Liu-Type Logistic Estimator

It is known that multicollinearity inflates the variance of the maximum likelihood estimator in logistic regression. Especially, if the primary interest is in the coefficients, the impact of collinearity can be very serious. To deal with collinearity, a ridge estimator was proposed by Schaefer et al. The primary interest of this article is to introduce a Liu-type estimator that had a smaller total mean squared error (MSE) than the Schaefer's ridge estimator under certain conditions. Simulation studies were conducted that evaluated the performance of this estimator. Furthermore, the proposed estimator was applied to a real-life dataset.     

Children's reliance on situational and vocal expression of emotions: Consistent and conflicting cues

The aim of the present study was to investigate developmental differences in reliance on situational versus vocal cues for recognition of emotions. Turkish preschool, second, and fifth grade children participated in the study. Children listened to audiotape recordings of situations between a mother and a child where the emotional cues implied by the context of a vignette and the vocal expression were either consistent or inconsistent. After listening to each vignette, participants were questioned about the content of the incident and were asked to make a judgment about the emotion of the mother referred to in the recording. Angry, happy, and neutral emotions were utilized. Results revealed that 1) recognition of emotions improved with age, and 2) children relied more on the channel depicting either anger or happiness than on the channel depicting neutrality.     

Prediction of bankruptcy using support vector machines: an application to bank bankruptcy

The purpose of this study was to apply support vector machines (SVMs) to bank bankruptcy analysis using practical steps. Although the prediction of the financial distress of companies is done using several statistical and machine learning techniques, bank classification and bankruptcy prediction still need to be investigated because few investigations have been conducted in this field of banking. In this study, SVMs were implemented to analyse financial ratios. Data sets from Turkish commercial banks were used. This study shows that SVMs with the Gaussian kernel are capable of extracting useful information from financial data and can be used as part of an early warning system.