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.

This book provides a fast dissemination of the gradient test in statistics literature from around the world, presenting the latest information on this interesting alternative to the classical large-sample tests, namely the likelihood ratio (LR), Wald (W), and Rao score (S) tests

About the Author
Artur J. Lemonte is a professor at Federal University of Pernambuco, Department of Statistics, Recife/PE, Brazil. He works on higher order asymptotics, mathematical statistics, regression models, parametric inference, and distribution theory. In the last years, he has published more than 60 papers in refereedstatistical journals (most of them about the gradient test).

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



Book Information
ISBN 9780128035962
Author Artur Lemonte
Format Paperback
Page Count 156
Imprint Academic Press Inc
Publisher Elsevier Science Publishing Co Inc
Weight(grams) 190g