Multivariate discriminant analysis for branching classification of colonic tubular adenoma glands

2020 ◽  
Vol 98 (5) ◽  
pp. 429-440
Author(s):  
Hyekyung Lee ◽  
Jong O. Kim ◽  
Jaesool Shim ◽  
Migyung Cho
Keyword(s):  
Discriminant Analysis ◽  
Tubular Adenoma ◽  
Multivariate Discriminant Analysis ◽  
Multivariate Discriminant

Direct classification of new psychoactive substances in seized blotter papers by ATR-FTIR and multivariate discriminant analysis

2017 ◽  
Vol 133 ◽  
pp. 96-103 ◽  
Author(s):  
Leandro S.A. Pereira ◽  
Fernanda L.C. Lisboa ◽  
José Coelho Neto ◽  
Frederico N. Valladão ◽  
Marcelo M. Sena
Keyword(s):  
Discriminant Analysis ◽  
New Psychoactive Substances ◽  
Psychoactive Substances ◽  
Multivariate Discriminant Analysis ◽  
Multivariate Discriminant

Relationships between risk factors and morphological patterns of human carotid atherosclerotic plaques. A multivariate discriminant analysis

1994 ◽  
Vol 108 (1) ◽  
pp. 39-60 ◽  
Author(s):  
Luigi Giusto Spagnoli ◽  
Alessandro Mauriello ◽  
Giampiero Palmieri ◽  
Giuseppe Santeusanio ◽  
Ada Amante ◽  
...  
Keyword(s):  
Risk Factors ◽  
Discriminant Analysis ◽  
Atherosclerotic Plaques ◽  
Multivariate Discriminant Analysis ◽  
Multivariate Discriminant ◽  
Human Carotid ◽  
Morphological Patterns

scholarly journals The Asymmetric Drift of the Thick Disk Population

1995 ◽  
Vol 164 ◽  
pp. 384-384
Author(s):  
Devendra Ojha ◽  
Annie C. Robin ◽  
Olivier Bienaymé
Keyword(s):  
Discriminant Analysis ◽  
Thick Disk ◽  
Population Synthesis ◽  
Sample Surveys ◽  
Multivariate Discriminant Analysis ◽  
Multivariate Discriminant

We investigate the kinematics of the thick disk population using photometric and astrometric sample surveys towards two opposite directions at intermediate latitude. A multivariate discriminant analysis (MDA) is used to distinguish the ‘Thick Disk’ from other populations with the help of the Besançon model of population synthesis. The data constrain the asymmetric drift of the thick disk population, which is found to be 40±10 km/s and does not depend on the galactic radius.

Urine protein markers distinguish stone-forming from non-stone-forming relatives of calcium stone formers

2006 ◽  
Vol 291 (3) ◽  
pp. F530-F536 ◽  
Author(s):  
Kristin J. Bergsland ◽  
Jennifer K. Kelly ◽  
Brian J. Coe ◽  
Fredric L. Coe
Keyword(s):  
Discriminant Analysis ◽  
Stone Formation ◽  
Stone Disease ◽  
Small Subset ◽  
Protein Markers ◽  
Urine Protein ◽  
Multivariate Discriminant Analysis ◽  
Stone Formers ◽  
Calgranulin B ◽  
Multivariate Discriminant

We have investigated urine protein inhibitors of calcium oxalate crystallization to determine whether variations in these proteins are associated with kidney stone disease and whether protein measurements improve the identification of stone formers compared with conventional risk factors (RF). Using Western blotting, we studied variations in the electrophoretic mobility patterns and relative abundances of crystallization-inhibitory proteins in urine from 50 stone-forming (SF) and 50 non-stone-forming (NS) first-degree relatives of calcium SF patients, matched by gender and age. Standard urine chemistry stone risk measurements were also made. Multivariate discriminant analysis was used to test the association of these proteins with nephrolithiasis. Differences in form and abundance of several urine proteins including inter-α-trypsin inhibitor (ITI), prothrombin fragment 1 (PF1), CD59, and calgranulin B (calB) were found to be associated with stone formation. By multivariate discriminant analysis, measurements of forms of PF1, ITI, and calB in men and ITI and CD59 in women, classified 84% of men and 76% of women correctly by stone status. In contrast, standard urine chemistry RF identified only 70% of men correctly and failed to distinguish female SF from NS. Thus a small subset of protein measurements distinguished SF from NS far better than conventional RF in a population of relatives of calcium SF, illustrating the significant association of these proteins with stone disease. Variations in these proteins may serve as markers of stone disease activity or vulnerability to recurrence and may provide new insights into mechanisms of stone formation.

scholarly journals The Assessment of Client Creditworthiness Using Predictive Methods Based on Multivariate Discriminant Analysis

2017 ◽  
Vol 2 (1) ◽  
pp. 17-21
Author(s):  
Anna Siekelova ◽  
Erika Spuchlakova
Keyword(s):  
Discriminant Analysis ◽  
Trade Credit ◽  
Debt Crisis ◽  
Balance Sheet ◽  
Jel Classification ◽  
Multivariate Discriminant Analysis ◽  
Predictive Methods ◽  
European Economies ◽  
Brighter Future ◽  
Multivariate Discriminant

Objective - Trade credit is the most important source of external finance for many companies. It appears on every balance sheet and represents more than 50 percent of company's short-term liabilities and a third of all company's total liabilities in OECD countries. Late payment of invoices may suffer firm's solvency. The European economies are now putting the years of financial turmoil and debt crisis behind them and several macro-economic indicators are pointing towards a brighter future. The aim of this paper is to assess creditworthiness of companies. Methodology/Technique - Assessment of client creditworthiness carried out using predictive methods based on multivariate discriminant analysis Findings - The situation in the enterprise can be characterized as stable. An enterprise that chooses this client to provide it a trade credit should also consider supplementing the predictive models by complex financial and economic analysis and review of available. If the firm provides trade credit to more clients, it is necessary to consider that the terms of trade credits may not be the same for everyone but also it is not in the power of company to approach to each client individually. Novelty - The study suggests that client groups can be created by using cluster analysis. Thus, the company may increase efficiency in the provision of trade credit. Type of Paper Review Keywords: Trade Credit; Trade Credit Receivables; Late Payment; Predictive Model; Z Score; IN 01; Taffler Model; G Index; SAF 2002. JEL Classification: E51, G21, G33.

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