Local Polynomial Modelling and Its Applications Monographs on Statistics and Applied Probability 66
Cyclic and Computer Generated Designs
Nonlinear Models for Repeated Measurement Data
Statistics for Long-Memory Processes
An Introduction to the Bootstrap
Inference and Asymptotics
Practical Risk Theory for Actuaries
Markov Models & Optimization
By Jianqing Fan, Irene Gijbels
March 01, 1996
Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The...
By J.A. John, E.R. Williams
September 01, 1995
Cyclic and Computer Generated Designs is a much-expanded and updated version of the well-received monograph, Cyclic Designs . The book is primarily concerned with the construction and analysis of designs with a number of different blocking structures, such as revolvable designs, row-column designs,...
By Marie Davidian, David .M. Giltinan
June 01, 1995
Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified ...
By J. S. Maritz
April 01, 1995
Distribution-free statistical methods enable users to make statistical inferences with minimum assumptions about the population in question. They are widely used, especially in the areas of medical and psychological research.This new edition is aimed at senior undergraduate and graduate level. It ...
By M.P. Wand, M.C. Jones
December 01, 1994
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets.The basic principle is that local averaging or smoothing is performed with respect to a kernel function. This book provides uninitiated readers with a feeling for the principles, applications, and ...
By Jan Beran
October 01, 1994
Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, ...
By Bradley Efron, R.J. Tibshirani
May 15, 1994
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as...
By O.E. Barndorff-Nielsen, D.R. Cox
March 01, 1994
This book provides a systematic account of some developments in asymptotic parametric inference from a likelihood-based perspective. It focuses on first-order asymptotic theory, and discusses the need for higher-order theory....
By C.D. Daykin, T. Pentikainen, Martti Pesonen
December 01, 1993
This classic textbook covers all aspects of risk theory in a practical way. It builds on from the late R.E. Beard's extremely popular book Risk Theory, but features more emphasis on simulation and modeling and on the use of risk theory as a practical tool. Practical Risk Theory is a textbook for ...
By M.H.A. Davis
August 01, 1993
This book presents a radically new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class of stochastic system ...
By Seymour Geisser
June 01, 1993
The author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction ...
By P.J. Green, Bernard. W. Silverman
May 01, 1993
In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method...