Russell Cheng

Non-Standard Parametric Statistical Inference

• Gebundenes Buch

Produktbeschreibung

This research monograph gives a unified view of non-standard estimation problems. It provides an overall mathematical framework, but also draws together and studies in detail a large number of practical problems, previously only treated separately, offering solution methods and numerical procedures for each.

Produktdetails

• Verlag: Oxford University Press, USA
• Seitenzahl: 430
• Erscheinungstermin: 22. August 2017
• Englisch
• Abmessung: 236mm x 157mm x 25mm
• Gewicht: 862g
• ISBN-13: 9780198505044
• ISBN-10: 0198505043
• Artikelnr.: 48023477

Autorenporträt

Russell Cheng is Emeritus Professor of Operational Research at the University of Southampton. He has an M.A. and a Diploma in Mathematical Statistics from Cambridge University, and obtained his Ph.D. from Bath University. He is a former Chairman of the U.K. Simulation Society, a former Fellow of the Royal Statistical Society, and Fellow of the Institute of Mathematics and Its Applications. His research interests include: design and analysis of simulation experiments and parametric estimation methods. He founded and was Joint Editor of the IMA Journal of Management Mathematics.

Inhaltsangabe

1: Introduction
2: Non-Standard Problems: Some Examples
3: Standard Asymptotic Theory
4: Bootstrap Analysis
5: Embedded Model Problem
6: Examples of Embedded Distributions
7: Embedded Distributions: Two Numerical Examples
8: Infinite Likelihood
9: The Pearson and Johnson Systems
10: Box-Cox Transformations
11: Change-Point Models
12: The Skew Normal Distribution
13: Randomized-Parameter Models
14: Indeterminacy
15: Nested Nonlinear Regression Models
16: Bootstrapping Linear Models
17: Finite Mixture Models
18: Finite Mixture Examples: MAPIS Details