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The Gradient Test

Another Likelihood-based Test

By: Artur Lemonte

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Paperback | 2 February 2016 | Edition Number 1

At a Glance

Paperback
156 Pages

Dimensions(cm)
15.3 x 23.0 x 1.0

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The Gradient Test: Another Likelihood-Based Test presents the latest on the gradient test, a large-sample test that was introduced in statistics literature by George R. Terrell in 2002. The test has been studied by several authors, is simply computed, and can be an interesting alternative to the classical large-sample tests, namely, the likelihood ratio (LR), Wald (W), and Rao score (S) tests.

Due to the large literature about the LR, W and S tests, the gradient test is not frequently used to test hypothesis. The book covers topics on the local power of the gradient test, the Bartlett-corrected gradient statistic, the gradient statistic under model misspecification, and the robust gradient-type bounded-influence test.

Covers the background of the gradient statistic and the different models
Discusses The Bartlett-corrected gradient statistic
Explains the algorithm to compute the gradient-type statistic

Industry Reviews

"...carrying out the asymptotic chi-square test as a gradient test based on ST is computationally less involved, because it avoids the computation or the estimation, respectively, of the Fisher information matrix of the model." --Zentralblatt MATH

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