Restricted minimax robust designs for misspecified regression models

Giseon Heo · Byron Schmuland · Douglas P. Wiens

24 citations0/0 files storedCanadian Journal of Statistics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Optimal Experimental Design Methods Advanced Statistical Methods and Models Statistical and numerical algorithms

Abstract

Abstract The authors propose and explore new regression designs. Within a particular parametric class, these designs are minimax robust against bias caused by model misspecification while attaining reasonable levels of efficiency as well. The introduction of this restricted class of designs is motivated by a desire to avoid the mathematical and numerical intractability found in the unrestricted minimax theory. Robustness is provided against a family of model departures sufficiently broad that the minimax design measures are necessarily absolutely continuous. Examples of implementation involve approximate polynomial and second order multiple regression.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 24 citations exist upstream.

References

1972Theory of optimal experiments 1972Theory of Optimal Experiments.1959A Basis for the Selection of a Response Surface Design 1971Optimal Experimental Design for Polynomial Regression 1992Minimax designs for approximately linear regression 1979The robustness and optimality of response surface designs 1982Optimal Robust Designs: Linear Regression in $R^k$1982Robust designs for nearly linear regression

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.