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 13 of 13 for “"Selection consistency"”.
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Model selection: Consistency and robustness properties of the Schwarz Information Criterion for generalized M-estimation
… of qualitative robustness appropriate for model selection is provided and it is shown that the crucial restriction needed to achieve robustness is the uniform boundedness of the objective function defining Bias robust M-estimators. In this process, the asymptotic performance of the SIC for …
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High dimensional feature selection under interactive models
… the popularity of high dimensional feature selection. High dimensional feature selection aims to select relevant features from the suspected feature space by removing redundant features. Among high feature selection studies, a large number have considered main effects only, although …
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Scalable sparsity structure learning using Bayesian methods
… sparsity structure in various models. Estimation consistency and selection consistency of our methods are established. First, a nonparametric Bayes estimator is proposed for the problem of estimating a sparse sequence based on Gaussian random variables. We adopt the popular two-group prior with …
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Variable selection for high-dimensional complex data
… 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 missing data …
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Group transformation and identification with kernel methods and big data mixed logistic regression
… covariance operators. The statistical consistency of the estimates has been established. We refer to the proposed framework and approach as the Optimal Kernel Group Transformation (OKGT) method.</p> <p>Secondly, we define the true additive group structure for OKGT when the response …
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A penalized linear mixed model with generalized method of moments estimators for complex phenotype prediction
… have oracle properties, including variable selection consistency, estimation consistency, and asymptotic normality. We further develop a hybrid screening rule that constitutes of the sequential strong rule and the enhanced dual polytope projection rule to reduce data dimension and improve …
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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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High-dimensional classification and attribute-based forecasting
… method is known for simultaneous variable selection and classification. However, the performance of this method declines as the number of variables increases. With this concern, in the first study, we propose a new classification approach that employs the penalized logistic regression …
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Mixed Effects Modeling and Correlation Structure Selection for High Dimensional Correlated Data
… random effects and correlation structure selection for high-dimensional data. In longitudinal studies, mixed-effects models are important for addressing subject-specific effects. However, most existing approaches assume normal distributions for the random effects, which could affect the …
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Statistical methods for learning sparse features
… (VB), is proposed and it can be shown to achieve selection consistency when both p and n go to infinity. Empirical studies have demonstrated the competitive performance of the proposed algorithm.
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Essays on Model Selection Uncertainty and Model Averaging: Computational and Empirical Work with Beta Regression, Multiple Linear Regression with ARMA Innovations, and the Minimum Description Length Principle
Uncertainty in model selection is under-explored and frequently resolved non-rigorously through beliefs about generalizability, practical usefulness, and computational ease. This is problematic as model selection routinely admits multiple models which imposes extra uncertainty on all post-selection …
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Change point detection for high dimensional data and valid inference for Bayesian linear models
… of tuning parameters and we derive bootstrap consistency under the null. We extend the theory results to testing multiple change points and provide the justification for the size and power. For estimation of unknown change point locations, we utilize the wild binary segmentation algorithm. …
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Essays on Econometrics and Policy Evaluation
… in a linear factor model framework, a new model selection consistency result and show that the penalized procedure has a faster mean squared error convergence rate. Through a simulation study, I then show that the sparse synthetic control achieves lower bias and has better post-treatment …