This monograph provides a careful review of the major statistical
techniques used to analyze regression data with nonconstant
variability and skewness. The authors have developed statistical
techniques--such as formal fitting methods and less formal
graphical techniques-- that can be applied to many problems across
a range of disciplines, including pharmacokinetics, econometrics,
biochemical assays, and fisheries research. While the main focus of
the book in on data transformation and weighting, it also draws
upon ideas from diverse fields such as influence diagnostics,
robustness, bootstrapping, nonparametric data smoothing,
quasi-likelihood methods, errors-in-variables, and random
coefficients. The authors discuss the computation of estimates and
give numerous examples using real data. The book also includes an
extensive treatment of estimating variance functions in regression.
General
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