= Haar projection estimator in Gaussian white noise
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{title2=$\widehat\Pi_{V_J}f=\sum_k\left(\int\phi_{J,k}\,dY\right)\phi_{J,k}$}
In the <Gaussian white noise model>, estimate a <Haar scaling function> coefficient by $\int\phi_{J,k}\,dY$. The resulting <Haar projection> estimator is an <unbiased estimator> with <independent> coefficient errors of <variance> $1/n$. On each cell it is the observed path increment divided by the cell length. This gives a finite-dimensional estimator without imposing smoothness on the drift.
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