Unlike proportional hazards models, the regression parameter estimates from AFT models are robust to omitted covariates.

The β's are the regression parameters to be estimated.

The test is based on the method proposed by Rolla Edward Park for estimating linear regression parameters in the presence of heteroscedastic error terms.

Further, x is a vector of independent variables for the jth observation and β is a vector of regression parameters.

The regression based interval mapping parameterization thus provides a mechanism to test for additive and dominance effects using tests of the regression parameters.

With aggregated data the modifiable areal unit problem can cause extreme variation in regression parameters.

Univariate normality is not needed for least squares estimates of the regression parameters to be meaningful (see Gauss-Markov theorem).

Here η is an intermediate variable representing a linear combination, containing the regression parameters, of the explanatory variables.

They can always be made to vanish by introducing a new regression parameter for each common factor.

However, no optimal HWB solution exists if the random variables do not contain enough information on all of the new regression parameters.