Traditional means of studying environmental economics and management problems consist of optimal control and dynamic game models that are solved for optimal or equilibrium strategies. Notwithstanding the possibility of multiple equilibria, the models’ users—managers or planners—will...

This master thesis is devoted to Classification and Regression Trees (CART). CART is classification method which uses historical data to construct decision trees. Depending on available information about the dataset, classification tree or regression tree can be constructed. Constructed tree can...

Purpose The purpose of this paper is to do a performance comparison of three different data mining techniques. Design/methodology/approach Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from...

For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classification problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not...

Despite growing interest in biomass over the last number of years, bio-energy derived from biomass currently contributes to a very small share of the total Spanish energy market. How individual farmers choose to respond to the opportunities presented by these relatively novel crops has still...

In the article offered approach to perfection method of estimation of solvency of management borrowers-subjects by the trees of classifications and Neurons networks which have considerable advantages as compared to the coefficient estimation of solvency: the problem of interpretation of results...

The aim of the paper is to estimate models for household classifi cation from the point of view of their financial situation. The models are constructed on the basis of data from the Living Conditions 2010 survey. The target indicators are the possibility of a household to afford a week-long...

Propensity score analysis is a relatively recent statistical innovation that is useful in the analysis of data from quasi-experiments. The goal of propensity score analysis is to balance two non-equivalent groups on observed covariates to get more accurate estimates of the effects of a treatment...

For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classification problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not...

Item nonresponse in survey data can pose significant problems for social scientists carrying out statistical modeling using a large number of explanatory variables. A number of imputation methods exist but many only deal with univariate imputation, or relatively simple cases of multivariate...