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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 400 for “"Model Selection"”.
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Model Selection with Information Criteria
This thesis is on model selection using information criteria. The information criteria include generalized information criterion and a family of Bayesian information criteria. The properties and improvement of the information criteria are investigated. We analyze nonasymptotic and asymptotic …
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Bayesian generalized additive model selection
Generalized additive models (GAMs) offer a parsimonious, flexible and interpretable framework for regression, particularly when handling a large numbers of candidate predictors. This thesis addresses the GAM variable selection problem: categorizing each candidate predictor's effect type to be …
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Stein Estimation and Model Selection
… to use Stein type estimators for statistical model selection purposes. First, a parameter truncation criterion developed in conjunction with the new Stein estimator (Stein, 1981) is used in an orthonormal linear statistical model setting, as a basis for simultaneously selecting the model and …
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Model selection in compositional spaces
We often build complex probabilistic models by composing simpler models-using one model to generate parameters or latent variables for another model. This allows us to express complex distributions over the observed data and to share statistical structure between dierent parts of a model. In this …
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Spatially Correlated Model Selection (SCOMS)
In this dissertation, a variable selection method for spatial data is developed. It is assumed that the spatial process is non-stationary as a whole but is piece-wise stationary. The pieces where the spatial process is stationary are called regions. The variable selection approach accounts for two …
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Predictive Alternatives in Bayesian Model Selection
Model comparison and hypothesis testing is an integral part of all data analyses. In this thesis, I present two new families of information criteria that can be used to perform model comparison. In Chapter 1, I review the necessary background to motivate the thesis. Of particular interest is the …
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Model Selection for Stochastic Block Models
… for complex systems, networks (graphs) model entities and their interactions as nodes and edges. In many real-world networks, nodes divide naturally into functional communities, where nodes in the same group connect to the rest of the network in similar ways. Discovering such communities …
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High-dimensional econometrics and model selection
… to biased estimators. We propose a LASSO based selection procedure in order to choose the informative moments and then, using the selected moments, conduct optimal GMM. My method can significantly reduce the bias of the optimal GMM estimator while retaining most of the information in the full …
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On Some Aspects of Model Selection Variability
… the data analytic approach to integrate the model selection uncertainty into the statistical inferences of high dimensional estimators. Two closed-form formulae of covariance matrices are derived for high dimensional bagging estimators, one for the nonparametric bootstrapping and the other …
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Critical study of AIC model selection techniques
Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-01-03T19:52:20Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Tan_MingYangJeremy.pdf: 761560 bytes, checksum: 1ec1ec8cdd591f86a9ba94337abe5c0a (MD5)
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Model Selection, Uniform Inference and Nonparametric Regression
Model selection in the nonparametric regression model is inevitable since any nonparametric estimator requires tuning parameters to be specified in order for it to be feasible. It is, however, standard practice to carry over the theory of nonparametric estimators when the model is fixed to the case …
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Model selection-regression and time series applications
… In practice researcher will propose one model or a group of competing models that attempts to explain the data being investigated. This process is known as model selection. Model selection techniques have been developed to aid researchers in finding a suitable approximation to the true …
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Hoeffding Races--model selection for MRI classification
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Model selection and estimation in high dimensional settings
… we propose new statistical methods to achieve model selection and estimation when there are more predictors than observations. We also design a new set of algorithms to efficiently solve the proposed statistical models. We apply the implemented methods to genetic data sets of cancer patients …
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Meta-Learning and the Full Model Selection Problem
… data preprocessing, outlier detection, feature selection, learning algorithm and evaluation techniques, for a given data project. This indeed was an enjoyable job at the beginning, because to me finding patterns and valuable information from data is always fun. Things become tricky when several …
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Impact of data-dependent model selection on inference
… thesis, I consider the problem of accounting for model uncertainty in a parametric regression model with focus on the uncertainty involved in selection of the optimal transformation of a continuous predictor in the Cox proportional hazards model (Cox, 1972). I use the minimum AIC approach to …
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Bayesian model selection with applications to radio astronomy
… focus on Bayesian methods and the problem of model selection in particular. The first part investigates a new approach to computing the Bayes factor for model selection without needing to compute the Bayesian evidence, while the second part shows, through an analytical calculation of the …
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Multiset Model Selection and Averaging, and Interactive Storytelling
… extend the sampler and the surrounding theory to model selection problems. In such problems efficient exploration of the model space becomes a challenge since independent and ad-hoc proposals might not be able to jointly propose multiple parameter sets which correctly explain a new pro- posed …
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Hierarchical Gaussian Processes for Spatially Dependent Model Selection
In this dissertation, we develop a model selection and estimation methodology for nonstationary spatial fields. Large, spatially correlated data often cover a vast geographical area. However, local spatial regions may have different mean and covariance structures. Our methodology accomplishes three …
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