Estimating random integrals from noisy observations: samplingdesigns and their performance

The problem of estimating a weighted average of a random process from noisy observations at a finite number of sampling points is considered. The performance of sampling designs with optimal or suboptimal, but easily computable, estimator coefficients is studied. Several examples and special cases are studied, including additive independent noise, nonlinear distortion with noise, and quantization noise