scholarly journals Hybrid model using logit and nonparametric methods for predicting micro-entity failure

2016 ◽  
Vol 13 (3) ◽  
pp. 35-46 ◽  
Author(s):  
A. Blanco-Oliver ◽  
A. Irimia-Dieguez ◽  
M.D. Oliver-Alfonso ◽  
M.J. Vázquez-Cueto
Keyword(s):  
Logistic Regression ◽  
Hybrid Model ◽  
Rough Set ◽  
Multilayer Perceptron ◽  
Predictive Power ◽  
Bankruptcy Prediction ◽  
Parametric Methods ◽  
Macroeconomic Variables ◽  
Accuracy Performance ◽  
Non Parametric

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

scholarly journals PERBANDINGAN ANTARA METODE CART (CLASSIFICATION AND EGRESSION TREE) DAN REGRESI LOGISTIK (LOGISTIC REGRESSION) DALAM MENGKLASIFIKASIKAN PASIEN PENDERITA DBD (DEMAM BERDARAH DENGUE)

2018 ◽  
Vol 15 (1) ◽  
pp. 98-107
Author(s):  
R Lestawati ◽  
Rais Rais ◽  
I T Utami
Keyword(s):  
Logistic Regression ◽  
Statistical Methods ◽  
Parametric Method ◽  
Parametric Methods ◽  
Classification Methods ◽  
Classification Result ◽  
The Status ◽  
Classification Table ◽  
Non Parametric

Classification is one of statistical methods in grouping the data compiled systematically. The classification of an object can be done by two approaches, namely classification methods parametric and non-parametric methods. Non-parametric methods is used in this study is the method of CART to be compared to the classification result of the logistic regression as one of a parametric method. From accuracy classification table of CART method to classify the status of DHF patient into category of severe and non-severe exactly 76.3%, whereas the percentage of truth logistic regression was 76.7%, CART method to classify the status of DHF patient into categories of severe and non-severe exactly 76.3%, CART method yielded 4 significant variables that hepatomegaly, epitaksis, melena and diarrhea as well as the classification is divided into several segmens into a more accurate whereas the logistic regression produces only 1 significant variables that hepatomegaly

scholarly journals Comparison of Three Statistical Classification Techniques for Maser Identification

2016 ◽  
Vol 33 ◽  
Author(s):  
Ellen M. Manning ◽  
Barbara R. Holland ◽  
Simon P. Ellingsen ◽  
Shari L. Breen ◽  
Xi Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forests ◽  
Parametric Method ◽  
Parametric Models ◽  
Critical Parameters ◽  
Parametric Methods ◽  
Statistical Classification ◽  
Classification Techniques ◽  
Linear Discriminant ◽  
Non Parametric

AbstractWe applied three statistical classification techniques—linear discriminant analysis (LDA), logistic regression, and random forests—to three astronomical datasets associated with searches for interstellar masers. We compared the performance of these methods in identifying whether specific mid-infrared or millimetre continuum sources are likely to have associated interstellar masers. We also discuss the interpretability of the results of each classification technique. Non-parametric methods have the potential to make accurate predictions when there are complex relationships between critical parameters. We found that for the small datasets the parametric methods logistic regression and LDA performed best, for the largest dataset the non-parametric method of random forests performed with comparable accuracy to parametric techniques, rather than any significant improvement. This suggests that at least for the specific examples investigated here accuracy of the predictions obtained is not being limited by the use of parametric models. We also found that for LDA, transformation of the data to match a normal distribution led to a significant improvement in accuracy. The different classification techniques had significant overlap in their predictions; further astronomical observations will enable the accuracy of these predictions to be tested.

scholarly journals Tax Arrears Versus Financial Ratios in Bankruptcy Prediction

2019 ◽  
Vol 12 (4) ◽  
pp. 187 ◽  
Author(s):  
Oliver Lukason ◽  
Art Andresson
Keyword(s):  
Logistic Regression ◽  
Multilayer Perceptron ◽  
Prediction Accuracy ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Prediction Methods ◽  
Combined Model ◽  
The Individual ◽  
Practical Implications

This paper aims to compare the usefulness of tax arrears and financial ratios in bankruptcy prediction. The analysis is based on the whole population of Estonian bankrupted and survived SMEs from 2013 to 2017. Logistic regression and multilayer perceptron are used as the prediction methods. The results indicate that closer to bankruptcy, tax arrears’ information yields a higher prediction accuracy than financial ratios. A combined model of tax arrears and financial ratios is more useful than the individual models. The results enable us to outline several theoretical and practical implications.

KEBANGGAAN TERHADAP BATIK MADURA BAGI WARGA SUMENEP

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Sayyida Sayyida ◽  
Nurdody Zakki
Keyword(s):  
Logistic Regression ◽  
Rural Areas ◽  
Research Method ◽  
Community Group ◽  
Parametric Regression ◽  
Logit Function ◽  
The Village ◽  
The City ◽  
Level Of Risk ◽  
Non Parametric

Diversity of Indonesian Batik hanging area. One of the very well-known Indonesian batik is Batik Madura. Batik Madura has become a pride for Indonesia, especially for Madura. The purpose of the study is to model the Sumenep pride to Batik Madura and to see the level of risk or tendency of batik madura pride for the community group Sumenep. This research method uses a non parametric regression used a non-parametric regression because the dependent variable in this study is the variable Y are variables not normally distributed. The results of this study states that the level of risk of the village in Sumenep proud of batik is almost 5 times higher than the islands while people in this city who live in the district town at risk Sumenep proud of Batik Madura 8-fold compared to the archipelago. So it can be concluded that the city is much more proud of batik than those who reside in rural areas especially those who reside in the islands. This study uses data from 100 questionnaires were analyzed using logistic regression analysis. The conclusion of this study is the pride of the batik model as follows: Function logistic regression / logit function: g (x) = 0,074 + 1,568X4(1)+2,159X4(2 this is case the islands as a comparison, X4(1)  is the place to stay in the village and X4(2)  is the place to stay in town, so the Model Opportunities p(x) = EXP(g(x))/1+EXP(g(x)).  Hopes for further research is to conduct research on the development of batik in an integrated region, the need to be disseminated to potential areas of particular potential in Madura batik, especially for residents who reside in the Islands.Keywords: Pride, Batik, Sumenep.

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