APPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING

Recursive Partitioning Algorithm (RPA) is introduced as a technique for credit scoring analysis, which allows direct incorporation of misclassification costs. This study corroborates nonagricultural credit studies, which indicate that RPA outperforms logistic regression based on within-sample observations. However, validation based on more appropriate out-of-sample observations indicates that logistic regression is superior under some conditions. Incorporation of misclassification costs can influence the creditworthiness decision.finance, credit scoring, misclassification, recursive partitioning algorithm, Agricultural Finance,

Identifying labour market dynamics using labour force survey data

This paper evaluates the appropriateness of the standard methodologies and the quality of the data frequently used to analyse labour market dynamics in Europe. Our results indicate that, due to recall error and heterogeneous survey design, the retrospective approach tends to result in a considerable number of spurious transitions being recorded. Whilst the use of quasi-longitudinal data should overcome such problems, sample attrition and more importantly, misclassification error, is shown to result in significant over-reporting of transitions. Studies which failure to allow for the error structure of the underlying data are therefore, likely to be subject to considerable bias. --

Discriminant Functions And Their Misclassification Errors

This paper is a survey study on discriminant functions and their misclassification errors. Here we consider three groups of discriminant functions, namely discriminant functions for respec- tively multivariate normal variables, multivariate binary variables, and a mixture of multivariate binary and normal variables. Finally we derive their misclassification errors

Estimation of error rate for linear discriminant functions by resampling: Non-Gaussian populations

AbstractThis article presents simulation results comparing various resampling estimators of classification error rate for linear discriminant type classification algorithms. Three non-Gaussian multivariate populations are studied namely, exponential, Cauchy and uniform. Simulations are conducted for small sample sizes, two-class and three-class problems and 2-D, 3-D and 5-D distributions. Estimation procedures and sample sizes are the same as in our previous study of Gaussian populations; again 200 bootstrap replications are used for each simulation trial. For exponential and uniform distributions the 0.632 estimator generally performs best. However, for Cauchy distributions the convex bootstrap and the e0 often outperform the 0.632 estimator

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

On a General Computer Algorithm for the Analysis of Models with Limited Dependent Variables

Several econometric models for the analysis of relationships with limited dependent variables have been proposed, including the probit, Tobit, two-limit probit, ordered discrete, and friction models. Widespread application of these methods has been hampered by the lack of suitable computer programs. This paper provides a concise survey of the various models; suggests a general functional model under which they may be formulated and analyzed; reviews the analytic problems and the similarities and dissimilarities of the models; and outlines the appropriate and necessary methods of analysis including, but not limited to, estimation. It is thus intended to serve as a guide for users of the various models, for the preparation of suitable computer programs, for the users of those programs; and, more specifically, for the users of the program package utilizing the functional model as implemented on the NBER TROLL system.