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Activity Number: 345
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #305729
Title: On Nonparametric Curve Estimation with Spatial Data
Author(s): Sucharita Ghosh*+
Companies: Swiss Federal Research Institute WSL
Address: Zuercherstrasse 111, CH-8903 Birmensdorf, , Switzerland
Keywords: spatial process ; kernel smoothing ; latent processes ; local stationarity

We consider kernel smoothing for spatial data that are transformations of unobserved Gaussian processes with location dependent marginal distributions of arbitrary shapes. We address bandwidth selection, extrapolation in the vicinity & change point estimation. In another context, Ghosh (2009) considers a Gaussian subordination on a lattice with long memory & short memory correlations; Hallin et al. (2004) & Robinson (2011) provide further results for curve estimation with spatial data; Beran et al. (2009) consider estimation in a long memory lattice process. We present some asymptotic results and examples from environmental research.


Beran, J., Ghosh, S., Schell, D. (2009) Least square estimation for stationary lattice processes with long-memory. Journal of Multivariate Analysis, 100: 2178-2194.

Ghosh, S. (2009) The unseen species number revisited. Sankhya, 71-B, 2: 137-150.

Hallin, M., Lu, Z., Tran L.T. (2004) Kernel density estimation for spatial processes: the L_1 theory. Journal of Multivariate Analysis, 88, 61-75.

Robinson, Peter (2011) Asymptotic theory for nonparametric regression with spatial data. Journal of econometrics, 165 (1). pp. 5-19.

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