Reassessing the Paradigms of Statistical Model-Building

Statistical model-building is the science of constructing models from data and from information about the data-generation process, with the aim of analysing those data and drawing inference from that analysis. Many statistical tasks are undertaken during this analysis; they include classification, forecasting, prediction and testing. Model-building has assumed substantial importance, as new technologies enable data on highly complex phenomena to be gathered in very large quantities. This creates a demand for more complex models, and requires the model-building process itself to be adaptive. The word “paradigm” refers to philosophies, frameworks and methodologies for developing and interpreting statistical models, in the context of data, and applying them for inference. In order to solve contemporary statistical problems it is often necessary to combine techniques from previously separate paradigms. The workshop addressed model-building paradigms that are at the frontiers of modern statistical research. It tried to create synergies, by delineating the connections and collisions among different paradigms. It also endeavoured to shape the future evolution of paradigms

On the efectiveness of several market integration measures: an empirical analysis

Many market integration measures are operationalized to compute their numerical values during a period characterized by the lack of stability ad market turmoil. The results of the tests give their degree of effectiveness, and reveal that the measures based on the principles of asset valuation, versus statistical measures, more clearly yield the level of integration of financial markets. Besides, cross market arbitrage-linked measures and equilibrium models-linked measures provide complementary information and reflect different properties, and consequently, both types of measures may be useful in practice

Functional Skills Support Programme: Developing functional skills in citizenship

This booklet is part of "... a series of 11 booklets which helps schools to implement functional skills across the curriculum. The booklets illustrate how functional skills can be applied and developed in different subjects and contexts, supporting achievement at Key Stage 3 and Key Stage 4. Each booklet contains an introduction to functional skills for subject teachers, three practical planning examples with links to related websites and resources, a process for planning and a list of additional resources to support the teaching and learning of functional skills." - The National Strategies website

Functional Skills Support Programme: Developing functional skills in art and design

This booklet is part of "... a series of 11 booklets which helps schools to implement functional skills across the curriculum. The booklets illustrate how functional skills can be applied and developed in different subjects and contexts, supporting achievement at Key Stage 3 and Key Stage 4. Each booklet contains an introduction to functional skills for subject teachers, three practical planning examples with links to related websites and resources, a process for planning and a list of additional resources to support the teaching and learning of functional skills." - The National Strategies website

Credit Risk Scoring: A Stacking Generalization Approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementCredit risk regulation has been receiving tremendous attention, as a result of the effects of the latest global financial crisis. According to the developments made in the Internal Rating Based approach, under the Basel guidelines, banks are allowed to use internal risk measures as key drivers to assess the possibility to grant a loan to an applicant. Credit scoring is a statistical approach used for evaluating potential loan applications in both financial and banking institutions. When applying for a loan, an applicant must fill out an application form detailing its characteristics (e.g., income, marital status, and loan purpose) that will serve as contributions to a credit scoring model which produces a score that is used to determine whether a loan should be granted or not. This enables faster and consistent credit approvals and the reduction of bad debt. Currently, many machine learning and statistical approaches such as logistic regression and tree-based algorithms have been used individually for credit scoring models. Newer contemporary machine learning techniques can outperform classic methods by simply combining models. This dissertation intends to be an empirical study on a publicly available bank loan dataset to study banking loan default, using ensemble-based techniques to increase model robustness and predictive power. The proposed ensemble method is based on stacking generalization an extension of various preceding studies that used different techniques to further enhance the model predictive capabilities. The results show that combining different models provides a great deal of flexibility to credit scoring models