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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 2089 for “"linear regression"”.
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Bayesian quantile linear regression
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive relationship between the response variable and the explanatory variables is desired. The traditional frequentists’ approach to quantile regression was well developed with asymptotic theories and …
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Parameter Estimation In Linear Regression
… resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the …
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Functional Linear Regression in High Dimensions
Functional linear regression has occupied a central position in the area of functional data analysis, and attracted substantial research attention in the past decade. With increasingly complex data of this type collected in modern experiments, we conduct further investigations in response to the …
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Outliers in a Linear Regression Model
Over the last several decades the linear regression model has become one of the most widely used tools of the social sciences and the physical sciences. Given the data, the least squares method gives information for statistical inferences. However, the researcher frequently feels that the …
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Robust Statistical Modeling In Functional Linear Regression
Functional linear regression is a prominent field within the domain of functional data analysis, with extensive applications in various domains such as biomedical studies, brain imaging, and chemometrics. However, despite the abundance of literature on functional linear regression, limited …
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Simultaneous confidence bands in linear regression analysis
… on the plausible range of an<br/>unknown regression model. For a simple linear regression model, the most frequently<br/>quoted bands in the statistical literature include the two-segment band, the three-segment<br/>band and the hyperbolic band, and for a multiple linear regression model, …
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An investigation into Functional Linear Regression Modeling
… data reduction, model evaluation, functional linear modeling and forecasting methods. FDA is applicable in numerous applications such as Bioscience, Geology, Psychology, Sports Science, Econometrics, Meteorology, etc. This dissertation main objective is to focus more specifically on Functional …
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A short cut method for linear regression
… the so-called “Group Averages method" in the linear regression, the quadratic regression, and the functional relation situations. In the linear and quadratic regression situations, under the assumption of X<sub>i</sub> equally spaced, the efficiency of the Group Averages estimator is quite …
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High-dimensional Linear Regression Problems via Graphical Models
… thesis 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 …
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Estimation For Simple Linear Regression With Exponentially Distributed Errors
In general, the theory developed in the area of linear regression analysis assumes that the error ∊ is normally distributed with mean zero and variance σ². In this thesis, we examine the results when the error ∊ is exponentially distributed with scale parameter ϴ. We derive both the maximum …
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Efficient Estimation of Linear Regression Models With Autocorrelated Errors
Made available in DSpace on 2014-12-14T06:06:04Z (GMT). No. of bitstreams: 1 7804091.pdf: 5452448 bytes, checksum: 10c8e65fdd1bc2bcffec86dfdd8aa565 (MD5) Previous issue date: 1977
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Functional linear regression on Namibian and South African data
… geographically spate populations with functional regression analysis using climate variables at each location. A number of statistical challenges present themselves such as the multivariate nature of the data. Functional data analysis was used in this project to display the data so as to highlight …
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Geometric optimization algorithms for linear regression on fixed-rank matrices
… optimization viewpoint to address the problem of linear regression in nonlinear and high-dimensional matrix search spaces. Our purpose is to efficiently exploit the geometric structure of the search space in the design of scalable linear regression algorithms. Our search space of main interest …
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Identifying outliers and influential observations in general linear regression models
… the influence of observations on least squares regression results. Since outliers can arise in different ways, the above mentioned measures are based on motivational arguments and they are designed to measure the influence of observations on different aspects of various regression results. In …
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A Comparison of Natural Gas Spot Price Linear Regression Forecasting Models
… EIA model are compared to the results of other linear regression models.
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In-situ prediction on sensor networks using distributed multiple linear regression models
… computing a matrix pseudoinverse and multiple linear regression model on a sensor network, (2) three applications of these algorithms with associated field experiments demonstrating their versatility, (3) a sensor network architecture and implementation for river flood prediction as well as …
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A study of the effect of non-normal distributions upon simple linear regression
LD2668 .T4 1966 W635
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