Deficiencies Between Linear Normal Experiments

Abstract

Let $X_1, \cdots, X_n$ be independent and normally distributed variables, such that $0 < \operatorname{Var} X_i = \sigma^2, i = 1, \cdots, n$ and $E(X_1, \cdots, X_n)' = A'\beta$ where $A$ is a $k \times n$ matrix with known coefficients and $\beta = (\beta_1, \cdots, \beta_k)'$ is an unknown vector. $\sigma$ may be known or unknown. Denote the experiment obtained by observing $X_1, \cdots, X_n$ by $\mathscr{E}_A.$ Let $A$ and $B$ be matrices of dimension $n_A \times k$ and $n_B \times k.$ The deficiency $\delta(\mathscr{E}_A, \mathscr{E}_B)$ is computed when $\sigma$ is known and for some cases, including the case $BB' - AA'$ positive semidefinite and $AA'$ nonsingular, also when $\sigma$ is unknown. The technique used consists of reducing to testing a composite hypotheses and finding a least favorable distribution.