This study is concerned in reducing high dimensionality problem of auxiliary variables in the calibration estimation with the presence of nonresponse. The calibration estimation is a weighting method assists to compensate for the nonresponse in the survey analysis. Calibration estimation using principal components (PCs) is new idea in the literatures. Principal component analysis (PCA) is used in reduction dimension of the auxiliary variables. PCA in calibration estimation is presented as an alternative method for choosing the auxiliary variables. In this study, simulation on the real data is used and nonresponse mechanism is applied on the sampled data. The calibration estimator is compared using different criteria such as varying the nonresponse rate and increasing the sample size. From the results, although the calibration estimation based on the principal components have reasonable outputs to use instead of the whole auxiliary variables for the means, the variance is very large compared with based on original auxiliary variables. Finally, we identified the principal component analysis is not efficient in the reduction of high dimensionality problem of auxiliary variables in the calibration estimation for large sample sizes.

One problem of interest is to relate genes to survival outcomes of patients for the purpose of building regression models to predict future patients¡¯ survival based on their gene expression data. Applying semeparametric additive risk model of survival analysis, this thesis proposes a new approach to conduct the analysis of gene expression data with the focus on model¡¯s predictive ability. The method modifies the correlation principal component regression to handle the censoring problem of survival data. Also, we employ the time dependent AUC and RMSEP to assess how well the model predicts the survival time. Furthermore, the proposed method is able to identify significant genes which are related to the disease. Finally, this proposed approach is illustrated by simulation data set, the diffuse large B-cell lymphoma (DLBCL) data set, and breast cancer data set. The results show that the model fits both of the data sets very well.

Evidence seems to suggest that with 14 years of unbroken economic growth, the UK’s construction and building services sector is experiencing severe skills crisis of between 40 – 50 per cent retention rate and declining numbers of entrant trainees. More importantly, the level of this severity varies with sub regional and regional peculiarities. To date, most studies on this area have focused on increasing the population of the existing pools of labour rather than harnessing existing ones. Adopting the concept of multiskilling, current techniques of evaluating skills crisis were critically reviewed. While there has been some empirically beneficial application of this concept in the US, it is a rarity in the literature to find previous works on multiskilling in UK’s construction and building services sector. Adopting an action research approach, a Project Steering Group of industry stakeholders served as a research ‘think tank’ for validating empirical results, and in line with the theory of construct validity, instruments of survey were designed and operationalized in a pilot and major surveys of supply and demand sides’ target groups. Employing the relative index ranking technique, the forecast implications of UK’s economic stability are ‘real’ and a demand led system is prescribed as a tentative ‘cushion’ for sustainable but immediate redress. A time series data for the period 1961 – 2004 is explored and systematised quantitative demand led models for evaluating construction output based on aggregated and disaggregated manpower attributes are developed using principal component regression (PCR). Aggregating these models, it is deduced that multiskilling could help redress skills shortage in the long term. A new trade equilibrium framework and a multiskilled focused partnership in training programme are prescribed with response strategies and recommendations.

No description available.

Este trabalho apresenta um estudo comparativo do poder de predição de quatro métodos de regressão adequados para situações nas quais os dados, dispostos na matriz de planejamento, apresentam sérios problemas de multicolinearidade e/ou de alta dimensionalidade, em que o número de covariáveis é maior do que o número de observações. No presente trabalho, os métodos abordados são: regressão por componentes principais, regressão por mínimos quadrados parciais, regressão ridge e LASSO. O trabalho engloba simulações, em que o poder preditivo de cada uma das técnicas é avaliado para diferentes cenários definidos por número de covariáveis, tamanho de amostra e quantidade e intensidade de coeficientes (efeitos) significativos, destacando as principais diferenças entre os métodos e possibilitando a criação de um guia para que o usuário possa escolher qual metodologia usar com base em algum conhecimento prévio que o mesmo possa ter. Uma aplicação em dados reais (não simulados) também é abordada. / This paper presents a comparative study of the predictive power of four suitable regression methods for situations in which data, arranged in the planning matrix, are very poorly multicolinearity and / or highdimensionality, wherein the number of covariatesis greater the number of observations. In this study, the methods discussed are: principal component regression,partial least squares regression,ridge regression and LASSO. The work includes simulations, where in the predictive power of each of the techniques is evaluated for different scenarios defined by the number of covariates, sample size and quantity and intensity ratios (effects) significant, high lighting the main dffierences between the methods and allowing for the creating a guide for the user to choose which method to use based on some prior knowledge that it may have. An applicationon real data (not simulated) is also addressed.

第22回名古屋大学年代測定総合研究センターシンポジウム平成21(2009)年度報告

Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2016-10-06T11:48:12Z No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T13:58:41Z (GMT) No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T13:58:47Z (GMT) No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5) / Made available in DSpace on 2016-10-20T13:58:52Z (GMT). No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5) Previous issue date: 2016-04-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / This paper presents a comparative study of the predictive power of four suitable regression methods for situations in which data, arranged in the planning matrix, are very poorly multicolinearity and / or high dimensionality, wherein the number of covariates is greater the number of observations. In this study, the methods discussed are: principal component regression, partial least squares regression, ridge regression and LASSO. The work includes simulations, wherein the predictive power of each of the techniques is evaluated for di erent scenarios de ned by the number of covariates, sample size and quantity and intensity ratios (e ects) signi cant, highlighting the main di erences between the methods and allowing for the creating a guide for the user to choose which method to use based on some prior knowledge that it may have. An application on real data (not simulated) is also addressed. / Este trabalho apresenta um estudo comparativo do poder de predi c~ao de quatro m etodos de regress~ao adequados para situa c~oes nas quais os dados, dispostos na matriz de planejamento, apresentam s erios problemas de multicolinearidade e/ou de alta dimensionalidade, em que o n umero de covari aveis e maior do que o n umero de observa c~oes. No presente trabalho, os m etodos abordados s~ao: regress~ao por componentes principais, regress~ao por m nimos quadrados parciais, regress~ao ridge e LASSO. O trabalho engloba simula c~oes, em que o poder preditivo de cada uma das t ecnicas e avaliado para diferentes cen arios de nidos por n umero de covari aveis, tamanho de amostra e quantidade e intensidade de coe cientes (efeitos) signi cativos, destacando as principais diferen cas entre os m etodos e possibilitando a cria c~ao de um guia para que o usu ario possa escolher qual metodologia usar com base em algum conhecimento pr evio que o mesmo possa ter. Uma aplica c~ao em dados reais (n~ao simulados) tamb em e abordada

No description available.

Pelchem, a commercial subsidiary of Necsa (South African Nuclear Energy Corporation), produces a range of commercial fluorocarbon products while driving research and development initiatives to support the fluorine product portfolio. One such initiative is to develop improved analytical techniques to analyse product composition during development and to quality assure produce. Generally the C–F type products produced by Necsa are in a solution of anhydrous HF, and cannot be directly analyzed with traditional techniques without derivatisation. A technique such as vibrational spectroscopy, that can analyze these products directly without further preparation, will have a distinct advantage. However, spectra of mixtures of similar compounds are complex and not suitable for traditional quantitative regression analysis. Multivariate data analysis (MVA) can be used in such instances to exploit the complex nature of spectra to extract quantitative information on the composition of mixtures. A selection of fluorocarbon alcohols was made to act as representatives for fluorocarbon compounds. Experimental design theory was used to create a calibration range of mixtures of these compounds. Raman and infrared (NIR and ATR–IR) spectroscopy were used to generate spectral data of the mixtures and this data was analyzed with MVA techniques by the construction of regression and prediction models. Selected samples from the mixture range were chosen to test the predictive ability of the models. Analysis and regression models (PCR, PLS2 and PLS1) gave good model fits (R2 values larger than 0.9). Raman spectroscopy was the most efficient technique and gave a high prediction accuracy (at 10% accepted standard deviation), provided the minimum mass of a component exceeded 16% of the total sample. The infrared techniques also performed well in terms of fit and prediction. The NIR spectra were subjected to signal saturation as a result of using long path length sample cells. This was shown to be the main reason for the loss in efficiency of this technique compared to Raman and ATR–IR spectroscopy. It was shown that multivariate data analysis of spectroscopic data of the selected fluorocarbon compounds could be used to quantitatively analyse mixtures with the possibility of further optimization of the method. The study was a representative study indicating that the combination of MVA and spectroscopy can be used successfully in the quantitative analysis of other fluorocarbon compound mixtures. / Thesis (M.Sc. (Chemistry))--North-West University, Potchefstroom Campus, 2012.

Pelchem, a commercial subsidiary of Necsa (South African Nuclear Energy Corporation), produces a range of commercial fluorocarbon products while driving research and development initiatives to support the fluorine product portfolio. One such initiative is to develop improved analytical techniques to analyse product composition during development and to quality assure produce. Generally the C–F type products produced by Necsa are in a solution of anhydrous HF, and cannot be directly analyzed with traditional techniques without derivatisation. A technique such as vibrational spectroscopy, that can analyze these products directly without further preparation, will have a distinct advantage. However, spectra of mixtures of similar compounds are complex and not suitable for traditional quantitative regression analysis. Multivariate data analysis (MVA) can be used in such instances to exploit the complex nature of spectra to extract quantitative information on the composition of mixtures. A selection of fluorocarbon alcohols was made to act as representatives for fluorocarbon compounds. Experimental design theory was used to create a calibration range of mixtures of these compounds. Raman and infrared (NIR and ATR–IR) spectroscopy were used to generate spectral data of the mixtures and this data was analyzed with MVA techniques by the construction of regression and prediction models. Selected samples from the mixture range were chosen to test the predictive ability of the models. Analysis and regression models (PCR, PLS2 and PLS1) gave good model fits (R2 values larger than 0.9). Raman spectroscopy was the most efficient technique and gave a high prediction accuracy (at 10% accepted standard deviation), provided the minimum mass of a component exceeded 16% of the total sample. The infrared techniques also performed well in terms of fit and prediction. The NIR spectra were subjected to signal saturation as a result of using long path length sample cells. This was shown to be the main reason for the loss in efficiency of this technique compared to Raman and ATR–IR spectroscopy. It was shown that multivariate data analysis of spectroscopic data of the selected fluorocarbon compounds could be used to quantitatively analyse mixtures with the possibility of further optimization of the method. The study was a representative study indicating that the combination of MVA and spectroscopy can be used successfully in the quantitative analysis of other fluorocarbon compound mixtures. / Thesis (M.Sc. (Chemistry))--North-West University, Potchefstroom Campus, 2012.