Estimating Functions

Edited by V. P. Godambe

A Clarendon Press Publication

Oxford Statistical Science Series

Estimating Functions

Edited by V. P. Godambe

A Clarendon Press Publication

Oxford Statistical Science Series

Description

This volume comprises a comprehensive collection of original papers on the subject of estimating functions. It is intended to provide statisticians with an overview of both the theory and the applications of estimating functions in biostatistics, stochastic processes, and survey sampling. From the early 1960s when the concept of optimality criterion was first formulated, together with the later work on optimal estimating functions, this subject has become both an active research area in its own right and also a cornerstone of the modern theory of statistics. Individual chapters have been written by experts in their respective fields and as a result this volume will be an invaluable reference guide to this topic as well as providing an introduction to the area for non-experts.

Estimating Functions

Edited by V. P. Godambe

A Clarendon Press Publication

Oxford Statistical Science Series

PART I: Overview
1. Estimating Functions: An Overview, V.P. Godambe and B.K. Kale
PART II
2. Applications of Estimating Function Theory to Replicates of Generalized Proportional Hazards Models, I.-Shou Chang and Chao A. Hsiung
3. Estimating Equations for Mixed Poisson Models, C.B. Dean
4. Estimating Equations in Generalized Linear Models with Measurement Error, Kung-Yee Liang and Xin-Hua Liu
5. A Unification of Inference from Capture-Recapture Studies Through Martingale Estimating Functions, C.J. Lloyd and P. Yip
6. The Role of Unbiasedness in Estimating Equations, T. Yanagimoto and E. Yamamoto
7. Use of a Quadratic Exponential Model to Generate Estimating Equations for Means, Variances, and Covariances, L.P. Zhao and R.L. Prentice
PART III: Stochastic Processes
8. Generalized Score Tests for Composite Hypotheses, I.V. Basawa
9. Quasi-likelihood Stochastic Processes and Optimal Estimating Functions, A.F. DesmondR
10. On Optimal Estimating Functions for Partially Specified Counting Process Models, P.E. Greenwood and W. Wefelmeyer
11. Approximate Confidence Zones in an Estimating Function Context, C.C. Heyde and Y.-X. Lin
12. Simplified and Two-stage Quasi-Likelihood Estimators,
J.E. Hutton, O.T. Ogunymei and P.I. Nelson
13. Tests Based on an Optimal Estimate, A. Thavaneswaran
PART IV: Survey Sampling
14. Estimating Functions in Survey Sampling: A Review,
M. Ghosh
15. Confidence Intervals for Quantiles, V.P. Godambe
16. Making Use of a Regression Model for Inference About a Finite Population Mean, H. Mantel
17. Estimating Functions in Survey Sampling: Estimation of Superpopulation Regression Parameters, K. Vijayan
PART V: Theory (Foundations)
18. Sufficiency, Ancillarity, and Information in Estimating Functions, V.P. Bhapkar
19. Inferential Estimation, Likelihood, and Maximum Likelihood Linear Estimating Functions, S.R. Chamberlin and D.A. Sprott
20. Geometrical Aspects of Efficiency Criteria for Spaces of Estimating Functions, C.G. Small and D.L. McLeish
PART VI: Theory (General Methods)
21. Estimating Equations from Modified Profile Likelihood, H. Ferguson, N. Reid and D.R. Cox
22. Resampling Using Estimating Equations, S. Lele
23. On Using Bivariate Moment Equations in Mixed Normal Problems, B.G. Lindsay and P. Basak
24. Estimating Functions in Semi-Parametric Models, Y. Ritov

Estimating Functions

Edited by V. P. Godambe

A Clarendon Press Publication

Oxford Statistical Science Series

Author Information

Contributors:

P. Basak, Department of Mathematics, Penn State University, Ivy Side, Altoona, Pennsylvania 16603, USA; I.V. Basawa, Department of Statistics, University of Georgia, Athens, Georgia 30602, USA; Vasant P. Bhapkar, Department of Statistics, University of Kentucky, Lexington, Kentucky 40506, USA; S.R. Chamberlin, Department of Theoretical Statistics, Mathematical Institute, Aarhus University, DK-8000, Aarhus C, Denmark; I-Shou Chang, Department of Mathematics, National Central University, Chung-Li, Taiwan, Republic of China; D.R. Cox, Nuffield College, Oxford OX1 1NF; C.B. Dean, Department of Mathematics and Statistics, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6; A.F. Desmond, Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada N1G 2W1; H. Ferguson, Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1; Chris J. Lloyd, Department of Statistics, La Trobe University, Bandoora, Victoria, Australia 3083; D.L. McLeish, Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1; H. Mantel, Department of Mathematics and Statistics, Carleton University, Ottawa, Canada K1S 5B6, P.I. Nelson, Department of Statistics, Kansas State University, Manhattan, Kansas 66506, USA; R.L. Prentice, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1124 Columbia Street, Seattle, Washington 98104, USA; N. Reid, Department of Statistics, University of Toronto, 100 St George Street, Toronto, Ontario, Canada M5S 1A1; Y. Ritov, Department of Statistics, The Hebrew University of Jerusalem, Jerusalem 91905, Israel; Christopher G. Small, Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1, D.A. Sprott, Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1, A. Thavaneswaran, Department of Statistics, The University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2; k. Vijayan, Department of Mathematics, The University of Western Australia, Nedlands, Western Australia,6009

Estimating Functions

Edited by V. P. Godambe

A Clarendon Press Publication

Oxford Statistical Science Series

Reviews and Awards

"[Godambe] has compiled 24 excellent articles. . . . well written with rich ideas on estimating functions and their applications. . . . also has extensive author and subject indices. I liked every article. I trongly recommend Estimating Functions to the statistical community." --Journal of the American Statistical Association