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Linearized Ridge Regression Estimator in Linear Regression
Communications in Statistics - Theory and Methods, 2011In this article, we aim to study the linearized ridge regression (LRR) estimator in a linear regression model motivated by the work of Liu (1993). The LRR estimator and the two types of generalized Liu estimators are investigated under the PRESS criterion.
Feng Gao, Xu-Qing Liu
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2001
A linear regression model assumes that the regression function E(Y|X) is linear in the inputs X 1,..., X p . Linear models were largely developed in the precomputer age of statistics, but even in today’s computer era there are still good reasons to study and use them.
Jerome H. Friedman +2 more
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A linear regression model assumes that the regression function E(Y|X) is linear in the inputs X 1,..., X p . Linear models were largely developed in the precomputer age of statistics, but even in today’s computer era there are still good reasons to study and use them.
Jerome H. Friedman +2 more
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2010
Publisher Summary This chapter introduces the use of the regression model to make inferences on means of populations identified by specified values of one or more quantitative factor variables. It discusses the uses of the linear regression model and explains the procedures for the estimation of the parameters of that model and the subsequent ...
Donna L. Mohr +2 more
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Publisher Summary This chapter introduces the use of the regression model to make inferences on means of populations identified by specified values of one or more quantitative factor variables. It discusses the uses of the linear regression model and explains the procedures for the estimation of the parameters of that model and the subsequent ...
Donna L. Mohr +2 more
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Correlation and Simple Linear Regression
2011Up until now in this book, you have been dealing with the situation in which you have had only one group or two groups of events or objects in your research study and only one measurement (i.e., variable) “number” on each of these. This chapter asks you to change gears again and to deal with the situation in which you are measuring two variables ...
Simone M. Cummings, Thomas J. Quirk
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1998
In this chapter, we study extensively the estimation of a linear relationship between two variables, Y i and X i , of the form: $${Y_i} = \alpha + \beta {X_i} + {u_i}\;i = 1,2, \ldots ,n$$ (3.1) where Y i denotes the i-th observation on the dependent variable Y which could be consumption, investment or output, and X i denotes the i-th ...
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In this chapter, we study extensively the estimation of a linear relationship between two variables, Y i and X i , of the form: $${Y_i} = \alpha + \beta {X_i} + {u_i}\;i = 1,2, \ldots ,n$$ (3.1) where Y i denotes the i-th observation on the dependent variable Y which could be consumption, investment or output, and X i denotes the i-th ...
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Grouping and linear regression
Journal of Chronic Diseases, 1982With a large number of observations, the method of grouping is often employed to provide simpler graphs or tables. When one investigates the relationship between two variables, one usually groups based on the magnitude of the independent variable, and then plots the dependent variable averages against independent variable averages to get a clearer ...
Max Halperin +2 more
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International Journal of Injury Control and Safety Promotion, 2018
Simple linear regression models study the relationship between a single continuous dependent variable Y and one independent variable X (Bangdiwala, 2018).
Shrikant I. Bangdiwala +2 more
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Simple linear regression models study the relationship between a single continuous dependent variable Y and one independent variable X (Bangdiwala, 2018).
Shrikant I. Bangdiwala +2 more
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2015
Two variates may be related to each other, often one causing the other. The question about how much the X variate changes (increases or decreases) for a one-unit change in Y is analyzed by regression analysis. This chapter deals with how to determine the slope of a straight line that fits the XY points, how to determine an estimate of the variability ...
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Two variates may be related to each other, often one causing the other. The question about how much the X variate changes (increases or decreases) for a one-unit change in Y is analyzed by regression analysis. This chapter deals with how to determine the slope of a straight line that fits the XY points, how to determine an estimate of the variability ...
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Understanding Linear Regression
PM&R, 2013Multivariate regression is a powerful statistical technique that allows researchers to explore multiple predictors simultaneously, to adjust for confounding, to test for interactions, and to improve predictions. Commonly used regression models include linear regression, logistic regression, and Cox regression.
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