The Experts below are selected from a list of 213 Experts worldwide ranked by ideXlab platform
B. D. Ripley - One of the best experts on this subject based on the ideXlab platform.
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Exploratory Multivariate Analysis
Modern Applied Statistics with S, 2002Co-Authors: W. N. Venables, B. D. RipleyAbstract:Multivariate Analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. The datasets can be summarized by data matrices X with n rows and p columns, the rows representing the observations or cases, and the columns the variables. The matrix can be viewed either way, depending on whether the main interest is in the relationships between the cases or between the variables. Note that for consistency we represent the variables of a case by the row vector x.
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Multivariate Analysis and Pattern Recognition
Statistics and Computing, 1999Co-Authors: W. N. Venables, B. D. RipleyAbstract:Multivariate Analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. The datasets can be summarized by data matrices X with n rows and p columns, the rows representing the observations or cases, and the columns the variables. The matrix can be viewed either way, depending on whether the main interest is in the relationships between the cases or between the variables. Note that for consistency we represent the variables of a case by the row vector x.
Bertha I. Juárez - One of the best experts on this subject based on the ideXlab platform.
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quality control of mezcal combining Multivariate Analysis techniques and raman spectroscopy
International Conference on Electronics Communications and Computers, 2015Co-Authors: Miguel Ramirez G Elias, Edgar Guevara, Francisco Javier González, Cynthia Zamora Pedraza, Rogelio Aguirre, Bertha I. JuárezAbstract:A fast method to discriminate between mezcal samples with different aging times was proposed using Raman spectroscopy and Multivariate Analysis techniques. The Multivariate Analysis were performed using Principal component Analysis (PCA) and Partial least squares discriminant Analysis (PLS-DA). The first principal component separates the matured aged mezcal (rested and aged) while the second principal component separates the non-matured from the matured mezcal. PLS-DA was chosen as supervised classifier to predict the belonging of unlabeled spectra to one of aging classes. The results demonstrated that Raman spectroscopy in combination with Multivariate Analysis could be used as fast method for discrimination between matured mezcal with different aging time.
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CONIELECOMP - Quality control of mezcal combining Multivariate Analysis techniques and Raman spectroscopy
2015 International Conference on Electronics Communications and Computers (CONIELECOMP), 2015Co-Authors: Miguel G. Ramirez Elias, Edgar Guevara, Francisco Javier González, Cynthia Zamora Pedraza, Rogelio Aguirre, Bertha I. JuárezAbstract:A fast method to discriminate between mezcal samples with different aging times was proposed using Raman spectroscopy and Multivariate Analysis techniques. The Multivariate Analysis were performed using Principal component Analysis (PCA) and Partial least squares discriminant Analysis (PLS-DA). The first principal component separates the matured aged mezcal (rested and aged) while the second principal component separates the non-matured from the matured mezcal. PLS-DA was chosen as supervised classifier to predict the belonging of unlabeled spectra to one of aging classes. The results demonstrated that Raman spectroscopy in combination with Multivariate Analysis could be used as fast method for discrimination between matured mezcal with different aging time.
W. N. Venables - One of the best experts on this subject based on the ideXlab platform.
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Exploratory Multivariate Analysis
Modern Applied Statistics with S, 2002Co-Authors: W. N. Venables, B. D. RipleyAbstract:Multivariate Analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. The datasets can be summarized by data matrices X with n rows and p columns, the rows representing the observations or cases, and the columns the variables. The matrix can be viewed either way, depending on whether the main interest is in the relationships between the cases or between the variables. Note that for consistency we represent the variables of a case by the row vector x.
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Multivariate Analysis and Pattern Recognition
Statistics and Computing, 1999Co-Authors: W. N. Venables, B. D. RipleyAbstract:Multivariate Analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. The datasets can be summarized by data matrices X with n rows and p columns, the rows representing the observations or cases, and the columns the variables. The matrix can be viewed either way, depending on whether the main interest is in the relationships between the cases or between the variables. Note that for consistency we represent the variables of a case by the row vector x.
Daisuke Fujita - One of the best experts on this subject based on the ideXlab platform.
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Multivariate Analysis for scanning tunneling spectroscopy data
Applied Surface Science, 2018Co-Authors: Junsuke Yamanishi, Shigeru Iwase, Nobuyuki Ishida, Daisuke FujitaAbstract:Abstract We applied principal component Analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 × 7) surface to explore the effectiveness of Multivariate Analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 × 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS Analysis without the support of theoretical calculations. Our Analysis showed that Multivariate Analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data.
T Woodandrew - One of the best experts on this subject based on the ideXlab platform.
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Approximation of power in Multivariate Analysis
Statistics and Computing, 2005Co-Authors: W Butlerronald, T WoodandrewAbstract:We consider the calculation of power functions in classical Multivariate Analysis. In this context, power can be expressed in terms of tail probabilities of certain noncentral distributions. The ne...
