The Experts below are selected from a list of 60459 Experts worldwide ranked by ideXlab platform
L M C Buydens - One of the best experts on this subject based on the ideXlab platform.
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predictive property ranked Variable reduction with final complexity adapted models in partial least squares modeling for multiple responses
Analytical Chemistry, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:For partial least-squares regression with one response (PLS1), many Variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative Variables. Many Variable-reduction methods are based on various PLS-model-related parameters, called Predictor-Variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, Variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new Predictor-Variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet–visible (UV–vis) spectra, near-...
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predictive property ranked Variable reduction with final complexity adapted models in partial least squares modeling for multiple responses
Analytical Chemistry, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:For partial least-squares regression with one response (PLS1), many Variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative Variables. Many Variable-reduction methods are based on various PLS-model-related parameters, called Predictor-Variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, Variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new Predictor-Variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet-visible (UV-vis) spectra, near-infrared (NIR) spectra, NMR spectra, and two simulated sets, one with correlated and one with uncorrelated responses. The four properties include the mean of the absolute values as well as the norm of the PLS2 regression coefficients and their significances. The four properties were found to be applicable by the FCAM-PLS2 method for Variable reduction. The predictive abilities of models resulting from the four properties are similar. The norm of the PLS2 regression coefficients has the best selective abilities, low numbers of Variables with an informative meaning to the responses are retained. The significance of the mean of the PLS2 regression coefficients is found to be the least-selective property.
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predictive property ranked Variable reduction in partial least squares modelling with final complexity adapted models comparison of properties for ranking
Analytica Chimica Acta, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:Abstract The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative Variables. Many Variable-reduction methods are based on so-called Predictor-Variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the Variable-reduction process. Recently, a new predictive-property-ranked Variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward Variable elimination method applied on the predictive-property-ranked Variables. The Variable number is first reduced, with constant PLS1 model complexity A , until A Variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of Variables. In this study for three data sets the utility and effectiveness of six individual and nine combined Predictor-Variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the Variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a Predictor Variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from Variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative Variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are similar or better. SIG has the best selective ability of all individual and combined properties, while the predictive ability is similar. REG is faster than SIG. This means that Variable reduction with the FCAM method is preferably conducted with properties REG or SIG. The selective ability of REG can be improved by combining it with NLW or VIP.
Jan P M Andries - One of the best experts on this subject based on the ideXlab platform.
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predictive property ranked Variable reduction with final complexity adapted models in partial least squares modeling for multiple responses
Analytical Chemistry, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:For partial least-squares regression with one response (PLS1), many Variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative Variables. Many Variable-reduction methods are based on various PLS-model-related parameters, called Predictor-Variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, Variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new Predictor-Variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet–visible (UV–vis) spectra, near-...
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predictive property ranked Variable reduction with final complexity adapted models in partial least squares modeling for multiple responses
Analytical Chemistry, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:For partial least-squares regression with one response (PLS1), many Variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative Variables. Many Variable-reduction methods are based on various PLS-model-related parameters, called Predictor-Variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, Variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new Predictor-Variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet-visible (UV-vis) spectra, near-infrared (NIR) spectra, NMR spectra, and two simulated sets, one with correlated and one with uncorrelated responses. The four properties include the mean of the absolute values as well as the norm of the PLS2 regression coefficients and their significances. The four properties were found to be applicable by the FCAM-PLS2 method for Variable reduction. The predictive abilities of models resulting from the four properties are similar. The norm of the PLS2 regression coefficients has the best selective abilities, low numbers of Variables with an informative meaning to the responses are retained. The significance of the mean of the PLS2 regression coefficients is found to be the least-selective property.
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predictive property ranked Variable reduction in partial least squares modelling with final complexity adapted models comparison of properties for ranking
Analytica Chimica Acta, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:Abstract The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative Variables. Many Variable-reduction methods are based on so-called Predictor-Variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the Variable-reduction process. Recently, a new predictive-property-ranked Variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward Variable elimination method applied on the predictive-property-ranked Variables. The Variable number is first reduced, with constant PLS1 model complexity A , until A Variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of Variables. In this study for three data sets the utility and effectiveness of six individual and nine combined Predictor-Variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the Variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a Predictor Variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from Variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative Variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are similar or better. SIG has the best selective ability of all individual and combined properties, while the predictive ability is similar. REG is faster than SIG. This means that Variable reduction with the FCAM method is preferably conducted with properties REG or SIG. The selective ability of REG can be improved by combining it with NLW or VIP.
Yvan Vander Heyden - One of the best experts on this subject based on the ideXlab platform.
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predictive property ranked Variable reduction with final complexity adapted models in partial least squares modeling for multiple responses
Analytical Chemistry, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:For partial least-squares regression with one response (PLS1), many Variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative Variables. Many Variable-reduction methods are based on various PLS-model-related parameters, called Predictor-Variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, Variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new Predictor-Variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet–visible (UV–vis) spectra, near-...
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predictive property ranked Variable reduction with final complexity adapted models in partial least squares modeling for multiple responses
Analytical Chemistry, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:For partial least-squares regression with one response (PLS1), many Variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative Variables. Many Variable-reduction methods are based on various PLS-model-related parameters, called Predictor-Variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, Variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new Predictor-Variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet-visible (UV-vis) spectra, near-infrared (NIR) spectra, NMR spectra, and two simulated sets, one with correlated and one with uncorrelated responses. The four properties include the mean of the absolute values as well as the norm of the PLS2 regression coefficients and their significances. The four properties were found to be applicable by the FCAM-PLS2 method for Variable reduction. The predictive abilities of models resulting from the four properties are similar. The norm of the PLS2 regression coefficients has the best selective abilities, low numbers of Variables with an informative meaning to the responses are retained. The significance of the mean of the PLS2 regression coefficients is found to be the least-selective property.
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predictive property ranked Variable reduction in partial least squares modelling with final complexity adapted models comparison of properties for ranking
Analytica Chimica Acta, 2013Co-Authors: Jan P M Andries, Yvan Vander Heyden, L M C BuydensAbstract:Abstract The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative Variables. Many Variable-reduction methods are based on so-called Predictor-Variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the Variable-reduction process. Recently, a new predictive-property-ranked Variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward Variable elimination method applied on the predictive-property-ranked Variables. The Variable number is first reduced, with constant PLS1 model complexity A , until A Variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of Variables. In this study for three data sets the utility and effectiveness of six individual and nine combined Predictor-Variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the Variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a Predictor Variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from Variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative Variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are similar or better. SIG has the best selective ability of all individual and combined properties, while the predictive ability is similar. REG is faster than SIG. This means that Variable reduction with the FCAM method is preferably conducted with properties REG or SIG. The selective ability of REG can be improved by combining it with NLW or VIP.
Chaves, Mendeley O Data) - One of the best experts on this subject based on the ideXlab platform.
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Database on fecal glucocorticoid metabolites (fGCM) in six groups of brown howlers (Alouatta guariba clamitans) in southern Brazil
2019Co-Authors: Chaves, Mendeley O Data)Abstract:Description of this data Datasets used to run the linear mixed models (LMM) assessing the influence of the proportion of fruit in the diet (RFc), fruit exploration intensity (Bi), fruit availability index (RFa), and proportion of time devoted to moving (%mov) on fecal glucocorticoids (fGC) in six groups of brown howlers (Alouatta guariba clamitans) in southern Brazil (see full details on each Predictor Variable in Methods). The results of these analyses will be published in the following paper: Chaves, Ó.M., Fernandes, F.A., Oliveira, G.T.,Bicca-Marques, J.C. (2019). Assessing the influence of ecological, climatic, and social factors on the physiological stress of a large primate in Atlantic Forest fragment
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Database on fecal glucocorticoids (fGC) in six groups of brown howlers (Alouatta guariba clamitans) in southern Brazil
2019Co-Authors: Chaves, Mendeley O Data)Abstract:Description of this data Datasets used to run the linear mixed models (LMM) assessing the influence of the proportion of fruit in the diet (RFc), fruit exploration intensity (Bi), fruit availability index (RFa), and proportion of time devoted to moving (%mov) on fecal glucocorticoids (fGC) in six groups of brown howlers (Alouatta guariba clamitans) in southern Brazil (see full details on each Predictor Variable in Methods). The results of these analyses will be published in the following paper: Chaves, Ó.M., Fernandes, F.A., Oliveira, G.T.,Bicca-Marques, J.C. (2019). Assessing the influence of ecological, climatic, and social factors on the physiological stress of a large primate in Atlantic Forest fragment
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Database on fecal glucocorticoids (fGC) in six groups of brown howlers (Alouatta guariba clamitans) in southern Brazil
2018Co-Authors: Chaves, Mendeley O Data)Abstract:Database used to run the linear mixed models (LMM) assessing the influence of the proportion of fruit in the diet (RFc), fruit exploration intensity (Bi), fruit availability index (RFa), and proportion of time devoted to moving (%mov) on fecal glucocorticoids (fGC) in six groups of brown howlers (Alouatta guariba clamitans) in southern Brazil (see full details on each Predictor Variable in Methods).N=239 fecal samples. The results of this analyses were published in the following paper: Fernandes, F.A.,Chaves, Ó.M.,Oliveira, G.T.,Bicca-Marques, J.C. (2018).Time moving and fruit consumption, but not habitat size, influence the physiological stress of an arboreal mammal in Atlantic forest fragments. Anim. Cons
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Database on fecal glucocorticoids (fGC) in six groups of brown howlers (Alouatta guariba clamitans) in southern Brazil
2018Co-Authors: Chaves, Mendeley O Data)Abstract:Description of this data Database used to run the linear mixed models (LMM) assessing the influence of the proportion of fruit in the diet (RFc), fruit exploration intensity (Bi), fruit availability index (RFa), and proportion of time devoted to moving (%mov) on fecal glucocorticoids (fGC) in six groups of brown howlers (Alouatta guariba clamitans) in southern Brazil (see full details on each Predictor Variable in Methods).N=239 fecal samples. The results of these analyses will be published in the following paper: Fernandes, F.A.,Chaves, Ó.M.,Oliveira, G.T.,Bicca-Marques, J.C. (2019). Assessing the influence of ecological, climatic, and social factors on the physiological stress of a large primate in Atlantic forest fragments in southern Brazil
Jiwook Choi - One of the best experts on this subject based on the ideXlab platform.
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cortical integrity of the inferior alveolar canal as a Predictor of paresthesia after third molar extraction
Journal of the American Dental Association, 2010Co-Authors: Wonse Park, Jiwook ChoiAbstract:ABSTRACT Background Paresthesia is a well-known complication of extraction of mandibular third molars (MTMs). The authors evaluated the relationship between paresthesia after MTM extraction and the cortical integrity of the inferior alveolar canal (IAC) by using computed tomography (CT). Methods The authors designed a retrospective cohort study involving participants considered, on the basis of panoramic imaging, to be at high risk of experiencing injury of the inferior alveolar nerve who subsequently underwent CT imaging and extraction of the MTMs. The primary Predictor Variable was the contact relationship between the IAC and the MTM as viewed on a CT image, classified into three groups: group 1, no contact; group 2, contact between the MTM and the intact IAC cortex; group 3, contact between the MTM and the interrupted IAC cortex. The secondary Predictor Variable was the number of CT image slices showing the cortical interruption around the MTM. The outcome Variable was the presence or absence of postoperative paresthesia after MTM extraction. Results The study sample comprised 179 participants who underwent MTM extraction (a total of 259 MTMs). Their mean age was 23.6 years, and 85 (47.5 percent) were male. The overall prevalence of paresthesia was 4.2 percent (11 of 259 teeth). The prevalence of paresthesia in group 3 (involving an interrupted IAC cortex) was 11.8 percent (10 of 85 cases), while for group 2 (involving an intact IAC cortex) and group 1 (involving no contact) it was 1.0 percent (1 of 98 cases) and 0.0 percent (no cases), respectively. The frequency of nerve damage increased with the number of CT image slices showing loss of cortical integrity ( P = .043). Conclusions The results of this study indicate that loss of IAC cortical integrity is associated with an increased risk of experiencing paresthesia after MTM extraction.
