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Linear Regression Models with Heteroscedastic Errors (Paperback)
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Linear Regression Models with Heteroscedastic Errors (Paperback)
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In this some new estimation methods and testing procedures for the
linear regression models with heteroscedastic disturbances. A
Minimum Norm Quadratic Unbiased (MINQU) estimation method has been
developed for estimating the unknown heteroscedastic error
variances by using the weighted studentized residuals. A
multiplicative heteroscedastic linear regression model has been
specified and a method of estimating the parameters of linear
regression model along with the in the heteroscedastic error
variance has been given by using the predicted residuals. Three
types of modified estimators have been proposed for the parameter
of multiplicative heteroscedastic error variance by using
internally studentized residuals.an adaptive method of estimation
has been suggested to estimate the heteroscedastic error variances
based on Bartlett's test by using the internally studentized
residuals. Besides these new estimation methods, the testing
procedures for testing the equality between the regression
coefficients in two/sets of linear regression models under
heteroscedasticity have been suggested by using the studentized
residuals.
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