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The gradient test : another likelihood-based test / Artur Lemonte.

By: Contributor(s): Publisher: Amsterdam : Academic Press, 2016Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780128036136
  • 0128036133
Subject(s): Genre/Form: Additional physical formats: Print version:: No titleLOC classification:
  • QA277
Online resources:
Contents:
Front Cover; The Gradient Test: Another Likelihood-Based Test; Copyright; Dedication; Contents; List of Figures; List of Tables; Preface; Chapter 1: The Gradient Statistic; 1.1 Background; 1.2 The Gradient Test Statistic; 1.3 Some Properties of the Gradient Statistic; 1.4 Composite Null Hypothesis; 1.5 Birnbaum-Saunders Distribution Under Type II Censoring; 1.5.1 Inference Under Type II Censored Samples; 1.5.2 Numerical Results; 1.5.3 Empirical Applications; 1.6 Censored Exponential Regression Model; 1.6.1 The Regression Model; 1.6.2 Finite-Sample Size Properties.
3.6.4 Real Data IllustrationChapter 4: The Gradient Statistic Under Model Misspecification; 4.1 Introduction; 4.2 The Robust Gradient Statistic; 4.3 Examples; 4.4 Numerical Results; Chapter 5: The Robust Gradient-Type Bounded-Influence Test; 5.1 Introduction; 5.2 The Robust Gradient-Type Test; 5.3 Robustness Properties; 5.4 Algorithm to Compute the Gradient-Type Statistic; 5.5 Closing Remarks; Bibliography; Back Cover.
Summary: 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.
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Preface Chapter 1 The gradient statistic Chapter 2 The local power of the gradient test Chapter 3 The Bartlett-corrected gradient statistic Chapter 4 The gradient statistic under model misspecification Chapter 5 The robust gradient-type bounded-influence test References.

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880-01 Front Cover; The Gradient Test: Another Likelihood-Based Test; Copyright; Dedication; Contents; List of Figures; List of Tables; Preface; Chapter 1: The Gradient Statistic; 1.1 Background; 1.2 The Gradient Test Statistic; 1.3 Some Properties of the Gradient Statistic; 1.4 Composite Null Hypothesis; 1.5 Birnbaum-Saunders Distribution Under Type II Censoring; 1.5.1 Inference Under Type II Censored Samples; 1.5.2 Numerical Results; 1.5.3 Empirical Applications; 1.6 Censored Exponential Regression Model; 1.6.1 The Regression Model; 1.6.2 Finite-Sample Size Properties.

3.6.4 Real Data IllustrationChapter 4: The Gradient Statistic Under Model Misspecification; 4.1 Introduction; 4.2 The Robust Gradient Statistic; 4.3 Examples; 4.4 Numerical Results; Chapter 5: The Robust Gradient-Type Bounded-Influence Test; 5.1 Introduction; 5.2 The Robust Gradient-Type Test; 5.3 Robustness Properties; 5.4 Algorithm to Compute the Gradient-Type Statistic; 5.5 Closing Remarks; Bibliography; Back Cover.

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.

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