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 542 for “"linear models"”.
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Diagnostics for generalized linear models
… In this thesis we look at how the generalized linear model has become one of the most important developments in statistics in the last thirty years, and on the adequacy of regression model diagnostics that are meaningful and significant in a generalized linear model context. Some asymptotic …
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Stochastic curtailment method under linear models.
Stochastic curtailment method under linear models.
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Some applications of generalised linear models
… extensions to and applications of generalised linear models and their implementation in a statistical package. The principal extension considered is the inclusion of extra parameters in the link function of the model in order to create a family of parametric link functions. This technique is …
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Monthly Streamflow Generation With Linear Models
Made available in DSpace on 2014-12-13T19:31:39Z (GMT). No. of bitstreams: 1 7726771.pdf: 6358270 bytes, checksum: cef309d8a9c6eaa3eb62673af66c0dcc (MD5) Previous issue date: 1977
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Transformed-linear models for time series extremes
… nonnegative regularly-varying time series models that are constructed similarly to classical non-extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly-varying time …
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Linear Models for Multivariate Repeated Measures Data
… and p x p respectively. We develop a general linear model approach to accommodate both balanced and unbalanced repeated measures data.</p> <p>Our main results are: (1) construction of Rao's score test for a simpler model with p=1 (univariate case) and V<sub>ij</sub> having a structure as in a …
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A study of estimators in linear models
… thesis is a study of Estimators, particularly in Linear Models. The newest technology of Bootstrap Methodology is employed in the estimation procedure. We present a survey of the Bootstrap Methodology in the beginning and move on to some serious problems in Linear Model estimation procedure. We …
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Generalized Linear Models with Nonignorable Missing Covariates
… thesis, we present an overview of generalized linear models (GLMs) for binary and count data with missing covariates when the missing data mechanism is nonignorable. We use the maximum likelihood method to estimate the parameters in GLMs. We study a set of ML estimating equations for fitting …
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Sequential decision making with feature-linear models
… Our focus is on methods that model the reward as linear in some feature space. We consider a bandit problem, where the rewards are linear in a reproducing kernel Hilbert space, and a reinforcement learning setting with features given by a neural network. The thesis is split into two parts …
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Invariant estimation with application to linear models
… estimation of the parameters in the general linear model under the (nuisance) group of scale changes on the dependent and independent variables. The invariant estimator of the regression coefficient is found to be a"standardized regression coefficient,'' but this standardized regression …
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Approximate cross validation for sparse generalized linear models
Cross validation (CV) is an effective yet computationally expensive tool for assessing the out of sample error for many methods in machine learning and statistics. Previous work has shown that methods to approximate CV can be very accurate and computationally cheap, but only for low dimensional …
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Bayesian D-Optimal Design for Generalized Linear Models
… D-optimal designs for multi-variable generalized linear models. Particularly, Poisson regression models and logistic regression models are investigated. Designs are examined for a range of prior distributions and the equivalence theorem is used to verify the design optimality. Design efficiency …
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On the Efficiency of Designs for Linear Models in Non-regular Regions and the Use of Standard Desings for Generalized Linear Models
… designs for non-regular regions for first order models with interaction for the two- and three-factor case, and using the standard designs in the case of generalized linear models (GLM). The Fraction of Design Space (FDS) technique is proposed as a new graphical evaluation technique that …
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P-VALUE BASED VARIABLE SELECTION FOR GENERALIZED LINEAR MODELS
… 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 can select important variables for …
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Structuring Representations in Deep Learning: Symmetries and Linear Models
… examine the implicit bias of gradient descent on linear group convolutional networks (G-CNNs), which provide a model for learning highly structured representations. For such architectures, we prove that gradient descent implicitly minimizes the net’s Schatten norm in Fourier space [Lawrence et …
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Inference of high-dimensional linear models with time-varying coefficients
… inference algorithm for high-dimensional linear models with time-varying coefficients and dependent error processes. The method is based on a novel combination of the nonparametric kernel smoothing technique and a Lasso bias-corrected ridge regression estimator using a bias-variance …
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Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models
Discrete phylogeographic models enable the inference of the geographic history of biological organisms along phylogenetic trees. Frequently applied in the context of epidemiological modelling, phylogeographic generalised linear models were developed to allow for the evaluation of multiple …
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A new approach to fitting linear models in high dimensional spaces
This thesis presents a new approach to fitting linear models, called “pace regression”, which also overcomes the dimensionality determination problem. Its optimality in minimizing the expected prediction loss is theoretically established, when the number of free parameters is infinitely large. In …
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Linear and log-linear models based on generalized inverse sampling scheme
… negative binomial and negative multinomial (NMn) models are applicable when there is only one rare category in the population. Here, a new model, based on generalized inverse sampling scheme, is introduced to study several rare events simultaneouly. The generalized inverse sampling scheme is used …
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