Robust design with application and implementation

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My work focuses on constructing the experimental designs for regression problems. Good designs should lead to small biases in the estimates when the fitted model is incorrect, and should also be efficient (i.e., lead to small variances) in the estimates when the fitted model is in fact correct. I intend to work on the design constructions for the regression problem with a very general setup. The regression responses are considered to be a function of a linear function. We watch for the situation where the fitted model may not exactly represent the truth. Other possible departures from the usual assumptions are also taken into account. Errors may not equally vary as they are assumed, for instance.