title: A Transnational Analysis of Elderly European Wealth Distributions: Developing and Testing New Methodologies within the new Household Financial and Consumption Survey

authors: Gaylard, David Michael

subjects: wealth

abstract: Transnational wealth inequality is an expanding field of study. However, the field has many limitations especially relating to problems of data availability, methodologies used, and the very limited pool of existing transnational literature. Alongside these basic limitations is a predominating focus on populations as a whole that correspond with a dearth of studies concerned specifically with elderly wealth distributions.
This thesis’ objective is to tackle these three problematic areas in the field, but with a specific focus on the elderly. In particular, new methodologies for analysing transnational distributions of wealth are explored and developed. These new methodologies are then tested within the Household Financial and Consumption Survey environment specifically focusing on the elderly population. This new survey provides an extensive high quality database on household financial characteristics that are pivotal to transnational and sub-cohort focused analysis.
The analysis generates several results. The first is a tested framework of a multifaceted approach for describing wealth distributions. This approach counters the widespread habit of using summary statistics which do not fully describe distributions. The results also provide evidence for two distinct macro distributions of wealth within the countries observed. These distributions represent two ends on a spectrum which contains all the possible wealth distribution shapes.
The combination of developing new methodological tools of analysis and testing them within a new data environment contribute significantly to the field. These new tools will allow for a standardised view providing deep level analysis which is still applicable to a large dataset.

text: A Transnational Analysis of Elderly European Wealth Distributions: Developing and Testing New Methodologies within the new Household Financial and Consumption Survey Transnational wealth inequality is an expanding field of study. However, the field has many limitations especially relating to problems of data availability, methodologies used, and the very limited pool of existing transnational literature. Alongside these basic limitations is a predominating focus on populations as a whole that correspond with a dearth of studies concerned specifically with elderly wealth distributions.
This thesis’ objective is to tackle these three problematic areas in the field, but with a specific focus on the elderly. In particular, new methodologies for analysing transnational distributions of wealth are explored and developed. These new methodologies are then tested within the Household Financial and Consumption Survey environment specifically focusing on the elderly population. This new survey provides an extensive high quality database on household financial characteristics that are pivotal to transnational and sub-cohort focused analysis.
The analysis generates several results. The first is a tested framework of a multifaceted approach for describing wealth distributions. This approach counters the widespread habit of using summary statistics which do not fully describe distributions. The results also provide evidence for two distinct macro distributions of wealth within the countries observed. These distributions represent two ends on a spectrum which contains all the possible wealth distribution shapes.
The combination of developing new methodological tools of analysis and testing them within a new data environment contribute significantly to the field. These new tools will allow for a standardised view providing deep level analysis which is still applicable to a large dataset.

year: 2017

title: Pruning decision trees and lists

authors: Frank, Eibe

abstract: Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. 

Decision trees and lists are potentially powerful predictors and embody an explicit representation of the structure in a dataset. Their accuracy and comprehensibility depends on how concisely the learning algorithm can summarize this structure. The final model should not incorporate spurious effects-patterns that are not genuine features of the underlying domain. Given an efficient mechanism for determining when a particular effect is due to chance alone, non-predictive parts of a model can be eliminated or “pruned.” Pruning mechanisms require a sensitive instrument that uses the data to detect whether there is a genuine relationship between the components of a model and the domain. Statistical significance tests are theoretically well-founded tools for doing exactly that. 

This thesis presents pruning algorithms for decision trees and lists that are based on significance tests. We explain why pruning is often necessary to obtain small and accurate models and show that the performance of standard pruning algorithms can be improved by taking the statistical significance of observations into account. We compare the effect of parametric and non-parametric tests, analyze why current pruning algorithms for decision lists often prune too aggressively, and review related work-in particular existing approaches that use significance tests in the context of pruning. The main outcome of this investigation is a set of simple pruning algorithms that should prove useful in practical data mining applications.

text: Pruning decision trees and lists Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. 

Decision trees and lists are potentially powerful predictors and embody an explicit representation of the structure in a dataset. Their accuracy and comprehensibility depends on how concisely the learning algorithm can summarize this structure. The final model should not incorporate spurious effects-patterns that are not genuine features of the underlying domain. Given an efficient mechanism for determining when a particular effect is due to chance alone, non-predictive parts of a model can be eliminated or “pruned.” Pruning mechanisms require a sensitive instrument that uses the data to detect whether there is a genuine relationship between the components of a model and the domain. Statistical significance tests are theoretically well-founded tools for doing exactly that. 

This thesis presents pruning algorithms for decision trees and lists that are based on significance tests. We explain why pruning is often necessary to obtain small and accurate models and show that the performance of standard pruning algorithms can be improved by taking the statistical significance of observations into account. We compare the effect of parametric and non-parametric tests, analyze why current pruning algorithms for decision lists often prune too aggressively, and review related work-in particular existing approaches that use significance tests in the context of pruning. The main outcome of this investigation is a set of simple pruning algorithms that should prove useful in practical data mining applications.

year: 2000

title: A proposed hauora Māori clinical guide for psychologists: Using the hui process and meihana model in clinical assessment and formulation

authors: Pitama, Suzanne G.

subjects: Māori

abstract: This paper documents a joint initiative of clinical practice educators from four tertiary institutions and their engagement in the design and development of a proposed Hauora Māori Clinical Guide for Psychologists, which outlines how to apply the Hui Process and Meihana Model to applied psychology. It describes the ability for this proposed Hauora Maori Clinical Guide for Psychologists to assist clinicians, professional psychology training programmes and institutions in meeting the expectations of the Health Practitioners Act and The New Zealand Psychologists Board’s (NZPB) Standards and Procedures document. It presents how this proposed guide can support the implementation of clinical and cultural competence and the Code of Ethics for Psychologists Working in New Zealand. It also provides an opportunity for the psychology profession to demonstrate responsivity to Te Tiriti o Waitangi obligations.

text: A proposed hauora Māori clinical guide for psychologists: Using the hui process and meihana model in clinical assessment and formulation This paper documents a joint initiative of clinical practice educators from four tertiary institutions and their engagement in the design and development of a proposed Hauora Māori Clinical Guide for Psychologists, which outlines how to apply the Hui Process and Meihana Model to applied psychology. It describes the ability for this proposed Hauora Maori Clinical Guide for Psychologists to assist clinicians, professional psychology training programmes and institutions in meeting the expectations of the Health Practitioners Act and The New Zealand Psychologists Board’s (NZPB) Standards and Procedures document. It presents how this proposed guide can support the implementation of clinical and cultural competence and the Code of Ethics for Psychologists Working in New Zealand. It also provides an opportunity for the psychology profession to demonstrate responsivity to Te Tiriti o Waitangi obligations.

year: 2017