Biased discriminant analysis: Evaluation of the optimum probability of misclassification

This article extends the work of DiPillo (1976) on the Biased Minimum x² Rule. The optimum value of k (the biasing factor) Is determined and the true probability of misclassification is found. The proportion improvements reported in the 1976 paper are shown to be conservative. Some suggestions for algorithms to determine the optimal value of k are presented.     

The application of bias to discriminant analysis

When classification rules are constructed using sample estimatest it is known that the probability of misclassification is not minimized. This article introduces a biased minimum X² rule to classify items from a multivariate normal population. Using the principle of variance reduction, the probability of misclassification is reduced when the biased procedure is employed. Results of sampling experiments over a broad range of conditions are provided to demonstrate this improvement.     

Associations Between Systemic Quality of Life and Burnout Among French Canadian Workers

Social Indicators Research - Burnout has very often been studied from a work-life specific perspective. However, a recent field of research leads to wonder about the impact that life events can...     

Growth,Trade and Factor Endowment: A Survey

This paper examines the relationship between factor endowment, human capital formation and trade in the theory of economic growth. The paper analyses the evolution of the idea that both trade and human capital play a leading role in explaining country performance and development. Economic growth, dynamic comparative advantage, international specialization and cumulative technological progress are identified as the key ingredients that explain why countries perform differently and, at the same time, why they may tend to converge towards a common benchmark.