Prediction of biomechanical parameters of the proximal femur using statistical appearance models and support vector regression

Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios

The Effect of Government Advertising Policies on the Market Power of Cigarette Firms

We estimate market power among cigarette manufacturers over 1952–1984, a period of uniform pricing. We apply the Bresnahan approach; adjust it to the firm level; employ a dynamic model with habit persistence; and add an advertising equation, which helps identify the parameters, increase degrees of freedom, and constrain parameters so we can interpret our results at the firm level, despite the fact that the equations conform to what we might see in a market model. We consider effects of government interventions upon demand and market power and find, for instance, that the 1971 broadcast advertising ban decreased market power. Copyright Springer 2006Advertising policies, broadcast advertising ban, cigarettes, market power, JEL classification, L1, L51, L66, M37,