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Showing 1 to 14 of 14 for “"Principal component regression"”.
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Principal Component Regression for Construction of Wing Weight Estimation Models
… and analyzed. Even though the benefits of using principal component regression with cross validation are only demonstrated by the wing weight data fitting problem, the proposed methodology could have significant advantages in fitting other historical or hard-to-obtain data.</p>
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Antarctic Station-based Pressure Reconstructions from 1905-2011 using Principal Component Regression
… Antarctic stations back to 1905, based on the Principal Component Regression (PCR). The PCR model uses only Southern Hemisphere mid-latitude pressure observations used as predictors. Several independent validation techniques are used to examine the level of accuracy of the PCR models, such as …
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Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data
… Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and Principal Component Regression (PCR). A total of 10 predictors that are associated with the response from 10 sensor channels are used to train and test the classifiers. A …
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Generalizable Methods for Modeling Lumbar Spine Kinematics
… lumbar vertebral kinematics was developed using principal component regression applied to <em>in vivo</em> vertebral measurement data across the range of flexion and extension joint motion. This principal component-based approach offers unique advantages for predicting and interpreting …
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Health disparities, environmental toxicants, and midlife women’s health outcomes
… this issue is three-fold; first ordinal logistic regressions were applied to the Midlife Women’s Health Study (MWHS) to understand the relationship between health, demographic, and lifestyle factors on quality of life between racial and minority groups at midlife. Secondly, to understand phthalate …
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Modelling the Supply and Demand for Construction and Building Services Skills in the Black Country
… 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 …
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Aplicación de técnicas quimiométricas a la resolución de señales electroquímicas solapadas
… multivariate calibration methods: Multilinear Regression (MLR), Partial-Least Squares Regression (PLS) and Principal Component Regression (PCR).The chemometric methodology developed has been applied to certain electrochemical systems of different complexity: the first one, inorganic, …
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The construction of a partial least squares biplot
… methods, such as Correspondence Analysis (CA), Principal Component Analysis (PCA), Canonical Variate Analysis (CVA) and Discriminant Analysis (DA), as a form of graphical display of data. Another possible employment is in Partial Least Squares (PLS). First introduced as a regression method, PLS …
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Forensic Discrimination, Age Estimation, and Spectral Optimization For Trace Detection of Blood On Textile Substrates Using Infrared Spectroscopy and Chemometrics
… changes were modeled as a function of time with principal component regression (PCR) while interval PCR (iPCR) was used to locate the optimal spectral regions associated with the changes due to blood aging.</p> <p>The sampling and collection of spectra was evaluated in order to decrease the …
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Ecological Sustainability of Winter Harvesting in the Duck Mountain Provincial Park, SK: The Effects of Skidder Traffic, Slash Loading, and Cumulative Effects on Soils and Aspen Regeneration
… harvested blocks. To assess cumulative effects, principal component analysis, principal component regression, and fuzzy logic analysis were used to determine the regeneration suitability across harvested blocks. This analysis indicated that the majority of a harvested block (51-71% of the area) …
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Algorithms for quantum simulation: design, analysis, implementation, and application
… quantum simulation and apply it to implement principal component regression. We also apply our new analysis of product formulas and obtain improved quantum Monte Carlo simulations of the transverse field Ising model and quantum ferromagnets.
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Radiomics of Nsclc: Quantitative Ct Image Feature Characterization and Tumor Shrinkage Prediction
… Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and …
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DEVELOPMENT AND APPLICATION OF NOVEL COMPUTATIONAL INTELLIGENCE TECHNIQUES TO THE MULTIVARIATE ANALYSIS OF METABOLOMICS BIOFLUIDS DATASETS
… classification of metabolic datasets. Moreover, principal component regression (PCR) was also employed for data iv probabilistic classification and regression purposes. This was followed by investigations of correlations between these biomolecular diseases features. Furthermore, the tri-ranking …