Stein regressionmulticollinearityLogistic regression using conditional maximum likelihood estimation has recently gained widespread use. Many of the applications of logistic regression have been in situations in
stepwise regressionTo compare their performance on high dimensional data, several regression methods are applied to data sets in which the number of exploratory variables greatly exceeds the sample sizes. The methods are stepwise regression, principal components regression, two forms of latent root ...
multivariate simple linear regressionmulticollinearityThe dependence relationship between two sets of variables is a subject of interest in statistical field. A frequent obstacle is that several of the explanatory variables will vary in rather similar ways. As a result, their collective power of ...
collinear variables SE/Robust vce(vcetype) vcetype may be oim or robust Reporting level(#) nocnsreport display options set confidence level; default is level(95) do not display constraints control column formats, row spacing, display of omitted variables and base and empty cells, and factor-...
In this case, the rank of XTX is less than K, and XTX is singular and cannot be inverted; hence, it is not possible to calculate the OLS estimates of the regression coefficients with Equation (7). Usually (e.g., near-infrared (NIR) spectra), the x-variables are neither completely ...
The maximum likelihood estimator (MLE) suffers from the instability problem in the presence of multicollinearity for a Poisson regression model (PRM). In this study, we propose a new estimator with some biasing parameters to estimate the regression coefficients for the PRM when there is multicollinea...
For each explanatory variable xi, one calculates i = Z,xi + x,i{i = 1, 2, … , M} (1b) and the residuals ex,i are used as explanatory variables in subsequent analyses. Traditionally, MLR is used to calculate the regression vectors from Eqs. (1a) and (1b) needed to adjust ...
Regression tends to give very unstable and unreliable regression weights when predictors are highly collinear. Several methods have been proposed to counter this problem. A subset of these do so by finding components that summarize the information in the predictors and the criterion variables. The ...
In principal components regression, the response y plays no role in determining the transformed predictor variables. One wonders whether an approach that incorporates the response in the construction of the transformed predictors from the very beginning would work better. This is where partial least ...
principal components regressionstein estimateSeveral Estimators (ridge, princlpal components, generalizedinverse and stein)have been proposed as alternatives to least squares fur the multiple linear regression model when the independent variables are multicollinear. These methods differ in the way they ...