Handling high level of censoring for endovascular aortic repair risk prediction

Feature selection is important in medical field for many reasons. However, selecting important variables is a difficult task with the presence of censoring that is a unique feature in survival data analysis. This paper proposed an approach to deal with the censoring problem in endovascular aortic repair survival data through Bayesian networks. It was merged and embedded with a hybrid feature selection process that combines cox's univariate analysis with machine learning approaches such as ensemble artificial neural networks to select the most relevant predictive variables. The proposed algorithm was compared with common survival variable selection approaches such as; least absolute shrinkage and selection operator LASSO, and Akaike information criterion AIC methods. The results showed that it was capable of dealing with high censoring in the datasets. Moreover, ensemble classifiers increased the area under the roc curves of the two datasets collected from two centers located in United Kingdom separately. Furthermore, ensembles constructed with center 1 enhanced the concordance index of center 2 prediction compared to the model built with a single network. Although the size of the final reduced model using the neural networks and its ensembles is greater than other methods, the model outperformed the others in both concordance index and sensitivity for center 2 prediction. This indicates the reduced model is more powerful for cross center prediction.

Entrepreneurial entry:which institutions matter?

In this paper we explore the relationship between the individual decision to become an entrepreneur and the institutional context. We pinpoint the critical roles of property rights and the size of the state sector for entrepreneurial activity and test the relationships empirically by combining country-level institutional indicators for 44 countries with working age population survey data taken from the Global Enterprise Monitor. A methodological contribution is the use of factor analysis to reduce the statistical problems with the array of highly collinear institutional indicators. We find that the key institutional features that enhance entrepreneurial activity are indeed the rule of law and limits to the state sector. However, these results are sensitive to the level of development.

De-industrialisation:Rowthorn and Wells' Model revisited

This paper presents a simple three-sector model that seeks to establish links between structural change in employment and the level of economic development. The model is a modified version of that of Rowthorn - Wells (1987). The theoretical analysis is supplemented with simple econometric tests which illustrate how the modified Rowthorn - Wells model can be used (i) to motivate empirical estimates of the link between the level of development and structures of employment, and (ii) to illustrate structural distortions under command economies.

De-industrialisation and the post-communist transition:Rowthorn and Wells' model revisited

In this paper we present a simple three-sector model explaining the structural change in employment, which is a modified version of Rowthorn-Wells (1987). We supplement the theoretical analysis with simple econometric tests, which illustrate how the modified Rowthorn-Wells model can be used to (i) motivate empirical estimates of the link between the level of development and structures of employment, (ii) illustrate structural distortions under the command economies, and the structural adjustment that happened during the post-Communist transition. We demonstrate that in the case of these economies, the transition process leads to an adjustment to the employment structures predicted by the model.