Add to ORKGMultiple regression with correlated explanatory variables is relevant to a broad range of problems in the physical, chemical, and engineering sciences. Chemometricians, in particular, have made heavy use of principal components regression and related procedures for predicting a response variable from a large number of highly correlated variables. In this paper we develop a general theory for selecting principal components that yield estimates of regression coefficients with low mean squared error. Our numerical results suggest that the theory also can be used to improve partial least squares regression estimators and regression estimators based on rotated principal components. Although our work has been motivated by the statistical genetics...
The logistic regression model is used to predict a binary response variable in terms of a set of exp...
In regression with near collinear explanatory variables, the least squares predictor has large varia...
A common practice in many scientific disciplines is to take measurements on several different variab...
Multiple regression with correlated explanatory variables is relevant to a broad range of problems i...
Principal component regression has been perceived as a remedy for multi-collinearity. Cook (2007) su...
In regression problems where the number of predictors greatly exceeds the number of observations, co...
A method for multivariate regression is proposed that is based on the simultaneous least-squares min...
In genomewide association studies (GWAS), population stratification (PS) is a major confounding fact...
In the behavioral sciences, researchers often link a criterion to multiple predictors, using multipl...
The regression coefficient estimates from ordinary least squares (OLS) have a low probability of bei...
2002 Mathematics Subject Classification: 62J05, 62G35.In classical multiple linear regression analys...
This thesis focuses on the investigation of partial least squares (PLS) method- ology to deal with h...
AbstractIn this paper we investigate the algebraic relationships between some of the more familiar e...
The interpretation of a principal component analysis can be complicated because the components are l...
Many human traits are highly correlated. This correlation can be leveraged to improve the power of g...
The logistic regression model is used to predict a binary response variable in terms of a set of exp...
In regression with near collinear explanatory variables, the least squares predictor has large varia...
A common practice in many scientific disciplines is to take measurements on several different variab...
Multiple regression with correlated explanatory variables is relevant to a broad range of problems i...
Principal component regression has been perceived as a remedy for multi-collinearity. Cook (2007) su...
In regression problems where the number of predictors greatly exceeds the number of observations, co...
A method for multivariate regression is proposed that is based on the simultaneous least-squares min...
In genomewide association studies (GWAS), population stratification (PS) is a major confounding fact...
In the behavioral sciences, researchers often link a criterion to multiple predictors, using multipl...
The regression coefficient estimates from ordinary least squares (OLS) have a low probability of bei...
2002 Mathematics Subject Classification: 62J05, 62G35.In classical multiple linear regression analys...
This thesis focuses on the investigation of partial least squares (PLS) method- ology to deal with h...
AbstractIn this paper we investigate the algebraic relationships between some of the more familiar e...
The interpretation of a principal component analysis can be complicated because the components are l...
Many human traits are highly correlated. This correlation can be leveraged to improve the power of g...
The logistic regression model is used to predict a binary response variable in terms of a set of exp...
In regression with near collinear explanatory variables, the least squares predictor has large varia...
A common practice in many scientific disciplines is to take measurements on several different variab...