A new statistic in the one-way multivariate analysis of variance

Nath, Ravinder; Pavur, Robert

doi:10.1016/0167-9473(85)90003-9

Cited by 38 publications

(34 citation statements)

References 15 publications

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“…The classical Wilks' Lambda statistic was used as a significance test for the equality of the group means in MANOVA (Nath and Pavur, 1985). According to the results (Wilks' lambda = 0.3141, pvalue = 0.00) four seasons are significantly different with regard to all water quality parameters.…”

Section: Seasonal Variations Of River Water Qualitymentioning

confidence: 99%

Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

Garizi

Sheikh

Sadoddin

2011

Int. J. Environ. Sci. Technol.

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ABSTRACT:Water pollution has become a growing threat to human society and natural ecosystems in the recent decades. Assessment of seasonal changes in water quality is important for evaluating temporal variations of river pollution. In this study, seasonal variations of chemical characteristics of surface water for the Chehelchay watershed in northeast of Iran was investigated. Various multivariate statistical techniques, including multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis were applied to analyze river water quality data set containing 12 parameters recorded during 13 years within 1995-2008. The results showed that river water quality has significant seasonal changes. Discriminant analysis identified most important parameters contributing to seasonal variations of river water quality. The analysis rendered a dramatic data reduction using only five parameters: electrical conductivity, chloride, bicarbonate, sulfate and hardness, which correctly assigned 70.2 % of the observations to their respective seasonal groups. Principal component analysis / factor analysis assisted to recognize the factors or origins responsible for seasonal water quality variations. It was determined that in each season more than 80 % of the total variance is explained by three latent factors standing for salinity, weathering-related processes and alkalinity, respectively. Generally, the analysis of water quality data revealed that the Chehelchay River water chemistry is strongly affected by rock water interaction, hydrologic processes and anthropogenic activities. This study demonstrates the usefulness of multivariate statistical approaches for analysis and interpretation of water quality data, identification of pollution sources and understanding of temporal variations in water quality for effective river water quality management.

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Section: Seasonal Variations Of River Water Qualitymentioning

confidence: 99%

Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

Garizi

Sheikh

Sadoddin

2011

Int. J. Environ. Sci. Technol.

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“…Some studies have investigated variable selection based on, for example, Wilks' Lambda [23] and a method of eliminating weight for unnecessary units in the NN during network learning [24]. However, no studies have proposed the application of these methods for optimal channel selection.…”

Section: Introductionmentioning

confidence: 99%

A Quasi-Optimal Channel Selection Method for Bioelectric Signal Classification Using a Partial Kullback–Leibler Information Measure

Shibanoki

Shima

Tsuji

et al. 2013

IEEE Trans. Biomed. Eng.

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This paper proposes a novel variable selection method involving the use of a newly defined metric called the partial Kullback-Leibler (KL) information measure to evaluate the contribution of each variable (dimension) in the data. In this method, the probability density functions of recorded data are estimated through a multidimensional probabilistic neural network trained on the basis of KL information theory. The partial KL information measure is then defined as the ratio of the values before and after dimension elimination in the data. The effective dimensions for classification can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to channel selection with nine subjects (including an amputee), and effective channels were selected from all channels attached to each subject's forearm. The results showed that the number of channels was reduced by 54.3 ±19.1%, and the average classification rate for evaluation data using selected three or four channels was 96.6 ±2.8% (min: 92.1%, max: 100%). These outcomes indicate that the proposed method can be used to select effective channels (optimal or quasi-optimal) for accurate classification.

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“…In this way, accurate estimation of data distribution enables accurate classification of data. The KL information represents the difference between two probability distributions [13], and nears zero as the estimated distribution comes closer to the true distribution. It is possible to evaluate classes (for classification or not) using the estimated distribution.…”

Section: A Partial Kl Information Measurementioning

confidence: 99%

A class selection method based on a partial Kullback-Leibler information measure for biological signal classification

Shibanoki

Shima

Takaki

et al. 2010

2010 IEEE/SICE International Symposium on System Integration

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Abstract-This paper proposes a novel class selection method based on the Kullback-Leibler (KL) information measure and outlines its application to optimal motion selection for bioelectric signal classification. When a user has no experience of controlling devices using bioelectric signals, for instance controlling a prosthetic hand using EMG signals, it is well known that voluntary generation of such signals might be difficult, so that the classification issue of multiple motions thus becomes problematic as the number of motions increases. An effective selection method for motions (classes) is needed for accurate classification. In the proposed method, the probability density functions (pdfs) of measured data are estimated through learning involving a multidimensional probabilistic neural network (PNN) based on the KL information theory. A partial KL information measure is then defined to evaluate the contribution of each class for classification. Effective classes can be selected by eliminating ineffective ones based on the partial KL information one by one.In the experiments performed, the proposed method was applied to motion selection with three subjects, and effective classes were selected from all motions measured in advance. The average classification rate using selected motions under the proposed method was 92.5 ± 0.9 %. These outcomes indicate that the proposed method can be used to select effective motions for accurate classification.

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A new statistic in the one-way multivariate analysis of variance

Cited by 38 publications

References 15 publications

Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

A Quasi-Optimal Channel Selection Method for Bioelectric Signal Classification Using a Partial Kullback–Leibler Information Measure

A class selection method based on a partial Kullback-Leibler information measure for biological signal classification

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