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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Outlier Resistant Model Robust Regression

Assaid, Christopher Ashley 14 April 1997 (has links)
Parametric regression fitting (such as OLS) to a data set requires specification of an underlying model. If the specified model is different from the true model, then the parametric fit suffers to a degree that varies with the extent of model misspecification. Mays and Birch (1996) addressed this problem in the one regressor variable case with a method known as Model Robust Regression (MRR), which is a weighted average of independent parametric and nonparametric fits to the data. This paper was based on the underlying assumption of "well-behaved" (Normal) data. The method seeks to take advantage of the beneficial aspects of the both techniques: the parametric, which makes use of the prior knowledge of the researcher via a specified model, and the nonparametric, which is not restricted by a (possibly misspecified) underlying model. The method introduced here (termed Outlier Resistant Model Robust Regression (ORMRR)) addresses the situation that arises when one cannot assume well-behaved data that vary according to a Normal distribution. ORMRR is a blend of a robust parametric fit, such as M-estimation, with a robust nonparametric fit, such as Loess. Some properties of the method will be discussed as well as illustrated with several examples. / Ph. D.
2

Accounting for context and lifetime factors a new approach for evaluating regression testing techniques /

Do, Hyunsook. January 1900 (has links)
Thesis (Ph.D.)--University of Nebraska-Lincoln, 2007. / Title from title screen (site viewed July 9, 2007). PDF text:180 p. : ill. (some col.) UMI publication number: AAT 3250075. Includes bibliographical references. Also available in microfilm and microfiche formats.
3

Imputation by neural networks and related methods

Zhao, Xinqiang January 2002 (has links)
No description available.
4

Ridge Estimation and its Modifications for Linear Regression with Deterministic or Stochastic Predictors

Younker, James 19 March 2012 (has links)
A common problem in multiple regression analysis is having to engage in a bias variance trade-off in order to maximize the performance of a model. A number of methods have been developed to deal with this problem over the years with a variety of strengths and weaknesses. Of these approaches the ridge estimator is one of the most commonly used. This paper conducts an examination of the properties of the ridge estimator and several alternatives in both deterministic and stochastic environments. We find the ridge to be effective when the sample size is small relative to the number of predictors. However, we also identify a few cases where some of the alternative estimators can outperform the ridge estimator. Additionally, we provide examples of applications where these cases may be relevant.
5

Ridge Estimation and its Modifications for Linear Regression with Deterministic or Stochastic Predictors

Younker, James 19 March 2012 (has links)
A common problem in multiple regression analysis is having to engage in a bias variance trade-off in order to maximize the performance of a model. A number of methods have been developed to deal with this problem over the years with a variety of strengths and weaknesses. Of these approaches the ridge estimator is one of the most commonly used. This paper conducts an examination of the properties of the ridge estimator and several alternatives in both deterministic and stochastic environments. We find the ridge to be effective when the sample size is small relative to the number of predictors. However, we also identify a few cases where some of the alternative estimators can outperform the ridge estimator. Additionally, we provide examples of applications where these cases may be relevant.
6

Semiparametric failure-time regression with mismeasured or missing covariates /

Hu, Chengcheng. January 2001 (has links)
Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 136-141).
7

On the biased estimation in regression studies on ridge-type estimation /

Leskinen, Esko, January 1980 (has links)
Thesis (Ph. D.)--University of Jyväskylä. / Extra t.p., with thesis statement, inserted. Summary in Finnish. Includes bibliographical references (p. 113-115).
8

Regression : when a nonparametric approach is most fitting / When a nonparametric approach is most fitting

Claussen, Pauline Elma Clara 21 August 2012 (has links)
This paper aims to demonstrate the benefits of adopting a nonparametric regression approach when the standard regression model is not appropriate; it also provides an overview of circumstances where a nonparametric approach might not only be beneficial, but necessary. It begins with a historical background on regression, leading into a broad discussion of the standard linear regression model assumptions. Following are particular methods to handle assumption violations which include nonlinear transformations, nonlinear parametric model fitting, and, finally, nonparametric methods. The software package, R, is used to illustrate examples of nonparametric regression techniques for continuous variables and a brief overview is given of procedures to handle nonparametric regression models that include categorical variables. / text
9

Two problems involving regression analysis

Pratley, Kenneth G. (Kenneth George) January 1969 (has links)
No description available.
10

A study of augmentation methods for locally weighted regression models

Williamson, JulieAnne 08 1900 (has links)
No description available.

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