TY - JOUR
AU - NAGNUR, B. N.
AB - Abstract Neyman (1959) has defined a locally asymptotically most powerful test, called optimal asymptotic test, of composite statistical hypotheses. This test is particularly applicable when either the standard methods of testing are not available or when they are too difficult to compute. However, Neyman (1959) restricted his studies to the case of testing a one-dimensional parameter. In this paper his results are generalized to the case of testing a k-dimensional parameter. This necessitates introducing the notion of a locally asymptotically most stringent test (L.A.M.S.T.). The theory is developed in the spirit of Neyman's work, using his ideas and definitions omitting a number of details. It is illustrated by testing a general linear hypothesis involving multinomial parameters. We also give the L.A.M.S.T. when the observations are not independent identically distributed. An example from bio-assay is also given. This content is only available as a PDF. © Oxford University Press
TI - Locally asymptotically most stringent tests and Lagrangian multiplier tests of linear hypotheses
JO - Biometrika
DO - 10.1093/biomet/52.3-4.459
DA - 1965-12-01
UR - https://www.deepdyve.com/lp/oxford-university-press/locally-asymptotically-most-stringent-tests-and-lagrangian-multiplier-FF0Xd0UkMr
SP - 459
EP - 468
VL - 52
IS - 3-4
DP - DeepDyve
ER -