Work detail

White Test for the Least Weighted Squares

Author: Bc. Věra Bludská
Year: 2011 - summer
Leaders: prof. RNDr. Jan Ámos Víšek CSc.
Consultants:
Work type: Bachelors
Language: English
Pages: 48
Awards and prizes: B.A. with distinction from the Dean of the Faculty of Social Sciences for an extraordinarily good bachelors diploma thesis.
Link:
Abstract: The Least Weighted Squares (LWS) is a robust method for computing coefficients in
linear regression models. An inherent problem of LWS is the complexity of its estimator
and, consequently, the lack of an analytical solution or fast exact algorithms for its
evaluation. To remedy this situation a novel exact algorithm running in polynomial
time has been proposed. The algorithm implemented in MATLAB programming
language has been employed for testing computationally more efficient non-exact LWS
methods. In addition to many potential uses of LWS in robust econometrics (e.g. outlier
diagnostics) the method has been applied to the problem of regression estimation in the
presence of heteroscedasticity. It has been demonstrated that the combined use of the
LWS estimator and White's test for heteroscedasticity significantly improves the
efficiency of the robust regression estimation.
Downloadable: Bachelor Thesis of Bludská
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