What determines informal hiring? Evidence from the Turkish textile sector

Most studies about the shadow economy focus on the estimation of the aggregate size. However, this study aims to address the sectoral or micro aspects of this phenomenon using the data from the textile sector in Turkey. It uses discriminant analysis and ordered and logistic regression models to unveil the determinants of the informal hiring in Turkey. It concludes that high competition, the skill structure of the employees, perceived penalty scheme, and the size of the firms in the sector are important factors of the textile firms hiring informally.informal hiring in Turkish textile sector; discriminant analysis; logistic regression model; ordered regression

Analisis Perbandingan Prediksi Kebangkrutan Perusahaan dengan Menggunakan Multivariate Discriminant Analysis dan Regresi Logistik pada Perusahaan Pertambangan Batubara Periode 2010-2014

This research objective is to predict the bankruptcy in sector property and real estate which listed in Indonesia stock exchange: using discriminant analysis and logistic regression period 2010-2014. Sampling methods used in the research was purposive sampling. The hypothesis examination is tested by discriminant analysis and logistic regression analysis to determine significant differences in financial ratios such as current ratio, leverage ratio, net profit margin, debt to equity, operating profit margin, total asset turnover to distinguish a group of companies that are considered insolvent and not statistically bankrupt on listed companies in Indonesia stock exchanges in coal mining sector during the period of 2010-2014. The data source of this research come from Indonesia Stock Exchange (IDX).The result of this research showed that the accuracy of the models using Discriminant analysis was 80.4% and Logistic Regression Analysis was 88.2%. In the discriminant analysis showed that the significant variables were leverage ratio and net profit margin. As for the second logistic regression showed that significant variables were leverage ratio, net profit margin, and total assets turnover that could affect the company's bankruptcy prediction coal mining sector in the period 2010 to 2014. Keywords— bankruptcy, current ratio, leverage ratio, net profit margin, debt to equity, operating profit margin, total asset turnover, logistic regression. discriminant analysis

A COMPARISON BETWEEN MAXIMUM LIKELIHOOD RULE AND LOGISTIC DISCRIMINANT ANALYSIS IN THE CLASSIFICATION OF MIXTURE OF DISCRETE AND CONTINOUS VARIABLES

An optimal measure of performance is the one that lead to maximization of average error rate or probability of misclassification. This paper aimed to compare between the maximum likelihood rule and logistic discriminant analysis in the classification of mixture of discrete and continuous variables. The efficiency of the methods was tested using simulated and real dataset. The result obtained showed that the maximum likelihood rule performed better than the logistic discriminant analyses, in maximizing the average error rate in both experiment conducted. Keyword: Maximum likelihood rule, Logistic discriminants, error rate, Likelihood ratio, Discriminant analysis

A Comparison of Two Modeling Techniques in Customer Targeting For Bank Telemarketing

Customer targeting is the key to the success of bank telemarketing. To compare the flexible discriminant analysis and the logistic regression in customer targeting, a survey dataset from a Portuguese bank was used. For the flexible discriminant analysis model, the backward elimination of explanatory variables was used with several rounds of manual re-defining of dummy variables. For the logistic regression model, the automatic stepwise selection was performed to decide which explanatory variables should be left in the final model. Ten-fold stratified cross validation was performed to estimate the model parameters and accuracies. Although employing different sets of explanatory variables, the flexible discriminant analysis model and the logistic regression model show equally satisfactory performances in customer classification based on the areas under the receiver operating characteristic curves. Focusing on the predicted “right” customers, the logistic regression model shows slightly better classification and higher overall correct prediction rate

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches

BACKGROUND: Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression. RESULTS: The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene × gene and gene × environment interactions. There was evidence to suggest the existence of gene × environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene × gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one. CONCLUSION: We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene × gene or gene × environment interactions

Would credit scoring work for Islamic finance? A neural network approach

Purpose – The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process. Design/methodology/approach – A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings – Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications – Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value –Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected