We deduce that a small value of the R 2 R 2 mathContainer Loading Mathjax statistic should not, in itself, be used to reject the usefulness of a regression model.doi:10.2139/ssrn.996664Kurz-Kim, Jeong-RyeolLoretan, MicoJournal of Econometrics...
To the material developed for that purpose, I have added the substance of two subsequent papers: "Efficient methods of estimating a regression equation with equi-correlated disturbances", and "The exact finite sample properties of estimators of coefficients in error components regression models" (with...
Local polynomial regressionErrors-in-variablesVarying coefficient models inherit the simplicity and easy interpretation of classical linear models while enjoying the flexibility of nonparametric models. They are very useful in analyzing the relation between a response and a set of predictors. There has ...
In a regression model, coefficients represent the relationship between the independent variables and the dependent variable. These coefficients are estimated by minimizing the error between the predicted values and the actual values in the dataset. However, when the model is not able to converge to a...
This article is concerned with the estimation of a varying-coefficient regression model when the response variable is sometimes missing and some of the covariates are measured with additive errors. We propose a class of estimators for the coefficient functions, as well as for the population mean ...
摘要: In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is n关键词: Correlated Predictors Screening False Discovery Rate High Dimensional Data Single Coefficient Test ...
In this article, we introduce a family of robust estimates for the parametric and nonparametric components under a generalised semiparametric varying coefficient partially linear regression model, where the data are modelled by y i |(x i , z i , u i ) F (路, i ) with for some known ...
摘要: In this paper we propose the conditional ridge-type estimator of regression coefficient in restricted linear regression model , we show that it is restricted admissible and superior to the restricted best linear unbiased estimator in terms of mean squares error and mean squares error matrix. ...
For linear models, the sums of the squared errors always add up in a specific manner: SS Regression + SS Error = SS Total. This seems quite logical. The variance that the regression model accounts for plus the error variance adds up to equal the total variance. Further, R-squared equals...
Calculating the constant would be the same as for the regression coefficients, except for using a different formula for the constant's standard error. Because the constant is seldom used in evaluating the regression model, no example is presented here. However, the constant is used in prediction...