Density estimation from noisy observations of a stochastic process
Centre for Mathematical Sciences
Lund Institute of Technology,
We consider a new approach to non-parametric density estimation for stochastic
processes observed with noise. By assuming the data are sampled from a smooth
continuous-time process plus independent noise we reconstruct the original
process by regression analysis and estimate the density by continuous-time
kernel methods. We find asymptotic bias and variance of the estimator and
apply to finding rate-optimal sampling schemes.
Non-parametric density estimation, kernel smoothing, errors-in-variables,
deconvolution, dependent data, interpolation.