Adaptive estimator

In statistics, an adaptive estimator is an estimator in a parametric or semiparametric model with nuisance parameters such that the presence of these nuisance parameters does not affect efficiency of estimation.

Definition

Formally, let parameter θ in a parametric model consists of two parts: the parameter of interest ν ∈ N ⊆ R^k, and the nuisance parameter η ∈ H ⊆ R^m. Thus θ = (ν,η) ∈ N×H ⊆ R^k+m. Then we will say that $\scriptstyle {\hat {\nu }}_{n}$ is an adaptive estimator of ν in the presence of η if this estimator is regular, and efficient for each of the submodels

${\mathcal {P}}_{\nu }(\eta _{0})={\big \{}P_{\theta }:\nu \in N,\,\eta =\eta _{0}{\big \}}.$

Adaptive estimator estimates the parameter of interest equally well regardless whether the value of the nuisance parameter is known or not.

The necessary condition for a regular parametric model to have an adaptive estimator is that

$I_{\nu \eta }(\theta )=\operatorname {E} [\,z_{\nu }z_{\eta }'\,]=0\quad {\text{for all }}\theta ,$

where z_ν and z_η are components of the score function corresponding to parameters ν and η respectively, and thus I_νη is the top-right k×m block of the Fisher information matrix I(θ).

Example

Suppose $\scriptstyle {\mathcal {P}}$ is the normal location-scale family:

${\mathcal {P}}={\Big \{}\ f_{\theta }(x)={\tfrac {1}{{\sqrt {2\pi }}\sigma }}e^{-{\frac {1}{2\sigma ^{2}}}(x-\mu )^{2}}\ {\Big |}\ \mu \in \mathbb {R} ,\sigma >0\ {\Big \}}.$

Then the usual estimator ${\hat {\mu }}\,=\,{\bar {x}}$ is adaptive: we can estimate the mean equally well whether we know the variance or not.

Basic references

• Bickel, Peter J.; Chris A.J. Klaassen; Ya’acov Ritov; Jon A. Wellner (1998). Efficient and adaptive estimation for semiparametric models. Springer: New York. ISBN 978-0-387-98473-5.{{cite book}}: CS1 maint: publisher location (link)

Other useful references

• I. V. Blagouchine and E. Moreau: "Unbiased Adaptive Estimations of the Fourth-Order Cumulant for Real Random Zero-Mean Signal", IEEE Transactions on Signal Processing, vol. 57, no. 9, pp. 3330–3346, September 2009.