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.
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Showing 1 to 20 of 193 for “"variable selection."”.
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INFERENCE AFTER VARIABLE SELECTION
… beta_1 x_1 + ... + beta_p x_p + e after model or variable selection, including prediction intervals for a future value of the response variable Y_f, and testing hypotheses with the bootstrap. If n is the sample size, most results are for n/p large, but prediction intervals are developed that may …
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Variable selection in discrete survival models
Selection of variables is vital in high dimensional statistical modelling as it aims to identify the right subset model. However, variable selection for discrete survival analysis poses many challenges due to a complicated data structure. Survival data might have unobserved heterogeneity leading to …
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Scalable algorithms for Bayesian variable selection
This Dissertation was approved for publication on 2016-07-14 at 08:43.
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Fast algorithms for Bayesian variable selection
Variable selection of regression and classification models is an important but challenging problem. There are generally two approaches, one based on penalized likelihood, and the other based on Bayesian framework. We focus on the Bayesian framework in which a hierarchical prior is imposed on all …
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Semiparametric Bayesian Joint Model With Variable Selection
… In this dissertation, we consider the problem of variable selection in a joint modeling framework where longitudinal and survival data are modeled jointly. Dirichlet process priors are used to relax the parametric assumption of random effects, which has advantages of making the model more robust …
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Objective bayesian variable selection for censored data
… selecting a set of regressors when the response variable follows a parametric model (such as Weibull or lognormal) and observations are right censored. Under a Bayesian approach, the most widely used tools are the Bayes Factors (BFs) which are, however, undefined when using improper priors. Some …
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Partially Bayesian Variable Selection in Classification Trees
… First, by de-emphasizing certain subsets of variables during the estimation process, unnecessary computational activity can be avoided. Second, by giving an expert's preferred variables priority, we reduce the chance that a spurious variable will appear in the model. Hence, our resulting …
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Variable selection for high-dimensional complex data
… involve high-dimensional complex data, where variable selection plays an important role for model construction. In this thesis, we address the following challenging issues for the variable selection problem: variable selection consistency when irrepresentable conditions fail, block-wise …
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Model-Free Variable Selection through Sufficient Dimension Reduction
… the fields of sufficient dimension reduction and variable selection to develop new theory and methods for model-free variable selection. After developing the natural connection between sufficient dimension reduction and model-free variable selection we introduce two approaches to select …
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P-VALUE BASED VARIABLE SELECTION FOR GENERALIZED LINEAR MODELS
… presents two new p-value based methods for variable selection in generalized linear models. Generalized linear models are widely used, but their non-analytic solutions and intricate dependencies create challenges for many existing methods. Addressing these issues, our proposed contributions …
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Bayesian variable selection in high dimensional censored regression models
… development in technologies drives research in variable selection in various fields, especially in bio-medical areas where high-dimensional gene expression data are present. Various approaches have been developed for associating patients' data with patients' survival times, however, not many can …
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Prognostic models for mesothelioma : variable selection and machine learning
… cross validation. In addition, other methods of variable selection and machine learning were investigated to build different types of predictive models. These analyses used a random training set from the newer data set. These models were evaluated using N-fold cross validation and the best of …
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Variable Selection for DNA Methylation Data Using Model-Based Clustering
<p>A variable selection approach was investigated through simulations and applied to DNA methylation data generated from the Isle of Wight birth cohort study. The approach featured the use of clustering with a penalty function to select informative variables. We evaluated the method by conducting …
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Variable Selection and Decision Trees: The DiVaS and ALoVaS Methods
… trees. Our approach facilitates covariate selection explicitly in the model, something not present in previous research. We define a transformation that allows us to use priors from linear models to facilitate covariate selection in decision trees. Using this transform, we modify many …
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Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection
… degradation test (ADDT) data; and 3) spatial variable selection and application to Lyme disease data in Virginia. Followed by the general introduction in Chapter 1, the rest of the dissertation consists of three main chapters. Chapter 2 presents the construction of two-sided simultaneous …
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Bayesian Model Averaging and Variable Selection in Multivariate Ecological Models
… As a result, variance components due to model selection are estimated and accounted for, contrary to the practice of conventional data analysis (such as, for example, stepwise model selection). In addition, variable activation probabilities can be obtained for each variable of interest. This …
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Variable selection for wind turbine condition monitoring and fault detection system
… This thesis focuses on the development of variable selection algorithm, such that the dimensionality of the monitoring data can be reduced, while useful information in relation to the later fault diagnosis and prognosis is preserved. The research started with a background and review of the …
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Bayesian Computation for Variable Selection and Multivariate Forecasting in Dynamic Models
… of this dissertation develops novel methods for variable selection in which models are scored and weighted based on specific forecasting and decision goals. In the time series setting, standard marginal likelihoods correspond to 1−step forecast densities, and considering alternate objectives is …
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Variable selection in logistic regression, with special application to medical data
In this thesis, the various methods of variable selection which have been proposed in the statistical, epidemiological and medical literature for prediction and estimation problems in logistic regression will be described. The procedures will be applied to medical data sets. On the basis of the …
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