Volume 15, 2011
|Page(s)||139 - 167|
|Published online||05 January 2012|
Expansions for the distribution of M-estimates with applications to the Multi-Tone problem
Applied Mathematics Group, Industrial Research Limited,
Lower Hutt, New Zealand.
2 School of Mathematics, University of Manchester, Manchester M13 9PL, UK; email@example.com
Revised: 9 March 2009
Revised: 4 June 2009
We give a stochastic expansion for estimates that minimise the arithmetic mean of (typically independent) random functions of a known parameter θ. Examples include least squares estimates, maximum likelihood estimates and more generally M-estimates. This is used to obtain leading cumulant coefficients of needed for the Edgeworth expansions for the distribution and density n1/2 ( of − θ0) to magnitude n−3/2 (or to n−2 for the symmetric case), where θ0 is the true parameter value and n is typically the sample size. Applications are given to least squares estimates for both real and complex models. An alternative approach is given when the linear parameters of the model are nuisance parameters. The methods are illustrated with the problem of estimating the frequencies when the signal consists of the sum of sinusoids of unknown amplitudes.
Mathematics Subject Classification: 62E17 / 62E20
Key words: Bias / edgeworth / maximum likelihood / M-estimates / Skewness
© EDP Sciences, SMAI, 2011
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