Canonical correlation discriminant analysis in the selection of suppliers

2010 ◽  
Author(s):  
Ganglong Duan ◽  
Jianren Wang ◽  
Na Liu
Keyword(s):  
Discriminant Analysis ◽  
Canonical Correlation ◽  
Selection Of

Relationship of Specific and Nonspecific Variables to Successful Basketball Performance among High School Players

1987 ◽  
Vol 64 (3) ◽  
pp. 823-827 ◽  
Author(s):  
Mark A. Brooks ◽  
Larry W. Boleach ◽  
J. L. Mayhew
Keyword(s):  
High School ◽  
Regression Analysis ◽  
Reaction Time ◽  
Discriminant Analysis ◽  
Multiple Regression Analysis ◽  
Canonical Correlation ◽  
Vertical Jump ◽  
The Third ◽  
Male High School ◽  
Relationship Of

To determine the predictive potential of selected cognitive and psychomotor variables to estimate basketball performance, 50 male high school players from 3 schools in the same conference were evaluated. One team won the Iowa state championship; the second team had a 12 and 10 record while the third team had a 4 and 16 record. The 3 coaches rated each player's ability from 1 to 10. Multiple regression analysis to predict coaches' rating of ability from vertical jump, hand reaction time, weight, and playing experience gave an R of .76. However, discriminant analysis to classify players on the 3 teams indicated as important knowledge about basketball, dribbling, shooting accuracy, and height. The canonical correlation for the 4 variables and team membership was .64. Using the 4 variables, 60% of the players could be correctly classified to their teams.

scholarly journals Local Discriminant Hyperalignment for multi-subject fMRI data alignment

2016 ◽  
Author(s):  
Muhammad Yousefnezhad ◽  
Daoqiang Zhang
Keyword(s):  
Discriminant Analysis ◽  
Canonical Correlation ◽  
State Of The Art ◽  
Experimental Studies ◽  
Superior Performance ◽  
Fmri Data ◽  
Data Alignment ◽  
Cognitive States ◽  
Multivariate Pattern ◽  
The Brain

AbstractMultivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final results. Hyperalignment (HA) is one of the most effective functional alignment methods, which can be mathematically formulated by the Canonical Correlation Analysis (CCA) methods. Since HA mostly uses the unsupervised CCA techniques, its solution may not be optimized for MVP analysis. By incorporating the idea of Local Discriminant Analysis (LDA) into CCA, this paper proposes Local Discriminant Hyperalignment (LDHA) as a novel supervised HA method, which can provide better functional alignment for MVP analysis. Indeed, the locality is defined based on the stimuli categories in the train-set, where the correlation between all stimuli in the same category will be maximized and the correlation between distinct categories of stimuli approaches to near zero. Experimental studies on multi-subject MVP analysis confirm that the LDHA method achieves superior performance to other state-of-the-art HA algorithms.

scholarly journals Clients’ selection of auditors: evidence from the Athens stock exchange

2015 ◽  
Vol 3 (2) ◽  
pp. 156
Author(s):  
Anastasia Maggina
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Stock Exchange ◽  
Audit Fees ◽  
Statistical Technique ◽  
Financial Ratios ◽  
Institutional Factors ◽  
Data Set ◽  
Second Tier ◽  
Selection Of

<p>The auditing profession is at a crossroad worldwide. It currently faces many challenges especially in Greece where auditor rotation has been instituted as mandatory by EU regulation and the auditing profession is going to be fully liberalized (no limits on audit fees) . Given the Greek environment, it is important to investigate how client companies select auditors. In this study we address three questions. First, can selection of auditors be forecasted? Second, which statistical technique better fits the data set? Third, are there differences in firms’ financial ratios as well as institutional factors that affect auditor choice? Clients’ selection of auditors is considered in a research context using discriminant analysis and logistic regression. The discriminating factors between the two groups of companies include some firm financial ratios and institutional factors: QATA(Quick Assets/Total Assets) when using one year data, and QATA(Quick Assets/Total Assets) and SHAREHOLD (level of shareholdings) when using two year data. Prediction accuracy is close to 60.0 percent using discriminant analysis and around 80.0 percent using logistic regression. The contribution of this study is that it discriminates between the two groups of companies (Big Four versus second-tier or local auditing firms) in an IFRS environment.</p>

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