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Title: Minimum Distance Estimation in Linear Regression Model
Version: 1.0
Description: Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
Depends: R (≥ 3.2.2)
License: GPL-2
LazyData: true
NeedsCompilation: no
Packaged: 2015-09-14 05:44:03 UTC; Jason
Author: Jiwoong Kim [aut, cre]
Maintainer: Jiwoong Kim <kimjiwo2@stt.msu.edu>
Repository: CRAN
Date/Publication: 2015-09-14 09:12:47

Performs minimum distance estimation in linear regression model: Y=Xb + error

Description

Performs minimum distance estimation in linear regression model: Y=Xb + error

Usage

LRMDE(Y, X)

Arguments

Y

- Response variable in linear regression model

X

- Explanatory variable in linear regression model

Value

Returns betahat - Minimum distance estimator of b

References

[1] Koul, H. L (1985). Minimum distance estimation in linear regression with unknown error distributions. Statist. Probab. Lett., 3 1-8.

[2] Koul, H. L (1986). Minimum distance estimation and goodness-of-fit tests in first-order autoregression. Ann. Statist., 14 1194-1213.

[3] Koul, H. L (2002). Weighted empirical process in nonlinear dynamic models. Springer, Berlin, Vol. 166

See Also

ARMDE

Examples

X <- matrix(c(1,1,3,4, 4,2), nrow=3, ncol=2)
Y <- c(1,-5, 8)
bhat <- LRMDE(Y,X)

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