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Biometrika 1985 72(2):419-428; doi:10.1093/biomet/72.2.419
© 1985 by Biometrika Trust
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The foundations of finite sample estimation in stochastic processes

V. P. GODAMBE

Department of Statistics and Actuarial Science, University of Waterloo Waterloo, Ontario, N2L 3G1, Canada

The Gauss-Markov theorem on least squares for linear models derives its general applicability because it depends on the underlying distribution only through the first two moments. In this paper, a similar theorem is established within the context of stochastic processes. Various problems of finite sample estimation are solved by application of this theorem.

Key Words: Conditional least squares • Estimating function • Finite sample optimality • Likelihood function • Observed Fisher information • Score function


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