Global ETD Search

11	Computer-aided methods for analyzing multiple-row effects in linear regression models Garner, Deborah Gail 08 1900 (has links) No description available. Regression analysis
12	Some problems in multiple regression. Cairns, Malcolm Bernard January 1972 (has links) No description available. Regression analysis
13	A numerical study of penalized regression Yu, Han 22 August 2013 (has links) In this thesis, we review important aspects and issues of multiple linear regression, in particular on the problem of multi-collinearity. The focus is on a numerical study of different methods of penalized regression, including the ridge regression, lasso regression and elastic net regression, as well as the newly introduced correlation adjusted regression and correlation adjusted elastic net regression. We compare the performance and relative advantages of these methods. penalized regression
14	A numerical study of penalized regression Yu, Han 22 August 2013 (has links) In this thesis, we review important aspects and issues of multiple linear regression, in particular on the problem of multi-collinearity. The focus is on a numerical study of different methods of penalized regression, including the ridge regression, lasso regression and elastic net regression, as well as the newly introduced correlation adjusted regression and correlation adjusted elastic net regression. We compare the performance and relative advantages of these methods. penalized regression
15	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. Ridge Regression
16	Treatment of autocorrelated disturbances in economic functions Fernandez, Jose Enrique 11 October 1972 (has links) Graduation date: 1973 Regression analysis
17	Split-line regression techniques. Glowik, John. January 1977 (has links) (PDF) Thesis (M.Sc.1977) from the Department of Stastistics, University of Adelaide. Regression analysis.
18	Simulation of flight operations and pilot duties in LANTIRN fighter squadrons using Simkit Azimetli, Mustafa. January 2008 (has links) (PDF) Thesis (M.S. in Modeling, Virtual Environments, and Simulation)--Naval Postgraduate School, June 2008. / Thesis Advisor(s): Buss, Arnold. "June 2008." Description based on title screen as viewed on August 26, 2008. Includes bibliographical references (p. 87-88). Also available in print. Regression analysis.
19	Robust inferential procedures applied to regression / Agard, David B., January 1990 (has links) Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaves 159-161). Also available via the Internet. Regression analysis.
20	Comparison and evaluation of the effect of outliers on ordinary least squares and Theil nonparametric regression with the evaluation of standard error estimates for the Theil nonparametric regression method / Wasser, Thomas E. January 1998 (has links) Thesis (Ph. D.)--Lehigh University, 1999. / Includes vita. Bibliography: leaves 68-69. Regression analysis.

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