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Nonparametric and Semiparametric Regression for Independent Data

  • Hua Liang
Chapter
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Abstract

Consider the linear model
$$\displaystyle{y_{i} = \mathbf{x}_{i}^{T}\beta +\sigma _{ i}\varepsilon _{i},i = 1,\cdots \,,n,}$$
where β is an unknown parameter vector and the \(\{\varepsilon _{i}\}\) are i.i.d. errors. It is well known that ordinary least squares (LS) estimators are unbiased and consistent, but are not efficient when errors are heteroscedastic, and the usual standard error estimators of LS estimators are biased. Hence the usual confidence intervals and test statistics are biased and may lead to incorrect conclusions.

Keywords

Semiparametric Regression Independent Data Usual Confidence Interval Unknown Parameter Vector Least Squares 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hua Liang
    • 1
  1. 1.The George Washington UniversityWashington, DCUSA

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