Towards analysing student failures: neural networks compared with regression analysis and multiple discriminant analysis

1997 ◽  
Vol 24 (4) ◽  
pp. 367-377 ◽  
Author(s):  
A.M. Flitman
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Discriminant Analysis ◽  
Multiple Discriminant Analysis

scholarly journals Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

2005 ◽  
Vol 55 (4) ◽  
pp. 403-426 ◽  
Author(s):  
Miklós Virág ◽  
Tamás Kristóf
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Bankruptcy Prediction ◽  
Simulation Experiments ◽  
Prediction Techniques ◽  
Further Development

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical-statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.

scholarly journals Discriminant Analysis: Applications In Finance

2011 ◽  
Vol 5 (2) ◽  
pp. 37 ◽  
Author(s):  
Alka J. Bramhandkar
Keyword(s):  
Regression Analysis ◽  
Discriminant Analysis ◽  
Statistical Technique ◽  
Multiple Discriminant Analysis ◽  
Bond Ratings ◽  
Bond Rating ◽  
Explanatory Variables ◽  
Matched Group ◽  
Statistical Procedures

This paper begins by defining and briefly explaining what discriminant analysis is. After noting the advantages and drawbacks of this statistical technique which is very similar to the regression analysis, the paper summarizes a number of important studies reported in the finance area during the last twenty years, using this technique. Some of the interesting efforts involve prediction of corporate bankruptcies, identification of conglomerate targets, prediction of bond rating, etc. The majority of the studies have relied on discriminant analysis to classify firms into two distinct groups. Few studies (e.g., studies on bond ratings) have used multiple discriminant analysis to identify more than two groups. Varying levels of accurate classification rates are reported depending on how the matched group was selected and which statistical procedures were used to select the independent, explanatory variables.

Comparison of Examinees’ and Doctors’ Judgement of Health Status Using Data in an AMHTS

1984 ◽  
Vol 23 (01) ◽  
pp. 15-22
Author(s):  
Y. Sekita ◽  
T. Ohta ◽  
M. Inoue ◽  
H. Takeda
Keyword(s):  
Health Status ◽  
Discriminant Analysis ◽  
Explanatory Power ◽  
Laboratory Data ◽  
Subjective Symptom ◽  
Health Conditions ◽  
Testing System ◽  
Multiple Discriminant Analysis ◽  
Canonical Axis ◽  
Using Data

SummaryJudgements of examinees’ health status by doctors and by the examinees themselves are compared applying multiple discriminant analysis. The doctors’ judgements of the examinees’ health status are studied comparatively using laboratory data and the examinees’ subjective symptom data.This data was obtained in an Automated Multiphasic Health Testing System. We discuss the health conditions which are significant for the judgement of doctors about the examinees. The results show that the explanatory power, when using subjective symptom data, is fair in the case of the doctors’ judgement. We found common variables, such as nervousness, lack of perseverance etc., which form the first canonical axis.

The validity of diagnostic systems for common mental disorders: a comparison between the ID-CATEGO and the DSM-III systems

1990 ◽  
Vol 20 (1) ◽  
pp. 209-218 ◽  
Author(s):  
David Grayson ◽  
Keith Bridges ◽  
Diane Cook ◽  
David Goldberg
Keyword(s):  
Discriminant Analysis ◽  
Depressive Disorders ◽  
Common Mental Disorders ◽  
Latent Trait ◽  
Mental Illnesses ◽  
Anxiety And Depression ◽  
Multiple Discriminant Analysis ◽  
Diagnostic Systems ◽  
Scale Scores ◽  
Initial Scale

SYNOPSISIt is argued that latent trait analysis provides a way of examining the construct validity of diagnostic concepts which are used to categorize common mental illnesses. The present study adds two additional aspects of validity using multiple discriminant analysis applied to two widely used taxonomic systems. Scales of anxiety and depression derived from previous latent trait analyses are applied to individuals reaching criteria for ‘caseness’ on the ID-CATEGO system and the DSM-III system, both at initial diagnosis and six months later. The first multiple discriminant analysis is carried out on the initial scale scores, and the results are interpreted in terms of concurrent validity. The second analysis uses improvement scores on the two scales and relates to predictive validity. It is argued that the ID-CATEGO system provides a better classification for common mental illnesses than the DSM-III system, since it allows a better discrimination to be made between anxiety and depressive disorders.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

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