, standard model selection techniques such as cross validation do not work as desired since its Unbiasedness is no longer maintained. In this paper, we propose a new method called importance weighted cross validation (IWCV), for which we prove its Unbiasedness even under the covariate shift. The IWCV procedure is the only one that can be applied for unbiased classification under covariate shift, whereas alternatives to IWCV exist for regression. The usefulness of our proposed method is illustrated by simulations, and furthermore demonstrated in the brain-computer interface, where strong non-stationarity effects can be seen between training and test sessions.
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On the Influence of Input Noise on a Generalization Error Estimator Masashi Sugiyama
2007Co-Authors: Yuta Okabe Hidemitsu, Yuta Okabe, Masashi Sugiyama, Hidemitsu OgawaAbstract:Estimating the generalization capability is one of the most important problems in supervised learning. Therefore, various generalization error estimators have been proposed so far, in the presence of noise in output values. On the other hand, noise often exists in input values as well as output values. In this paper, we therefore investigate the influence of input noise on a generalization error estimator. We focus on a particular generalization error estimator called the subspace information criterion (SIC), which is shown to be unbiased in the absence of input noise. Intuitively, small input noise does not seem to affect the Unbiasedness of SIC severely because small input noise varies the output values only slightly if the learning target function is continuous. On the contrary to this intuition, we show that even small input noise can totally corrupt the Unbiasedness of SIC. This fact casts doubt on the use of SIC in the presence of input noise. To cope with this problem, we provide a sufficient condition to guarantee that SIC is unbiased in the limit of small input noise. We finally show that this condition is always fulfilled when the standard ridge estimation is used for learning, which allows us to use SIC without concern even in the presence of small input noise
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On the Influence of Input Noise on a Generalization Error Estimator
2004Co-Authors: Masashi Sugiyama, Yuta Okabe, Hidemitsu OgawaAbstract:Estimating the generalization capability is one of the most important problems in supervised learning. Therefore, various generalization error estimators have been proposed so far, in the presence of noise in output values. On the other hand, noise often exists in input values as well as output values. In this paper, we therefore investigate the influence of input noise on a generalization error estimator. We focus on a particular generalization error estimator called the subspace information criterion (SIC), which is shown to be unbiased in the absence of input noise. Intuitively, small input noise does not seem to affect the Unbiasedness of SIC severely because small input noise varies the output values only slightly if the learning target function is continuous. On the contrary to this intuition, we show that even small input noise can totally corrupt the Unbiasedness of SIC. This fact casts doubt on the use of SIC in the presence of input noise. To cope with this problem, we provide a sufficient condition to guarantee that SIC is unbiased in the limit of small input noise. We finally show that this condition is always fulfilled when the standard ridge estimation is used for learning, which allows us to use SIC without concern even in the presence of small input noise.
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LETTER Perturbation Analysis of a Generalization Error Estimator
2003Co-Authors: Masashi Sugiyama, Yuta Okabe, Hidemitsu OgawaAbstract:Abstract – Estimating the generalization capability is one of the most important problems in supervised learning. Therefore, various generalization error estimators have been proposed so far, in the presence of noise in output values. On the other hand, noise often exists in input values as well as output values. In this paper, we therefore investigate the influence of input noise on a generalization error estimator. We focus on a particular generalization error estimator called the subspace information criterion (SIC), which is shown to be unbiased in the absence of input noise. Intuitively, small input noise does not seem to affect the Unbiasedness of SIC severely because small input noise varies the output values only slightly if the learning target function is continuous. On the contrary to this intuition, we show that even small input noise can totally corrupt the Unbiasedness of SIC. This fact casts doubt on the use of SIC in the presence of input noise. To cope with this problem, we provide a sufficient condition to guarantee that SIC is unbiased in the limit of small input noise. We finally show that this condition is always fulfilled when the standard ridge estimation is used for learning, which allows us to use SIC without concern even in the presence of small input noise
Wook Hyun Kwon - One of the best experts on this subject based on the ideXlab platform.
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brief a receding horizon unbiased fir filter for discrete time state space models
Automatica, 2002Co-Authors: Wook Hyun Kwon, Pyung Soo Kim, Soohee HanAbstract:This paper concerns with a new linear finite impulse response (FIR) filter called the receding horizon unbiased FIR (RHUF) filter for the state estimation in discrete-time state space models. To obtain the RHUF filter, linearity, Unbiasedness and FIR structure will be required beforehand in addition to a performance criteria of minimum variance. The RHUF filter is obtained by directly solving an optimization problem with the Unbiasedness constraint. The RHUF filter has time-invariance and deadbeat properties. The RHUF filter is represented in both a batch form and an iterative form. It is shown that the RHUF filter is equivalent to the existing receding horizon Kalman FIR (RHKF) filter whose optimality is not clear to understand. The former is more systematic and logical, while the latter is heuristic due to handling of infinite covariance of the initial state information.
