Some Model Robust Designs in Regression

Model-Robust Designs for Quantile Regression

We give methods for the construction of designs for regression models, when the purpose of the investigation is the estimation of the conditional quantile function, and the estimation method is quantile regression. The designs are robust against misspecified response functions, and against unanticipated heteroscedasticity. The methods are illustrated by example, and in a case study in which the...

Robust Inference in Fuzzy Regression Discontinuity Designs

Fuzzy regression discontinuity (RD) design and instrumental variable(s) (IV) regression share similar identification strategies and numerically yield the same results under certain conditions. While the weak identification problem is widely recognized in IV regressions, it has drawn much less attention in fuzzy RD designs, where the standard t-test can also suffer from asymptotic size distortio...

Robust Designs for Poisson Regression Models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these ...

Model-Robust Factorial Designs

Robust Designs for Approximately Polynomial Regression Abbreviated title: Robust Polynomial Designs

Abstract We study designs for the regression model E[Y jx] = Pp 1 j=0 jx+x (x), where (x) is unknown but bounded in absolute value by a given function (x). This class of response functions models departures from an exact polynomial response. We consider the construction of designs which are robust, with respect to various criteria, as the true response varies over this class. The resulting desi...