Nonparametric Regression and Bandwidth Selection for Spatial Models
Nonparametric Double Conditional Smoothing for 2D Surfaces
tools:::Rd_package_title("DCSmooth")
Assign a Kernel Function
Print a list of available kernels in the DCSmooth package
Contour Plot for the Double Conditional Smoothing
Summarize Results from Double Conditional Smoothing
Print and Summarize Options for Double Conditional Smoothing
Print the Summary of a DCS estimation
Print the Summary of a "sarma"/"sfarima" object
Residuals of "dcs"-object
Estimation of an SARMA-process
Simulation of a -process
Set Options for the DCS procedure
Estimation of a SFARIMA-process
Simulation of a -process
Summarizing Results from Double Conditional Smoothing
Print and Summarize Options for Double Conditional Smoothing
Summarizing SARMA/SFARIMA Estimation or Simulation
3D Surface Plot of "dcs"-object or numeric matrix
Nonparametric smoothing techniques for data on a lattice and functional time series. Smoothing is done via kernel regression or local polynomial regression, a bandwidth selection procedure based on an iterative plug-in algorithm is implemented. This package allows for modeling a dependency structure of the error terms of the nonparametric regression model. Methods used in this paper are described in Feng/Schaefer (2021) <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021) <https://ideas.repec.org/p/pdn/ciepap/143.html>.