Sköld, M. The asymptotic variance of the continuous-time kernel estimator with applications to bandwidth selection. (English) Zbl 0984.62029 Stat. Inference Stoch. Process. 4, No. 1, 99-117 (2001). Summary: We derive simple expressions for the asymptotic variance of the kernel-density estimator of a stationary continuous-time process in one and \(d\) dimensions and relate convergence rates to sample path smoothness. Important applications include methods for selecting optimal smoothing parameters and construction of confidence bands for testing hypotheses about the density. In a simulation study the results are applied to bandwidth selection for discrete-time processes that can be modelled as continuous-time processes sampled at a high rate. Cited in 6 Documents MSC: 62G07 Density estimation 62M09 Non-Markovian processes: estimation 62G20 Asymptotic properties of nonparametric inference Keywords:kernel estimate; dependent data; continuous time; bandwith selection; asymptotic variance Software:KernSmooth PDFBibTeX XMLCite \textit{M. Sköld}, Stat. Inference Stoch. Process. 4, No. 1, 99--117 (2001; Zbl 0984.62029) Full Text: DOI