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Transformation and Weighting in Regression

Raymond J. Carroll | David Ruppert
€235.60
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A01=David Ruppert
A01=Raymond J. Carroll
absolute
Absolute Residuals
advanced regression variance modeling
Author_David Ruppert
Author_Raymond J. Carroll
Beverton Holt Model
Calibration Confidence Intervals
Category=PBT
Category=PBW
confidence
Data Sets
David Ruppert
Empirical Influence Function
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
errors-in-variables modeling
estimates
Estimating Variance Functions
Extended Quasi-likelihood
heteroscedasticity analysis
influence diagnostics
interval
Li Diagnostics
likelihood
Likelihood Ratio Confidence Interval
maximum
Maximum Likelihood
Maximum Likelihood Estimators
MLE
model
Normal Theory Maximum Likelihood
Normal Theory Maximum Likelihood Estimate
Power Weighting
prediction
Prediction Intervals
Pseudo-likelihood Estimate
quasi-likelihood approach
Raymond J. Carroll
Regression Parameter
residuals
ricker
Ricker Model
Robust Estimator
robust regression methods
Sockeye Salmon
Untransformed Response
Variance Function
variance function estimation
Vice Versa
Violated

Product details

  • ISBN 9780412014215
  • Weight: 476g
  • Dimensions: 156 x 234mm
  • Publication Date: 01 Aug 1988
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description
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.
About David Ruppert | Raymond J. Carroll
Carroll, Raymond J.; Ruppert, David

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