The theory of estimating functions plays a major role in analysis of data pertaining to Biostatistics, Econometrics, Time Series Analysis, Reliability studies and other varied fields. This book discusses at length the application of the theory in interpretation of results in Survey Sampling.

The book's comprehensive coverage on the application of econometric methods to empirical analysis of economic issues is impressive. It uncovers the missing link between textbooks on economic theory and econometrics and highlights the powerful connection between economic theory and empirical analysis perfectly through examples on rigorous experimental design. The use of data sets for estimation derived with the Monte Carlo method helps facilitate the understanding of the role of hypothesis testing applied to economic models. Topics covered in the book are: consumer behavior, producer behavior, market equilibrium, macroeconomic models, qualitative-response models, panel data analysis and time-series analysis. Key econometric models are introduced, specified, estimated and evaluated. The treatment on methods of estimation in econometrics and the discipline of hypothesis testing makes it a must-have for graduate students of economics and econometrics and aids their understanding on how to estimate economic models and evaluate the results in terms of policy implications.

Non linearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis. More entrenched forms of nonlinearity often require intensive numerical methods to construct estimators, and the use of root search algorithms, or one-step estimators, is a standard method of solution. This book provides a comprehensive study of nonlinear estimating equations and artificial likelihood's for statistical inference. It provides extensive coverage and comparison of hill climbing algorithms, which when started at points of nonconcavity often have very poor convergence properties, and for additional flexibility proposes a number of modification to the standard methods for solving these algorithms. The book also extends beyond simple root search algorithms to include a discussion of the testing of roots for consistency, and the modification of available estimating functions to provide greater stability in inference. A variety of examples from practical applications are included to illustrate the problems and possibilities thus making this text ideal for the research statistician and graduate student.

Essential background reading for engineers and scientists working in such fields as communications, control, signal, and image processing, radar and sonar, radio astronomy, seismology, remote sensing, and instrumentation. The book can be used as a textbook for a single course, as well as a combination of an introductory and an advanced course, or even for two separate courses, one in signal detection, the other in estimation.

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.

"This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--

"A clearly written book that is a useful primer for a very complicated set of topics." --Capers Jones, Chief Scientist Emeritus, Software Productivity Research LLC Practical Software Estimation brings together today's most valuable tips, techniques, and best practices for accurately estimating software project efforts, costs, and schedules. Written by a leading expert in the field, it addresses the full spectrum of real-world challenges faced by those who must develop reliable estimates. M. A. Parthasarathy draws on the immense experience of Infosys, one of the world's largest and most respected providers of IT-enabled business solutions, to bring you the only book with detailed guidance on estimating insourced and outsourced software projects, as well as projects that blend both approaches. He demonstrates how to successfully utilize Function Point (FP) methods, the industry's leading estimation model. Then, using real case studies, he systematically identifies pitfalls that can lead to inaccurate estimates--and offers proven solutions. Coverage includes How to estimate all types of software projects, including "fresh" development, reengineering, and maintenance How to incorporate the impact of core project elements on estimates: scope, environment, experience, and tools FP analysis from start to finish: data and transaction functions, general system characteristics, and more FP methods for any platform or business function Innovative re-estimation methods to track progress How to quote RFPs and prepare contracts: fixed price, time/material, and project execution lifecycle models Alternatives to FP: Delphi, COCOMO II, and COSMIC-FFP How to choose the right estimation tools Practical Software Estimation is the definitive reference for anyone who must estimate software projects accurately: project and IT managers, individual developers, system designers, architects, executives, consultants, and outsourcers alike. List of Figures List of Tables Foreword Preface Acknowledgments Chapter 1: Introduction Chapter 2: Role of Estimation in Software Projects Chapter 3: A Study of Function Point Analysis Chapter 4: Data Functions Chapter 5: Transactional Functions Chapter 6: General System Characteristics Chapter 7: Size, Effort, and Scheduling of Projects Chapter 8: Estimation Flavors Chapter 9: A Sense of Where You Are Chapter 10: Tips, Tricks, and Traps Chapter 11: Insourcing versus Outsourcing Chapter 12: Key Factors in Software Contracts Chapter 13: Project Estimation and Costing Chapter 14: Other Estimation Methods Chapter 15: Estimation Tools Chapter 16: Estimation Case Study Appendix A: Reference Tables: Transaction Function Counts Appendix B: Reference Tables: Data Function Points Bibliography Index

Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions.

Das englischsprachige Buch vermittelt Kenntnisse der wichtigsten Methoden der modernen design-basierten Stichprobentheorie. Alle Methoden werden mit Hilfe von numerischen Beispielen illustriert und deren Umsetzung mit Hilfe der statistischen Software R dargestellt. Zahlreiche empirische Beispiele und Simulationen helfen, die Eigenschaften von Schätzfunktionen zu beurteilen. This textbook provides an up-to-date treatment of modern design based theory of survey sampling. All methods are illustrated with numerical examples and applied using the statistical software R. Numerous empirical examples and simulations provide insights into the properties of estimation functions.