Global ETD Search

51	Political and economic events 1988 to 1998 : their impact on the specification of the nonlinear multifactor asset pricing model described by the arbitrage pricing theory for the financial and industrial sector of the Johannesburg Stock Exchange Stephanou, Costas Michael 05 1900 (has links) The impact of political and economic events on the asset pricing model described by the arbitrage pricing theory (APTM) was examined in order to establish if they had caused any changes in its specification. It was concluded that the APTM is not stationary and that it must be continuously tested before it can be used as political and economic events can change its specification. It was also found that political events had a more direct effect on the specification of the APTM, in that their effect is more immediate, than did economic events, which influenced the APTM by first influencing the economic environment in which it operated. The conventional approach that would have evaluated important political and economic events, case by case, to determine whether they affected the linear factor model (LFM), and subsequently the APTM, could not be used since no correlation was found between the pricing of a risk factor in the LFM and its subsequent pricing in the APTM. A new approach was then followed in which a correlation with a political or economic event was sought whenever a change was detected in the specification of the APTM. This was achieved by first finding the best subset LFM, chosen for producing the highest adjusted R2 , month by month, over 87 periods from 20 October1991 to 21 June 1998, using a combination of nine prespecified risk factors (five of which were proxies for economic events and one for political events). Multivariate analysis techniques were then used to establish which risk factors were priced most often during the three equal subperiods into which the 87 periods were broken up. Using the above methodology, the researcher was able to conclude that political events changed the specification of the APTM in late 1991. After the national elections in April 1994 it was found that the acceptance of South Africa into the world economic community had again changed the specification of the APTM and the two most important factors were proxies for economic events. / Business Leadership / DBL Arbitrage pricing theory Arbitrage pricing model Arbitrage pricing theory model Non-linear asset pricing model Linear multifactor model Nonlinear three stage least squares Macroeconomic and financial factors 332.632220968 Johannesburg Stock Exchange Arbitrage -- Mathematical models
52	Electromagnetic field and neurological disorders Alzheimer´s disease, why the problem is difficult and how to solve it Lyttkens, Peter January 2018 (has links) No description available. Alzheimer oxidative stress EMF microwave VGCC ACh protein refolding APOE big data FINGER wifi DECT AChE DNA-damage mobile phone glucose metabolism demyelinating antioxidant defence defective proteins non thermal effects Alzheimer risk pathophysiology mechanism amyloid pathologies multifactor strategy diet exercise cognitive training vascular risk management exposure sleep disturbance circadian dysfunction neuropsychiatric symptom risk assessments Neurosciences Neurovetenskaper
53	Political and economic events 1988 to 1998 : their impact on the specification of the nonlinear multifactor asset pricing model described by the arbitrage pricing theory for the financial and industrial sector of the Johannesburg Stock Exchange Stephanou, Costas Michael 05 1900 (has links) The impact of political and economic events on the asset pricing model described by the arbitrage pricing theory (APTM) was examined in order to establish if they had caused any changes in its specification. It was concluded that the APTM is not stationary and that it must be continuously tested before it can be used as political and economic events can change its specification. It was also found that political events had a more direct effect on the specification of the APTM, in that their effect is more immediate, than did economic events, which influenced the APTM by first influencing the economic environment in which it operated. The conventional approach that would have evaluated important political and economic events, case by case, to determine whether they affected the linear factor model (LFM), and subsequently the APTM, could not be used since no correlation was found between the pricing of a risk factor in the LFM and its subsequent pricing in the APTM. A new approach was then followed in which a correlation with a political or economic event was sought whenever a change was detected in the specification of the APTM. This was achieved by first finding the best subset LFM, chosen for producing the highest adjusted R2 , month by month, over 87 periods from 20 October1991 to 21 June 1998, using a combination of nine prespecified risk factors (five of which were proxies for economic events and one for political events). Multivariate analysis techniques were then used to establish which risk factors were priced most often during the three equal subperiods into which the 87 periods were broken up. Using the above methodology, the researcher was able to conclude that political events changed the specification of the APTM in late 1991. After the national elections in April 1994 it was found that the acceptance of South Africa into the world economic community had again changed the specification of the APTM and the two most important factors were proxies for economic events. / Business Leadership / DBL Arbitrage pricing theory Arbitrage pricing model Arbitrage pricing theory model Non-linear asset pricing model Linear multifactor model Nonlinear three stage least squares Macroeconomic and financial factors 332.632220968 Johannesburg Stock Exchange Arbitrage -- Mathematical models
54	Functions of Mentoring as Christian Discipleship Foster, Hiram S. 16 June 2014 (has links) No description available. Behavioral Psychology Behavioral Sciences Behaviorial Sciences Bible Biblical Studies Communication Counseling Psychology Education Educational Psychology Educational Theory Higher Education Occupational Psychology Personal Relationships Pastoral Counseling Personality Psychology Psychological Tests Psychotherapy Religion Religious Congregations Religious Education School Counseling Social Psychology Social Research Spirituality Teaching mentoring Cru Campus Crusade for Christ Christianity discipleship psychosocial instrumental student mentoring development hierarchy mutuality multifactor leadership questionnaire friendship mentee mentor interpersonal leadership protege
55	A Multilinear (Tensor) Algebraic Framework for Computer Graphics, Computer Vision and Machine Learning Vasilescu, M. Alex O. 09 June 2014 (has links) This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''. multilinear algebra tensor algebra computer vision computer graphics machine learning principal component analysis (PCA) independent component analysis (ICA/BSS) kernel component analysis (KPCA/KICA) local linear embedding multifactor LLE M-mode SVD M-mode PCA M-mode ICA multilinear projection m-mode pseudo-inverse tensor m-mode identity tensor m-mode product genearlization texture synthesis face recognition human motion signatures 0984 0800 0805 0463
56	A Multilinear (Tensor) Algebraic Framework for Computer Graphics, Computer Vision and Machine Learning Vasilescu, M. Alex O. 09 June 2014 (has links) This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''. multilinear algebra tensor algebra computer vision computer graphics machine learning principal component analysis (PCA) independent component analysis (ICA/BSS) kernel component analysis (KPCA/KICA) local linear embedding multifactor LLE M-mode SVD M-mode PCA M-mode ICA multilinear projection m-mode pseudo-inverse tensor m-mode identity tensor m-mode product genearlization texture synthesis face recognition human motion signatures 0984 0800 0805 0463

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