James Hannan

On Estimation and Adaptive Estimation for Locally Asymptotically Normal Families

SummaryLocally asymptotically minimax (LAM) estimates are constructed for locally asymptotically normal (LAN) families under very mild additional assumptions. Adaptive estimation is also considered and a sufficient condition is given for an estimate to be locally asymptotically minimax adaptive. Incidently, it is shown that a well known lower bound due to Hájek (1972) for the local asymptotic minimax risk is not sharp.

Identification of the Ordered Bivariate Normal Distribution by Minimum Variate

Abstract For full-rank covariance matrix and other subfamilies of the ordered bivariate normal distribution, the identifiability of parameters by the minimum variate is demonstrated. For the general family, the distribution is not identified. These identifiability questions arise in connection with estimation within certain econometric models, for example, Tobin and Fair-Jaffee.

Bounds for robust maximum likelihood and posterior consistency in compound mixture state experiments

Uniform bounds on rates of L1-consistency for the empiric mean of state sequences in a family of probability models (compound with finite-mixture-state component) are obtained for MLEs (Section sec2) and posterior means for quasi-uniform hyperpriors (Section sec3), both determined in the iid mixture (empirical Bayes) sub-models. Qualitative aspects of results of this type were described by Robbins (1951). Application to the Gilliland and Hannan (1974/86) restricted-risk-finite-state-component compound decision problem (Section sec4) yields uniform bounds on rates of asymptotic regret of Bayes solutions therein (with extension to mixture-state by expectation), giving strong affirmation to an asymptotic form of a Robbins (1951) conjecture. The general extension to mixture-state components (Remark rem4.1) strengthens much of the existing compound literature.