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MINIMUM DISPARITY ESTIMATION IN LINEAR REGRESSION

MODELS: DISTRIBUTION AND EFFICIENCY

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RO JIN PAK^{1} AND AYANENDRANATH BASU^{2}

^{1} *Department of Statistics, Taejon University, Taejon 300-716, Korea*

^{2} *Applied Statistics Unit, Indian Statistical Institute, 203 B. T. Road, Calcutta 700 035, India*
(Received January 13, 1997; revised July 22, 1997)

**Abstract.**
This paper deals with the minimum
disparity estimation in linear regression models. The
estimators are defined as statistical quantities which
minimize the blended weight Hellinger distance between a
weighted kernel density estimator of errors and a smoothed
model density of errors. It is shown that the estimators of
the regression parameters are asymptotic normally
distributed and efficient at the model if the weights of
the density estimators are appropriately chosen.

**Abstract.**
Asymptotic efficiency,
blended weight Hellinger distance, kernel density
estimator, linear regression model.

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