Package: KoulMde
Title: Koul's Minimum Distance Estimation in Linear Regression and
        Autoregression Model by Coordinate Descent Algorithm
Version: 3.1.0
Author: Jiwoong Kim <kimjiwo2@stt.msu.edu>
Maintainer: Jiwoong Kim <kimjiwo2@stt.msu.edu>
Description: 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. Kim
    (2018) <DOI:10.1080/00949655.2017.1392527> proposed a fast computational method
    which enables practitioners to compute the minimum distance estimator of the vector
    of general multiple regression parameters for several integrating measures. 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.
    The new version is written in Rcpp and dramatically reduces computational time.
Depends: R (>= 3.2.2)
License: GPL-2
LazyData: TRUE
Imports: Rcpp (>= 0.12.7), expm
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-05-29 21:42:06 UTC; Jason
Repository: CRAN
Date/Publication: 2018-05-29 22:36:10 UTC
