| 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)