title: A new approach to fitting linear models in high dimensional spaces

authors: Wang, Yong

abstract: This thesis presents a new approach to fitting linear models, called “pace regression”, which also overcomes the dimensionality determination problem. Its optimality in minimizing the expected prediction loss is theoretically established, when the number of free parameters is infinitely large. In this sense, pace regression outperforms existing procedures for fitting linear models. Dimensionality determination, a special case of fitting linear models, turns out to be a natural by-product. A range of simulation studies are conducted; the results support the theoretical analysis. 

Through the thesis, a deeper understanding is gained of the problem of fitting linear models. Many key issues are discussed. Existing procedures, namely OLS, AIC, BIC, RIC, CIC, CV(d), BS(m), RIDGE, NN-GAROTTE and LASSO, are reviewed and compared, both theoretically and empirically, with the new methods. 

Estimating a mixing distribution is an indispensable part of pace regression. A measure-based minimum distance approach, including probability measures and nonnegative measures, is proposed, and strongly consistent estimators are produced. Of all minimum distance methods for estimating a mixing distribution, only the nonnegative-measure-based one solves the minority cluster problem, what is vital for pace regression.

Pace regression has striking advantages over existing techniques for fitting linear models. It also has more general implications for empirical modeling, which are discussed in the thesis.

text: A new approach to fitting linear models in high dimensional spaces This thesis presents a new approach to fitting linear models, called “pace regression”, which also overcomes the dimensionality determination problem. Its optimality in minimizing the expected prediction loss is theoretically established, when the number of free parameters is infinitely large. In this sense, pace regression outperforms existing procedures for fitting linear models. Dimensionality determination, a special case of fitting linear models, turns out to be a natural by-product. A range of simulation studies are conducted; the results support the theoretical analysis. 

Through the thesis, a deeper understanding is gained of the problem of fitting linear models. Many key issues are discussed. Existing procedures, namely OLS, AIC, BIC, RIC, CIC, CV(d), BS(m), RIDGE, NN-GAROTTE and LASSO, are reviewed and compared, both theoretically and empirically, with the new methods. 

Estimating a mixing distribution is an indispensable part of pace regression. A measure-based minimum distance approach, including probability measures and nonnegative measures, is proposed, and strongly consistent estimators are produced. Of all minimum distance methods for estimating a mixing distribution, only the nonnegative-measure-based one solves the minority cluster problem, what is vital for pace regression.

Pace regression has striking advantages over existing techniques for fitting linear models. It also has more general implications for empirical modeling, which are discussed in the thesis.

year: 2000

title: California’s Renewables Portfolio Standard (RPS) requires 33% renewable electricity generation by 2020 - Dream or Reality?

authors: Walmsley, Michael R.W.

abstract: Progress on California’s Renewable Portfolio Standard (RPS), which requires 33% of all retail electricity sales to be served by renewable energy sources by 2020, excluding large hydro, is reported in this paper. The emerging renewable electricity mix in California (CA) and surrounding states which form the Western Electricity Coordination Council (WECC) is analysed using the Carbon Emission Pinch Analysis (CEPA) and Energy Return on Energy Invested (EROI) methodologies. The reduction in emissions with increased renewables is illustrated and the challenge of maintaining high EROI levels for renewable generation is examined for low and high electricity demand growth. The role of the California government in facilitating progress towards a more sustainable renewable electricity future is also highlighted. The investigation shows that wind and solar PV collectively form an integral part of California reaching the 33% renewables target (excluding large hydro) by 2020. Government intervention of tax rebates and subsidies, net electricity metering and a four tiered electricity price has accelerated the uptake of renewable wind and solar PV. Residential uptake of solar PV is also reducing overall California electricity grid demand. Emphasis on new renewable generation is stimulating development of affordable wind and solar technology in California which has the added benefit of enhancing social sustainability through improved employment opportunities at a variety of technical levels.

text: California’s Renewables Portfolio Standard (RPS) requires 33% renewable electricity generation by 2020 - Dream or Reality? Progress on California’s Renewable Portfolio Standard (RPS), which requires 33% of all retail electricity sales to be served by renewable energy sources by 2020, excluding large hydro, is reported in this paper. The emerging renewable electricity mix in California (CA) and surrounding states which form the Western Electricity Coordination Council (WECC) is analysed using the Carbon Emission Pinch Analysis (CEPA) and Energy Return on Energy Invested (EROI) methodologies. The reduction in emissions with increased renewables is illustrated and the challenge of maintaining high EROI levels for renewable generation is examined for low and high electricity demand growth. The role of the California government in facilitating progress towards a more sustainable renewable electricity future is also highlighted. The investigation shows that wind and solar PV collectively form an integral part of California reaching the 33% renewables target (excluding large hydro) by 2020. Government intervention of tax rebates and subsidies, net electricity metering and a four tiered electricity price has accelerated the uptake of renewable wind and solar PV. Residential uptake of solar PV is also reducing overall California electricity grid demand. Emphasis on new renewable generation is stimulating development of affordable wind and solar technology in California which has the added benefit of enhancing social sustainability through improved employment opportunities at a variety of technical levels.

year: 2014-09-20

title: The relevance of discourse analysis to the teaching and learning of te reo Māori: A text-centred example

authors: Greensill, Hine-iti-moana

subjects: Māori language

abstract: In this paper, I analyse a single text written in te reo Māori in terms of two different approaches derived from discourse analysis (discourse macro-patterning and discourse relations and their signalling) in order to demonstrate the importance of deriving a discourse-centred methodology for teaching learners of Māori what is involved in interpreting and creating coherent discourses.

text: The relevance of discourse analysis to the teaching and learning of te reo Māori: A text-centred example In this paper, I analyse a single text written in te reo Māori in terms of two different approaches derived from discourse analysis (discourse macro-patterning and discourse relations and their signalling) in order to demonstrate the importance of deriving a discourse-centred methodology for teaching learners of Māori what is involved in interpreting and creating coherent discourses.

year: 2007