scholarly journals Performance of Linear Discriminant Analysis Using Different Robust Methods

2018 ◽  
Vol 11 (1) ◽  
pp. 284 ◽  
Author(s):  
Mufda Jameel Alrawashdeh ◽  
Taha Radwan Radwan ◽  
Kalid Abunawas Abunawas
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Multivariate Statistical Analysis ◽  
Robust Methods ◽  
Minimum Covariance Determinant ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Misclassification Probability ◽  
Conventional Banks

This study aims to combine the new deterministic minimum covariance determinant (DetMCD) algorithm with linear discriminant analysis (LDA) and compare it with the fast minimum covariance determinant (FastMCD), fast consistent high breakdown (FCH), and robust FCH (RFCH) algorithms. LDA classifies new observations into one of the unknown groups and it is widely used in multivariate statistical analysis. The LDA mean and covariance matrix parameters are highly influenced by outliers. The DetMCD algorithm is highly robust and resistant to outliers and it is constructed to overcome the outlier problem. Moreover, the DetMCD algorithm is used to estimate location and scatter matrices. The DetMCD, FastMCD, FCH, and RFCH algorithms are applied to estimate misclassification probability using robust LDA. All the algorithms are expected to improve the LDA model for classification purposes in banks, such as bankruptcy and failures, and to distinguish between Islamic and conventional banks. The performances of the estimators are investigated through simulation and actual data.

scholarly journals Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4146
Author(s):  
José Enrique Herbert-Pucheta ◽  
José Daniel Lozada-Ramírez ◽  
Ana E. Ortega-Regules ◽  
Luis Ricardo Hernández ◽  
Cecilia Anaya de Parrodi
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Field Gradient ◽  
Multivariate Statistical Analysis ◽  
Data Matrix ◽  
Cabernet Sauvignon ◽  
Gradient Echo ◽  
Multivariate Statistical ◽  
Orthogonal Projections ◽  
Pulsed Field

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad 1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

Multivariate statistic for researchers

2016 ◽  
Author(s):  
Shamshuritawati Sharif ◽  
Hazlina Ali ◽  
Sharipah Soaad Syed Yahaya
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Valuable Resource ◽  
Multivariate Statistical ◽  
Multivariate Statistic ◽  
Linear Discriminant ◽  
New Ideas ◽  
Young Researchers

This book is a valuable resource for those engaged in multivariate statistical techniques. Most chapters include a set of problems and solution that enable readers to overcome the drawback of the classical techniques.It covers a theoretical disadvantage of correlation and covariance test, Hotellings T2 statistic, local influence, and linear discriminant analysis to inspire new or young researchers with new ideas for theoretical improvement.This book is also worthy for people who want to learn multivariate statistics extensively.

Multivariate Statistical Analysis of a Regional Subsistence-Settlement Model for Owens Valley

1979 ◽  
Vol 44 (3) ◽  
pp. 455-470 ◽  
Author(s):  
Robert L. Bettinger
Keyword(s):  
Factor Analysis ◽  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Human Ecology ◽  
Multivariate Statistical Analysis ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Owens Valley ◽  
Multivariate Statistical

Despite their growing importance in the study of prehistoric human ecology, regional subsistence-settlement models continue to be developed and justified largely on intuitive grounds. This shortcoming can be at least partially overcome by using multivariate statistical techniques to clarify and refine these models. Such an approach is illustrated using classical factor analysis and discriminant analysis to explicate and improve a regional subsistence-settlement model previously developed for Owens Valley, eastern California.

scholarly journals Depth-Based Classification for Distributions with Nonconvex Support

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Daniel Hlubinka ◽  
Ondrej Vencalek
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Problem ◽  
Data Depth ◽  
Misclassification Rate ◽  
Skewed Data ◽  
Weighted Version ◽  
Linear Discriminant ◽  
Normal Populations

Halfspace depth became a popular nonparametric tool for statistical analysis of multivariate data during the last two decades. One of applications of data depth considered recently in literature is the classification problem. The data depth approach is used instead of the linear discriminant analysis mostly to avoid the parametric assumptions and to get better classifier for data whose distribution is not elliptically symmetric, for example, skewed data. In our paper, we suggest to use weighted version of halfspace depth rather than the halfspace depth itself in order to obtain lower misclassification rate in the case of “nonconvex” distributions. Simulations show that the results of depth-based classifiers are comparable with linear discriminant analysis for two normal populations, while for nonelliptic distributions the classifier based on weighted halfspace depth outperforms both linear discriminant analysis and classifier based on the usual (nonweighted) halfspace depth.

Discrimination of Mungbean Cultivars/Varieties Based on Minor Saccharides Composition by HPLC Coupled with Multivariate Statistical Analysis

2020 ◽  
Vol 42 (3) ◽  
pp. 418-418
Author(s):  
Mudasir Majeed Mudasir Majeed ◽  
Abdullah Ijaz Hussain Abdullah Ijaz Hussain ◽  
Shahzad Ali Shahid Chatha Shahzad Ali Shahid Chatha ◽  
Ghulam Mustafa Kamal and Qasim Ali Ghulam Mustafa Kamal and Qasim Ali
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Principle Component Analysis ◽  
Multivariate Statistical Analysis ◽  
Genetic Factors ◽  
Multivariate Statistical ◽  
Clear Separation ◽  
Metabolic Variation ◽  
Potential Use

Present study reports the potential use of HPLC coupled with principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA), for differentiation of approved mungbean variety from the promising lines based on minor saccharides profiles. A total of 48 mungbean samples from one approved variety and seven promising lines were analyzed for minor saccharides using HPLC and multivariate statistical analysis. PCA showed a clear separation among the classes. PLSDA was conducted to extract the variables that were responsible for the separation of mungbean approved variety from the lines. Maltoheptaose, maltohexaose, maltopentaose, maltotretraose, maltitol, maltose, mannitole, betaine varied significantly while stachyose, raffinose, sucrose, lectitol, dulcitol, xylitol, galactose showed non-significant differences. Maltoheptaose, maltohexaose, maltotretraose, maltitol, mannitole and galactose were found as the most abundant compounds while stachyose, raffinose, sucrose, lectitol and betaine were found less abundant in all lines and approved variety of V. radiata. The study highlights metabolic variation among mungbean variety and lines for minor saccharides profiles and its usefulness for consumers to choose for their desired variety or line as well as for breeders to look into the genetic factors responsible for this variation.

On the Interpretation of Discriminant Analysis

1969 ◽  
Vol 6 (2) ◽  
pp. 156-163 ◽  
Author(s):  
Donald G. Morrison
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Computer Programs ◽  
Statistical Analyses ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Linear Discriminant

With the availability of “canned” computer programs, it is extremely easy to run complex multivariate statistical analyses. However, it is not as easy to interpret the output of these programs. This article offers some comments about the well-known technique of linear discriminant analysis; potential pitfalls are also mentioned.

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