The Experts below are selected from a list of 61278 Experts worldwide ranked by ideXlab platform
Lei Wang - One of the best experts on this subject based on the ideXlab platform.
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estimating the sea state bias of jason 2 altimeter from crossover differences by using a three dimensional Nonparametric Model
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Maofei Jiang, Yalong Liu, Lei WangAbstract:With a standard deviation as large as 2 cm, the sea state bias (SSB) has become the dominant source of error in satellite altimetry. The operational SSB correction Models are two-dimensional (2-D) empirical (parametric or Nonparametric) Models based on the altimeter-measured wind speed (U) and significant wave height (SWH). However, these 2-D SSB Models cannot entirely parameterize the range bias variability. The SSB uncertainty may be lowered through improved SSB Models including additional measurable or predictable correlatives. This paper presents a method to estimate the SSB from crossover differences by using a three-dimensional (3-D) Nonparametric Model. The Model is based on U, SWH from Jason-2 altimeter ocean observations, and the mean wave period from the European Centre for Medium-Range Weather Forecasts reanalysis project ERA-Interim (The SSB Model developed with the method presented in this paper is called “3-D SSB Model” and the SSB estimated with the 3-D SSB Model is called “3-D SSB estimate”). Simulations indicate that the wave period can greatly affect the SSB. Evaluated by the separate annual datasets from 2009 to 2011, the 3-D SSB estimates can increase the explained variance by 1.32 cm2, or 1.15-cm RMS relative to the traditional 2-D SSB estimates based on U and SWH. Spatial evaluation of improvement shows that the 3-D SSB estimates are better than the traditional 2-D SSB estimates at all latitudes. The enhancement from 2-D to 3-D SSB estimates is of great significance to improve the precision of the altimeter product.[COMP]: Please set math TYPE gin the sentence below (40) as per the authors PDF.
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IGARSS - Estimating the sea state bias of HY-2A radar altimeter by using a three-dimentional Nonparametric Model
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Maofei Jiang, Yalong Liu, Lei WangAbstract:The sea state bias (SSB) has become the dominant source of error in satellite altimetry. The operational SSB correction Models are two-dimensional (2-D) Nonparametric Models based on the wind speed (U) and the significant wave height (SWH) that can be directly measured by the altimeters. This paper estimates the sea state bias of HY-2A radar altimeter using a three-dimensional (3-D) Nonparametric Model based on SWH from HY_2A interim geophysical dataset records (IGDR), U and the mean wave period (MWP) from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis project ERA-Interim. The 3-D SSB estimates can increase the explained variance by 1.72 cm2, or 1.31 cm RMS relative to the traditional 2-D SSB estimates based on U and SWH.
Anthony Ohagan - One of the best experts on this subject based on the ideXlab platform.
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Modelling sf 6d health state preference data using a Nonparametric bayesian method
Journal of Health Economics, 2007Co-Authors: Samer A. Kharroubi, John Brazier, Jennifer Roberts, Anthony OhaganAbstract:This paper reports on the findings from applying a new approach to Modelling health state valuation data. The approach applies a Nonparametric Model to estimate SF-6D health state utility values using Bayesian methods. The data set is the UK SF-6D valuation study where a sample of 249 states defined by the SF-6D (a derivative of the SF-36) was valued by a representative sample of the UK general population using standard gamble. The paper presents the results from applying the Nonparametric Model and comparing it to the original Model estimated using a conventional parametric random effects Model. The two Models are compared theoretically and in terms of empirical performance. The paper discusses the implications of these results for future applications of the SF-6D and further work in this field.
Samer A. Kharroubi - One of the best experts on this subject based on the ideXlab platform.
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Modeling SF-6D Hong Kong Standard Gamble Health State Preference Data Using a Nonparametric Bayesian Method
Value in Health, 2013Co-Authors: Samer A. Kharroubi, John Brazier, Sarah M. McgheeAbstract:Objectives: This article reports on the findings from applying a recently described approach to Modeling health state valuation data and the impact of the respondent characteristics on health state valuations. The approach applies a Nonparametric Model to estimate a Bayesian six-dimensional health state short form (derived from short-form 36 health survey) health state valuation algorithm. Methods: A sample of 197 states defined by the six-dimensional health state short form (derived from short-form 36 health survey)has been valued by a representative sample of the Hong Kong general population by using standard gamble. The article reports the application of the Nonparametric Model and compares it to the original Model estimated by using a conventional parametric random effects Model. The two Models are compared theoretically and in terms of empirical performance. Results: Advantages of the Nonparametric Model are that it can be used to predict scores in populations with different distributions of characteristics than observed in the survey sample and that it allows for the impact of respondent characteristics to vary by health state (while ensuring that full health passes through unity). The results suggest an important age effect with sex, having some effect, but the remaining covariates having no discernible effect. Conclusions: The Nonparametric Bayesian Model is argued to be more theoretically appropriate than previously used parametric Models. Furthermore, it is more flexible to take into account the impact of covariates.
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Modeling HUI 2 Health State Preference Data Using a Nonparametric Bayesian Method
Medical Decision Making, 2008Co-Authors: Samer A. Kharroubi, Christopher MccabeAbstract:This article reports the application of a recently described approach to Modeling health state valuation data and the impact of the respondent characteristics on health state valuations. The approach applies a Nonparametric Model to estimate a Bayesian Health Utilities Index Mark 2 (HUI 2) health state valuation algorithm. The data set is the UK HUI 2 valuation study where a sample of 51 states defined by the HUI 2 was valued by a sample of the UK general population using standard gamble. The article reports the application of the Nonparametric Model and compares it to the original Model estimated using a conventional parametric random effects Model. Advantages of the Nonparametric Model are that it can be used to predict scores in populations with different distributions of characteristics than observed in the survey sample and that it allows for the impact of respondent characteristics to vary by health state. The results suggest an important age effect with sex, having some effect, but the remaining co...
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Modelling sf 6d health state preference data using a Nonparametric bayesian method
Journal of Health Economics, 2007Co-Authors: Samer A. Kharroubi, John Brazier, Jennifer Roberts, Anthony OhaganAbstract:This paper reports on the findings from applying a new approach to Modelling health state valuation data. The approach applies a Nonparametric Model to estimate SF-6D health state utility values using Bayesian methods. The data set is the UK SF-6D valuation study where a sample of 249 states defined by the SF-6D (a derivative of the SF-36) was valued by a representative sample of the UK general population using standard gamble. The paper presents the results from applying the Nonparametric Model and comparing it to the original Model estimated using a conventional parametric random effects Model. The two Models are compared theoretically and in terms of empirical performance. The paper discusses the implications of these results for future applications of the SF-6D and further work in this field.
Christian Soize - One of the best experts on this subject based on the ideXlab platform.
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Parametric and Nonparametric Models of the impedance matrix of a random medium
European Journal of Computational Mechanics, 2008Co-Authors: Régis Cottereau, Didier Clouteau, Christian SoizeAbstract:Two approaches are presented for the Modeling of the impedance matrix of a random medium: one parametric and the other Nonparametric. The former allows to take into account the data uncertainties while introducing a Model error, that yields, in some cases, very high levels. The latter is based on a much simpler, deterministic, Model, for which both data uncertainties and Model errors are accounted for. When the Model error is negligible, the parametric approach can be used for the identification of the parameters of the Nonparametric Model of the impedance matrix.
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Nonparametric Modeling of Random Uncertainties for Dynamic Response of Mistuned Bladed Disks
Journal of Engineering for Gas Turbines and Power, 2004Co-Authors: Evangéline Capiez-lernout, Christian SoizeAbstract:The random character of blade mistuning is a motivation to construct probability Models of random uncertainties. Recently, a new approach known as a Nonparametric Model of random uncertainties, based on the entropy optimization principle, was introduced for Modeling random uncertainties in linear and nonlinear elastodynamics. This paper presents an extension of this Nonparametric Model for vibration analysis of structures with cyclic geometry. In particular this probability Model allows the blade eigenfrequencies uncertainties and the blade-modal-shape uncertainties to be Modeled.
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Random uncertainties Model in dynamic substructuring using a Nonparametric probabilistic Model
JOURNAL OF ENGINEERING MECHANICS-ASCE, 2003Co-Authors: Christian Soize, H. ChebliAbstract:This paper presents a new approach, called a Nonparametric approach, for constructing a Model of random uncertainties in dynamic substructuring in order to predict the matrix-valued frequency response functions of complex structures. Such an approach allows nonhomogeneous uncertainties to be Modeled with the Nonparametric approach. The Craig-Bampton dynamic substructuring method is used. For each substructure, a Nonparametric Model of random uncertainties is introduced. This Nonparametric Model does not require identifying uncertain parameters in the reduced matrix Model of each substructure as is usually done for the parametric approach. This Nonparametric Model of random uncertainties is based on the use of a probability Model for symmetric positive-definite real random matrices using the entropy optimization principle. The theory and a numerical example are presented in the context of the finite-element method. The numerical results obtained show the efficiency of the Model proposed.
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Random matrix theory and random uncertainties Modeling
2002Co-Authors: Christian SoizeAbstract:Random matrix theory was intensively studied in the context of nuclear physics. For physical applications, the most important ensemble is the Gaussian Orthogonal Ensemble (GOE) whose elements are real symmetric random matrices with statistically independent entries and are invariant under orthogonal linear transformations. Recently, a new approach, called a Nonparametric Model of random uncertainties, has been introduced by the author for Modeling random uncertainties in vibration analysis. This approach has been developed in introducing a new ensemble of random matrices constituted of symmetric positive-definite real random matrices, called the "positive-definite" ensemble, which differs from the GOE. The first objective of this paper is to compare the GOE with the "positive-definite" ensemble of random matrices in the context of the Nonparametric approach of random uncertainties in dynamic systems for the low-frequency range. The second objective of this paper is to give a new validation for the Nonparametric Model of random uncertainties in dynamic systems in comparing, in the low-frequency range, the dynamical response of a simple system having random uncertainties Modeled by the parametric and the Nonparametric methods. It is proved that the "positive-definite" ensemble of random matrices, which has been introduced in the context of the development of this Nonparametric approach, is well adapted to the low-frequency vibration analysis, while the use of the Gaussian orthogonal ensemble (GOE) is not.
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A Nonparametric Model of random uncertainties for reduced matrix Models in structural dynamics
Probabilistic Engineering Mechanics, 2000Co-Authors: Christian SoizeAbstract:Random uncertainties in finite element Models in linear structural dynamics are usually Modeled by using parametric Models. This means that: (1) the uncertain local parameters occurring in the global mass, damping and stiffness matrices of the finite element Model have to be identified; (2) appropriate probabilistic Models of these uncertain parameters have to be constructed; and (3) functions mapping the domains of uncertain parameters into the global mass, damping and stiffness matrices have to be constructed. In the low-frequency range, a reduced matrix Model can then be constructed using the generalized coordinates associated with the structural modes corresponding to the lowest eigenfrequencies. In this paper we propose an approach for constructing a random uncertainties Model of the generalized mass, damping and stiffness matrices. This Nonparametric Model does not require identifying the uncertain local parameters and consequently, obviates construction of functions that map the domains of uncertain local parameters into the generalized mass, damping and stiffness matrices. This Nonparametric Model of random uncertainties is based on direct construction of a probabilistic Model of the generalized mass, damping and stiffness matrices, which uses only the available information constituted of the mean value of the generalized mass, damping and stiffness matrices. This paper describes the explicit construction of the theory of such a Nonparametric Model.
Maofei Jiang - One of the best experts on this subject based on the ideXlab platform.
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estimating the sea state bias of jason 2 altimeter from crossover differences by using a three dimensional Nonparametric Model
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Maofei Jiang, Yalong Liu, Lei WangAbstract:With a standard deviation as large as 2 cm, the sea state bias (SSB) has become the dominant source of error in satellite altimetry. The operational SSB correction Models are two-dimensional (2-D) empirical (parametric or Nonparametric) Models based on the altimeter-measured wind speed (U) and significant wave height (SWH). However, these 2-D SSB Models cannot entirely parameterize the range bias variability. The SSB uncertainty may be lowered through improved SSB Models including additional measurable or predictable correlatives. This paper presents a method to estimate the SSB from crossover differences by using a three-dimensional (3-D) Nonparametric Model. The Model is based on U, SWH from Jason-2 altimeter ocean observations, and the mean wave period from the European Centre for Medium-Range Weather Forecasts reanalysis project ERA-Interim (The SSB Model developed with the method presented in this paper is called “3-D SSB Model” and the SSB estimated with the 3-D SSB Model is called “3-D SSB estimate”). Simulations indicate that the wave period can greatly affect the SSB. Evaluated by the separate annual datasets from 2009 to 2011, the 3-D SSB estimates can increase the explained variance by 1.32 cm2, or 1.15-cm RMS relative to the traditional 2-D SSB estimates based on U and SWH. Spatial evaluation of improvement shows that the 3-D SSB estimates are better than the traditional 2-D SSB estimates at all latitudes. The enhancement from 2-D to 3-D SSB estimates is of great significance to improve the precision of the altimeter product.[COMP]: Please set math TYPE gin the sentence below (40) as per the authors PDF.
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IGARSS - Estimating the sea state bias of HY-2A radar altimeter by using a three-dimentional Nonparametric Model
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Maofei Jiang, Yalong Liu, Lei WangAbstract:The sea state bias (SSB) has become the dominant source of error in satellite altimetry. The operational SSB correction Models are two-dimensional (2-D) Nonparametric Models based on the wind speed (U) and the significant wave height (SWH) that can be directly measured by the altimeters. This paper estimates the sea state bias of HY-2A radar altimeter using a three-dimensional (3-D) Nonparametric Model based on SWH from HY_2A interim geophysical dataset records (IGDR), U and the mean wave period (MWP) from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis project ERA-Interim. The 3-D SSB estimates can increase the explained variance by 1.72 cm2, or 1.31 cm RMS relative to the traditional 2-D SSB estimates based on U and SWH.
