Doctor of Philosophy / Department of Statistics / Weixing Song / The regression model has been given a considerable amount of attention and played a
significant role in data analysis. The usual assumption in regression analysis is that the
variances of the error terms are constant across the data. Occasionally, this assumption of
homoscedasticity on the variance is violated; and the data generated from real world applications
exhibit heteroscedasticity. The practical importance of detecting heteroscedasticity
in regression analysis is widely recognized in many applications because efficient inference
for the regression function requires unequal variance to be taken into account. The goal of
this thesis is to propose new testing procedures to assess the adequacy of fitting parametric
variance function in heteroscedastic regression models.
The proposed tests are established in Chapter 2 using certain minimized L[subscript]2 distance
between a nonparametric and a parametric variance function estimators. The asymptotic
distribution of the test statistics corresponding to the minimum distance estimator under
the fixed model and that of the corresponding minimum distance estimators are shown to
be normal. These estimators turn out to be [sqrt]n consistent. The asymptotic power of the
proposed test against some local nonparametric alternatives is also investigated. Numerical
simulation studies are employed to evaluate the nite sample performance of the test in one
dimensional and two dimensional cases.
The minimum distance method in Chapter 2 requires the calculation of the integrals
in the test statistics. These integrals usually do not have a tractable form. Therefore,
some numerical integration methods are needed to approximate the integrations. Chapter
3 discusses a nonparametric empirical smoothing lack-of-fit test for the functional form
of the variance in regression models that do not involve evaluation of integrals. empirical
smoothing lack-of-fit test can be treated as a nontrivial modification of Zheng (1996)'s
nonparametric smoothing test and Koul and Ni (2004)'s minimum distance test for the
mean function in the classic regression models. The asymptotic normality of the proposed
test under the null hypothesis is established. Consistency at some fixed alternatives and
asymptotic power under some local alternatives are also discussed. Simulation studies are
conducted to assess the nite sample performance of the test. The simulation studies show
that the proposed empirical smoothing test is more powerful and computationally more
efficient than the minimum distance test and Wang and Zhou (2006)'s test.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/10744 |
| Date | January 1900 |
| Creators | Samarakoon, Nishantha Anura |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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