Bounds on Rounding Errors in Linear Regression Modelsdoi:10.2307/2348515Wolfgang PolasekJournal of the Royal Statistical Society. Series D (The Statistician)
Measurement errorEstimating equationGMMIn a linear mean regression setting with repeated measurement errors, we develop asymptotic properties of a naive estimator to better clarify the effects of these errors. We then construct a group of unbiased estimating equations with independent repetitions and make...
Calculates slope and intercept for linear regression of data with errors in X and Y. The errors can be specified as varying point to point, as can the correlation of the errors in X and Y. The uncertainty in the slope and intercept are also estimated. This follows the method in D. ...
Linear Regression with Errors in Both Variables: A Proper Bayesian ApproachTom Minka
Summary We consider the linear regression model with observation error in the design. In this setting, we allow the number of covariates to be much larger than the sample size. Several new estimation methods have been recently introduced for this model. Indeed, the standard lasso estimator or ...
In this case, also the standard errors, which are equal to the square roots of the diagonal entries of the covariance matrix, are said to be heteroskedasticity-robust. The linear regression Consider thelinear regressionmodel where: is the dependent variable; ...
Regression models play a dominant role in analyzing several data sets arising from areas like agricultural experiment, space experiment, biological experiment, financial modeling, etc. One of the major strings in developing the regression models is the assumption of the distribution of the error terms...
, for Multiple Linear Regression and , for Nonlinear Regression - Levenberg-Marquardt algorithm. Here n is the number of observations and p is the number of parameters. I would like to know if the above formulae are correct. Why aren't the errors associated...
In this study we investigate the problem of estimation and testing of hypotheses in multivariate linear regression models when the errors involved are assumed to be non-normally distributed. We consider the class of heavy-tailed distributions for this purpose. Although our method is applicable for ...
In the presence of heteroscedasticity, OLS estimates are unbiased, but the usual tests of significance are inconsistent. However, tests based on a heteroscedasticity consistent covariance matrix (HCCM) are consistent. While most applications using a HCCM appear to be based on the asymptotic version ...