Testing goodness-of-fit via order selection criteria /.
| Main Author: | |
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| Other Authors: | , , |
| Format: | Thesis Book |
| Language: | English |
| Published: |
1992.
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| Subjects: | |
| Online Access: | Link to OAKTrust copy ProQuest, Abstract |
| Abstract: | The objective of this research is to investigate the problem of goodness-of-fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The first proposed test statistic λ[α] is itself a smoothing parameter which is selected to minimize an estimated MISE for a truncated series estimator of the comparison density function. Therefore, this test statistic leads immediately to a point estimate of the density function in the event that H[0] is rejected. The limiting distribution of λ[α] was obtained under the null hypothesis. It was also shown that this test is consistent against fixed alternatives. The other new test statistic is essentially a Neyman smooth test that uses an estimated smoothing parameter to choose the number of term s in the statistic. In our simulation study, we found this test to have excellent empirical power properties. |
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| Item Description: | "Major subject: Statistics." Vita. |
| Physical Description: | viii, 93 leaves : illustrations ; 28 cm |
| Bibliography: | Includes bibliographical references. |