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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
PART 1: OVERVIEW: V.P. Godambe & B.K. Kale: Estimating functions: an overview; PART 2: BIOSTATISTICS: I.-Shou Chang & Chao A. Hsiung: Applications of estimating function theory to replicates of generalized proportional hazards models; C.B. Dean: Estimating equations for mixed Poisson models; Kung-Lee Liang & Xin-Hua Liu: Estimating equations in generalized linear models with measurement error; C.J. Lloyd & P. Yip: A unification of inference from capture-recapture studies through martingale estimating functions; T. Yanagimoto & E. Yamamoto: The role of unbiasedness in estimating equations; L.P. Zhao & R.L. Prentice: Use of a quadratic exponential model to generate estimating equations for means, variances, and covariances; PART 3: STOCHASTIC PROCESSES: I.V. Basawa: Generalized score tests for composite hypotheses; A.F. Desmond: Quasi-likelihood stochastic processes and optimal estimating functions; P.E. Greenwood & W. Wefelmeyer: On optimal estimating functions for partially specified counting process models; C.C. Heyde & Y.-X. Lin: Approximate confidence zones in an estimating function context; J.E. Hutton, O.T. Ogunyemi & P.I. Nelson: Simplified and two-stage quasi-likelihood estimators; A. Thavaneswaran: Tests based on an optimal estimate; PART 4: SURVEY SAMPLING: M. Ghosh: Estimating functions in survey sampling: a review; V.P. Godambe: Confidence intervals for quantiles; H. Mantel: Making use of a regression model for inferences about a finite population mean; K. Vijayan: Estimating functions in survey sampling: estimation of superpopulation regression parameters; PART 5: THEORY (Foundations): V.P. Bhapkar: Sufficiency, ancillarity, and information in estimating functions; S.R. Chamberlin & D.A. Sprott: Inferential estimation, likelihood, and maximum likelihood linear estimating functions; C.G. Small & D.L. McLeish: Geometrical aspects of efficiency criteria for spaces of estimating functions; PART 6: THEORY (General Methods): H. Ferguson, N. Reid & D.R. Cox: Estimating equations from modified profile likelihood; S. Lele: Resampling using estimating equations; B.G. Lindsay & P. Basak: On using bivariate moment equations in mixed normal problems; Y. Ritov: Estimating funtions in semi-parametric models.
PART 1: OVERVIEW: V.P. Godambe & B.K. Kale: Estimating functions: an overview; PART 2: BIOSTATISTICS: I.-Shou Chang & Chao A. Hsiung: Applications of estimating function theory to replicates of generalized proportional hazards models; C.B. Dean: Estimating equations for mixed Poisson models; Kung-Lee Liang & Xin-Hua Liu: Estimating equations in generalized linear models with measurement error; C.J. Lloyd & P. Yip: A unification of inference from capture-recapture studies through martingale estimating functions; T. Yanagimoto & E. Yamamoto: The role of unbiasedness in estimating equations; L.P. Zhao & R.L. Prentice: Use of a quadratic exponential model to generate estimating equations for means, variances, and covariances; PART 3: STOCHASTIC PROCESSES: I.V. Basawa: Generalized score tests for composite hypotheses; A.F. Desmond: Quasi-likelihood stochastic processes and optimal estimating functions; P.E. Greenwood & W. Wefelmeyer: On optimal estimating functions for partially specified counting process models; C.C. Heyde & Y.-X. Lin: Approximate confidence zones in an estimating function context; J.E. Hutton, O.T. Ogunyemi & P.I. Nelson: Simplified and two-stage quasi-likelihood estimators; A. Thavaneswaran: Tests based on an optimal estimate; PART 4: SURVEY SAMPLING: M. Ghosh: Estimating functions in survey sampling: a review; V.P. Godambe: Confidence intervals for quantiles; H. Mantel: Making use of a regression model for inferences about a finite population mean; K. Vijayan: Estimating functions in survey sampling: estimation of superpopulation regression parameters; PART 5: THEORY (Foundations): V.P. Bhapkar: Sufficiency, ancillarity, and information in estimating functions; S.R. Chamberlin & D.A. Sprott: Inferential estimation, likelihood, and maximum likelihood linear estimating functions; C.G. Small & D.L. McLeish: Geometrical aspects of efficiency criteria for spaces of estimating functions; PART 6: THEORY (General Methods): H. Ferguson, N. Reid & D.R. Cox: Estimating equations from modified profile likelihood; S. Lele: Resampling using estimating equations; B.G. Lindsay & P. Basak: On using bivariate moment equations in mixed normal problems; Y. Ritov: Estimating funtions in semi-parametric models.
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