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.
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.
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
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
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