The Experts below are selected from a list of 72480 Experts worldwide ranked by ideXlab platform
C. Forbes - One of the best experts on this subject based on the ideXlab platform.
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risk factors for chronic fatigue syndrome myalgic encephalomyelitis a systematic scoping review of Multiple Predictor studies
Psychological Medicine, 2008Co-Authors: Susanne Hempel, Duncan Chambers, Anne-marie Bagnall, C. ForbesAbstract:Background The aetiology of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is still unknown. The identification of risk factors for CFS/ME is of great importance to practitioners. Method A systematic scoping review was conducted to locate studies that analysed risk factors for CFS/ME using Multiple Predictors. We searched for published and unpublished literature in 11 electronic databases, reference lists of retrieved articles and guideline stakeholder submissions in conjunction with the development of a forthcoming national UK guideline. Risk factors and findings were extracted in a concise tabular overview and studies synthesized narratively. Results Eleven studies were identified that met inclusion criteria: two case-control studies, four cohort studies, three studies combining a cohort with a case-control study design, one case-control and twin study and one cross-sectional survey. The studies looked at a variety of demographic, medical, psychological, social and environmental factors to predict the development of CFS/ME. The existing body of evidence is characterized by factors that were analysed in several studies but without replication of a significant association in more than two studies, and by studies demonstrating significant associations of specific factors that were not assessed in other studies. None of the identified factors appear suitable for the timely identification of patients at risk of developing CFS/ME within clinical practice. Conclusions Various potential risk factors for the development of CFS/ME have been assessed but definitive evidence that appears meaningful for clinicians is lacking.
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Risk factors for chronic fatigue syndrome/myalgic encephalomyelitis: a systematic scoping review of Multiple Predictor studies
Psychological medicine, 2007Co-Authors: Susanne Hempel, Duncan Chambers, Anne-marie Bagnall, C. ForbesAbstract:Background The aetiology of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is still unknown. The identification of risk factors for CFS/ME is of great importance to practitioners. Method A systematic scoping review was conducted to locate studies that analysed risk factors for CFS/ME using Multiple Predictors. We searched for published and unpublished literature in 11 electronic databases, reference lists of retrieved articles and guideline stakeholder submissions in conjunction with the development of a forthcoming national UK guideline. Risk factors and findings were extracted in a concise tabular overview and studies synthesized narratively. Results Eleven studies were identified that met inclusion criteria: two case-control studies, four cohort studies, three studies combining a cohort with a case-control study design, one case-control and twin study and one cross-sectional survey. The studies looked at a variety of demographic, medical, psychological, social and environmental factors to predict the development of CFS/ME. The existing body of evidence is characterized by factors that were analysed in several studies but without replication of a significant association in more than two studies, and by studies demonstrating significant associations of specific factors that were not assessed in other studies. None of the identified factors appear suitable for the timely identification of patients at risk of developing CFS/ME within clinical practice. Conclusions Various potential risk factors for the development of CFS/ME have been assessed but definitive evidence that appears meaningful for clinicians is lacking.
Jon Craig Helton - One of the best experts on this subject based on the ideXlab platform.
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Multiple Predictor smoothing methods for sensitivity analysis: Description of techniques
Reliability Engineering & System Safety, 2008Co-Authors: Curtis B. Storlie, Jon Craig HeltonAbstract:The use of Multiple Predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. Then, in the second and concluding part of this presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.
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Multiple Predictor smoothing methods for sensitivity analysis: Example results
Reliability Engineering & System Safety, 2008Co-Authors: Curtis B. Storlie, Jon Craig HeltonAbstract:The use of Multiple Predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.
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Multiple Predictor smoothing methods for sensitivity analysis.
2006Co-Authors: Jon Craig Helton, Curtis B. StorlieAbstract:Abstract Not Provide
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Multiple Predictor smoothing methods for sensitivity analysis.
2006Co-Authors: Jon Craig Helton, Curtis B. StorlieAbstract:The use of Multiple Predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present
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Winter Simulation Conference - Multiple Predictor smoothing methods for sensitivity analysis
Proceedings of the Winter Simulation Conference 2005., 1Co-Authors: Curtis B. Storlie, Jon Craig HeltonAbstract:The use of Multiple Predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models (GAMs), (iii) projection pursuit regression (PP/spl I.bar/REG), and (iv) recursive partitioning regression (RP/spl I.bar/REG). The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the waste isolation pilot plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or response surface regression when nonlinear relationships between model inputs and model predictions are present.
Susanne Hempel - One of the best experts on this subject based on the ideXlab platform.
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risk factors for chronic fatigue syndrome myalgic encephalomyelitis a systematic scoping review of Multiple Predictor studies
Psychological Medicine, 2008Co-Authors: Susanne Hempel, Duncan Chambers, Anne-marie Bagnall, C. ForbesAbstract:Background The aetiology of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is still unknown. The identification of risk factors for CFS/ME is of great importance to practitioners. Method A systematic scoping review was conducted to locate studies that analysed risk factors for CFS/ME using Multiple Predictors. We searched for published and unpublished literature in 11 electronic databases, reference lists of retrieved articles and guideline stakeholder submissions in conjunction with the development of a forthcoming national UK guideline. Risk factors and findings were extracted in a concise tabular overview and studies synthesized narratively. Results Eleven studies were identified that met inclusion criteria: two case-control studies, four cohort studies, three studies combining a cohort with a case-control study design, one case-control and twin study and one cross-sectional survey. The studies looked at a variety of demographic, medical, psychological, social and environmental factors to predict the development of CFS/ME. The existing body of evidence is characterized by factors that were analysed in several studies but without replication of a significant association in more than two studies, and by studies demonstrating significant associations of specific factors that were not assessed in other studies. None of the identified factors appear suitable for the timely identification of patients at risk of developing CFS/ME within clinical practice. Conclusions Various potential risk factors for the development of CFS/ME have been assessed but definitive evidence that appears meaningful for clinicians is lacking.
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Risk factors for chronic fatigue syndrome/myalgic encephalomyelitis: a systematic scoping review of Multiple Predictor studies
Psychological medicine, 2007Co-Authors: Susanne Hempel, Duncan Chambers, Anne-marie Bagnall, C. ForbesAbstract:Background The aetiology of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is still unknown. The identification of risk factors for CFS/ME is of great importance to practitioners. Method A systematic scoping review was conducted to locate studies that analysed risk factors for CFS/ME using Multiple Predictors. We searched for published and unpublished literature in 11 electronic databases, reference lists of retrieved articles and guideline stakeholder submissions in conjunction with the development of a forthcoming national UK guideline. Risk factors and findings were extracted in a concise tabular overview and studies synthesized narratively. Results Eleven studies were identified that met inclusion criteria: two case-control studies, four cohort studies, three studies combining a cohort with a case-control study design, one case-control and twin study and one cross-sectional survey. The studies looked at a variety of demographic, medical, psychological, social and environmental factors to predict the development of CFS/ME. The existing body of evidence is characterized by factors that were analysed in several studies but without replication of a significant association in more than two studies, and by studies demonstrating significant associations of specific factors that were not assessed in other studies. None of the identified factors appear suitable for the timely identification of patients at risk of developing CFS/ME within clinical practice. Conclusions Various potential risk factors for the development of CFS/ME have been assessed but definitive evidence that appears meaningful for clinicians is lacking.
Curtis B. Storlie - One of the best experts on this subject based on the ideXlab platform.
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Multiple Predictor smoothing methods for sensitivity analysis: Description of techniques
Reliability Engineering & System Safety, 2008Co-Authors: Curtis B. Storlie, Jon Craig HeltonAbstract:The use of Multiple Predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. Then, in the second and concluding part of this presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.
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Multiple Predictor smoothing methods for sensitivity analysis: Example results
Reliability Engineering & System Safety, 2008Co-Authors: Curtis B. Storlie, Jon Craig HeltonAbstract:The use of Multiple Predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.
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Multiple Predictor smoothing methods for sensitivity analysis.
2006Co-Authors: Jon Craig Helton, Curtis B. StorlieAbstract:Abstract Not Provide
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Multiple Predictor smoothing methods for sensitivity analysis.
2006Co-Authors: Jon Craig Helton, Curtis B. StorlieAbstract:The use of Multiple Predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present
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Winter Simulation Conference - Multiple Predictor smoothing methods for sensitivity analysis
Proceedings of the Winter Simulation Conference 2005., 1Co-Authors: Curtis B. Storlie, Jon Craig HeltonAbstract:The use of Multiple Predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models (GAMs), (iii) projection pursuit regression (PP/spl I.bar/REG), and (iv) recursive partitioning regression (RP/spl I.bar/REG). The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the waste isolation pilot plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or response surface regression when nonlinear relationships between model inputs and model predictions are present.
Ashish Sharma - One of the best experts on this subject based on the ideXlab platform.
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Modelling and understanding the hierarchy in a mixture of experts using Multiple catchment descriptors
Journal of Hydrology, 2013Co-Authors: Erwin Jeremiah, Lucy Marshall, Ashish SharmaAbstract:Summary Modelling with a mixture of experts represent a platform for probabilistic combination of responses from Multiple hydrologic models, thereby better expressing the uncertainty associated with the use of a single stand-alone model structure. In hydrology, the mixture of experts framework has previously been applied successfully and demonstrated to address modelling uncertainty and improving the goodness-of-fit with respect to the observed runoff ( Jeremiah et al., 2013 , Marshall et al., 2006 , Marshall et al., 2007b ). The key to successful reduction in model uncertainty through the mixture of experts architecture lies in the specification of the associated gating function. The gating function models calculate the probability of selecting each component using a range of plausible Predictor variables. The Predictors used in this function include derived or modelled indicators of the catchment state that force the different component model forms to be used. A previous article by the authors assessed the advantages of identifying Multiple Predictor variables in the gating function. The present study takes this further, by attempting to relate the gating function and associated Predictor variables to measurable catchment attributes. This is performed using data for over 50 catchments in Australia, the result being a classification of gating function complexity and formulation as a function of one or more catchment attributes. Formulation of the gating function using this classification enables users to specify the mixture of experts architecture over ungauged models, through a transposition of the model from gauged catchments via the classification proposed.
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conditional resampling of hydrologic time series using Multiple Predictor variables a k nearest neighbour approach
Advances in Water Resources, 2006Co-Authors: Rajeshwar Mehrotra, Ashish SharmaAbstract:Abstract Unlike parametric alternatives for time series generation, non-parametric approaches generate new values by conditionally resampling past observations using a probability rationale. Observations lying ‘close’ to the conditioning vector are resampled with higher probability, ‘closeness’ is defined using a Euclidean or Mahalanobis distance formulation. A common problem with these approaches is the difficulty in distinguishing the importance of each Predictor in the estimation of the distance. As a consequence, the conditional probability and hence the resampled series, can offer a biased representation of the true population it aims to simulate. This paper presents a variation of the K-nearest neighbour resampler designed for use with Multiple Predictor variables. In the modification proposed, an influence weight is assigned to each Predictor in the conditioning set with the aim of identifying nearest neighbours that represent the conditional dependence in an improved manner. The workability of the proposed modification is tested using synthetic data from known linear and non-linear models and its applicability is illustrated through an example where daily rainfall is downscaled over 15 stations near Sydney, Australia using a Predictor set consisting of selected large-scale atmospheric circulation variables.