Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 69 for “"Ridge regression"”.
-
A comparison of three prediction based methods of choosing the ridge regression parameter k
A solution to the regression model y = xβ+ε is usually obtained using ordinary least squares. However, when the condition of multicollinearity exists among the regressor variables, then many qualities of this solution deteriorate. The qualities include the variances, the length, the stability, and …
-
Spatial variation of the food price index in Canada's Northwest Territories: a ridge regression analysis
… of statistical methods, in this case multiple regression analysis, to relatively minor changes in the definitions of some variables from a previous study. It also suggests that the wide range of interpretations sometimes observed in the literature pertaining to high prices in the NWT, may well …
-
The simulation analysis on the accuracy and efficiency of MIT transaction index
… paper aims to investigate the improvement that Ridge regression brings to the commercial real estate index estimation. The MIT transaction based index extends the use of the last model by Fisher, Gatzlaff, Geltner and Haurin (2003.) They address the sample selection bias and the noise effect of …
-
Scalable sparsity structure learning using Bayesian methods
… classification. In theory, we not only build a bridge to connect the estimation error of the mean difference and the classification error in different scenarios, also provide sufficient conditions of sub-optimal classifiers and optimal classifiers. Third, we study adaptive ridge regression for …
-
Development and Calibration of Relative Value Trading Models
… rv-NSS * Chapter 3 - Compares the widest set of ridge regression estimators ever assembled - Modified (r-k) Class Ridge Regression (MCRR) did not specify how to estimate all of its parameters, two methods to address this were introduced - Improved Ridge Estimators (IRE) had convergence problems, …
-
Forecasting Wind Direction Across Very Short and Short Term Time Horizons for Wind Turbine Control
… autoregressive moving average (ARMA), and ridge regression. The models were tested on several time horizons (Δ𝑇) that are relevant to turbine control and operation, ranging from 30 seconds to 2 hours. In this thesis, persistence demonstrated the highest predictive accuracy among the three …
-
Essays on Algorithmic Learning and Uncertainty Quantification
… convergence rates for non-parametric logistic regression in non-convex models. The second essay, titled “Kernel Ridge Regression Inference,” introduces a new technique for deriving sharp, non-asymptotic, uniform Gaussian approximation for partial sums in a reproducing kernel Hilbert space, …
-
Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression
… prediction methods built using Support Vector Regression (SVR) and Pearson Correlation Coefficient (PCC) to perform SNPs selection and then predict unobserved phenotype using ridge regression and SVR. The investigation shows that ranking SNPs by SVR significantly increases predictive accuracy …
-
High-dimensional Linear Regression Problems via Graphical Models
… introduces a new method for solving the linear regression problem where the number of observations n is smaller than the number of variables (predictors) v. In contrast to existing methods such as ridge regression, Lasso and Lars, the proposed method uses the idea of graphical models and …
-
Statistical Modeling for High-dimensional Compositional data with Applications to the Human Microbiome
… models, including LASSO, elastic net, ridge regression and cumulative logit model, to identify the most important subset of variables. This dissertation is structured as follows: </p> <p>Chapter 1 introduces the compositional microbiome data, and then briefly review different …
-
Relationships among heifer traits, early-life productive traits, and lifetime productivity within Angus and Simmental female cattle
… equations for fertility using logistic regression methodology. For productive performance, three methodologies were compared: least squares, ridge regression and principal components regression. For heifers of both breeds, fertility was associated with birth date, weaning weight ratio …
-
Optimal and sequential design for bridge regression with application in organic chemistry
… shrinkage methods, known collectively as bridge regression, with emphasis on the two special cases of ridge regression and the lasso. The application is the problem of understanding and predicting the melting point of small molecule organic compounds using chemical …
-
Integration of Energy Storage into a Future Energy System with a High Penetration of Distributed Photovoltaic Generation
… structure. The ESU designed is evaluated using ridge regression for price forecasting; Ridge regression applied to overcome numerical stability and overfitting issues associated with the large number of highly correlated predictors. Heuristics are developed to speed convergence of simulated …
-
Inference of high-dimensional linear models with time-varying coefficients
… smoothing technique and a Lasso bias-corrected ridge regression estimator using a bias-variance decomposition to address non-stationarity in the model. A hypothesis testing setup with familywise error control is presented alongside synthetic data and a real application to fMRI data for …
-
Optimizing deep learning networks using multi-armed bandits
… trees, SVM, Naïve Bayes, LDA, QDA, logistic regression, Gaussian process classifier, kernel ridge regression, LASSO regression, linear regression, Bayesian Ridge regression, boosting, bagging and random forests. The results on the data sets show that some of the new methods (i) generalize …
-
Penalised regression for high-dimensional data: an empirical investigation and improvements via ensemble learning
… likelihood methods are widely used to tackle regression problems in these high-dimensional settings. In this thesis, we carry out an extensive empirical comparison of the performance of popular penalised regression methods in high-dimensional settings and propose new methodology that uses …
-
Diversity-inducing probability measures for machine learning
… DPPs to kernel matrix approximation and kernel ridge regression.
-
Assessment of Future Impact of Climate Change on Structural Protections and Hydrological Extremes – Floods
… series realization, principal components, and ridge regression. The model was applied at four stations in the Manawatu catchment to downscale daily rainfall. The performance of the SDCRR model was compared with that of the widely used statistical downscaling model (SDSM). The performance of two …
-
A Multiple Classifier System for Predicting Best-Selling Amazon Products
… Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be …
-
A Review of 'Big Data' Variable Selection Procedures For Use in Predictive Modeling
… on ‘big data.’ For instance, multiple linear regression models cannot be used on datasets with hundreds of variables. However several techniques are becoming common tools for selective inference as the need for analyzing big data increases. Forward selection and penalized regression models …
Page 1 of 4