On optimality of designs with three distinct eigenvalues

Abstract

Let v,b,k denote the family of all connected block designs with v treatments and b blocks of size k. Let d∈v,b,k. The replication of a treatment is the number of times it appears in the blocks of d. The matrix C(d)=R(d)-1kN(d)N(d) is called the information matrix of d where N(d) is the incidence matrix of d and R(d) is a diagonal matrix of the replications. Since d is connected, C(d) has v-1 nonzero eigenvalues μ1(d),...,μv-1(d). Let be the class of all binary designs of v,b,k. We prove that if there is a design d*∈ such that (i) C(d*) has three distinct eigenvalues, (ii) d* minimizes trace of C(d)2 over d∈, (iii) d* maximizes the smallest nonzero eigenvalue and the product of the nonzero eigenvalues of C(d) over d∈, then for all p>0, d* minimizes (Σi=1v-1μi(d)-p)1/p over d∈. In the context of optimal design theory, this means that if there is a design d*∈ such that its information matrix has three distinct eigenvalues satisfying the condition (ii) above and that d* is E- and D-optimal in , then d* is p-optimal in for all p>0. As an application, we demonstrate the p-optimality of certain group divisible designs. Our proof is based on the method of KKT conditions in nonlinear programming.

0

Turn this paper into a lesson

ArcXiv compiles a structured reading guide from this paper's metadata: plain-English importance, contributions, prerequisite concepts, which sections to read first, flashcards, and a quiz. Grounded in the abstract, never invented.

Discussion (0)

Sign in to join the discussion.

Loading comments…