KoulMde: Koul's Minimum Distance Estimation in Linear Regression and Autoregression Model by Coordinate Descent Algorithm

Consider linear regression model and autoregressive model of order q where errors in the linear regression model and innovations in the autoregression model are independent and symmetrically distributed. Hira L. Koul (1986) <DOI:10.1214/aos/1176350059> proposed a nonparametric minimum distance estimation method by minimizing L2-type distance between certain weighted residual empirical processes. He also proposed a simpler version of the loss function by using symmetry of the integrating measure in the distance. This package contains three functions: KoulLrMde(), KoulArMde(), and Koul2StageMde(). The former two provide minimum distance estimators for linear regression model and autoregression model, respectively, where both are based on Koul's method. These two functions take much less time for the computation than those based on parametric minimum distance estimation methods. Koul2StageMde() provides estimators for regression and autoregressive coefficients of linear regression model with autoregressive errors through minimum distant method of two stages.

Author
Jiwoong Kim [aut, cre]
Date of publication
2016-11-10 13:16:52
Maintainer
Jiwoong Kim <kimjiwo2@stt.msu.edu>
License
GPL-2
Version
2.2.0

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Man pages

Koul2StageMde
Two-stage minimum distance estimation in linear regression...
KoulArMde
Minimum distance estimation in the autoregression model of...
KoulLrMde
Minimum distance estimation in linear regression model.

Files in this package

KoulMde
KoulMde/NAMESPACE
KoulMde/R
KoulMde/R/MdeFunc.R
KoulMde/MD5
KoulMde/DESCRIPTION
KoulMde/man
KoulMde/man/KoulLrMde.Rd
KoulMde/man/Koul2StageMde.Rd
KoulMde/man/KoulArMde.Rd