A consistent variable selection method in high-dimensional canonical discriminant analysis

Oda, Ryoya; Suzuki, Yuya; Yanagihara, Hirokazu; Fujikoshi, Yasunori

doi:10.1016/j.jmva.2019.104561

Cited by 14 publications

(4 citation statements)

References 18 publications

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“…Multivariate statistical analysis is a branch developed from classical statistics and is a comprehensive analysis method [ 11 , 12 ]. It can analyze the statistical laws of multiple objects and multiple indicators when they are related to each other, including cluster analysis (CA) [ 13 ], discriminant analysis (DA) [ 14 ], principal component analysis (PCA) [ 15 ] and factor analysis (FA) [ 16 ]. Multivariate statistical analysis is a suitable tool for multi-component chemical and physical measurements for meaningful data reduction and interpretation [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

et al. 2021

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Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

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Section: Introductionmentioning

confidence: 99%

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

et al. 2021

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“…The calculations for CDA required in this paper were performed using SAS PROC CANDISC (version 9.4; SAS Institute Inc., Cary, NC, USA). CDA obtains linear combinations of the quantitative variables that emphasize the differences among the groups (Ares et al, 2022;Jobson, 1991;Oda et al, 2020). To determine how many canonical functions must be used in the CDA, one should consider the possible proportion of accumulated variability explained by the canonical functions, at least 90%.…”

Section: Discussionmentioning

confidence: 99%

Mineral composition of bee pollen and its relationship with botanical origin and harvesting period

Valverde

Tapia

Pérez‐Sanz

et al. 2023

Journal of Food Composition and Analysis

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“…Where α=1 is the depth of poverty index and =1 is the severity of poverty index) (Badan Pusat Statistik Provinsi Bengkulu., 2019); and 2) Discriminant Analysis Method, used to determine and analyse which economic variables are significant or insignificant at α = .05, seen from their influence in differentiating the level of poverty (poor or nonpoor) that occurs in rural fishermen's communities (Fajrianti et al, 2019;Dakwah & Sutanto, 2020;Oda et al, 2020). The Qualitative Analysis Method (The Descriptive Qualitative Analysis Method), used to reveal and analyse the ability of social variables (non-metric variables) that cannot be analysed through quantitative analysis methods, namely to explain their effect on the level of poverty (poor or not poor) that occurs in rural fishing communities.…”

Section: Methodsmentioning

confidence: 99%

Fishermen’s poverty reduction based on integrated marketing system

Ridwan

2022

ajcd

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Poor small fishing communities in rural areas are always faced with a detrimental marketing system trap for marine fish catches. This study aims to reveal the characteristics of fishermen's poverty (poverty depth and severity index), detrimental marketing systems in poor fishing communities, the factors that determine them, and to build a qualitative model of an integrated (mutually beneficial) marketing system to reduce fishermen’s poverty in rural areas. This research is a case study by setting the North Bengkulu Regency, Bengkulu Province, as the sample location. Data collection is done by interview, focus group discussion, observation, and documentation. The results showed that fishermen's poverty's depth and severity index in the sample villages were 0.645 and 0.257 (both of which had quite high scores). Quantitative analysis shows that the depth and severity of fishermen's poverty are determined by the quality of the marketing system (detrimental or profitable) and the quality of capital (captures facilities). Qualitative analysis shows that fishermen's poverty is determined by the cooperation factor, the role of cooperative institutions, the role of the government, and the role of universities. The qualitative model that can be built is an integrated marketing system model (improvement of a detrimental marketing system to be profitable for fishing communities).

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A consistent variable selection method in high-dimensional canonical discriminant analysis

Cited by 14 publications

References 18 publications

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

Mineral composition of bee pollen and its relationship with botanical origin and harvesting period

Fishermen’s poverty reduction based on integrated marketing system

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