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ePub Transformation and Weighting in Regression (Chapman Hall/CRC Monographs on Statistics and Applied Probability) download

by David Ruppert,Raymond J. Carroll

ePub Transformation and Weighting in Regression (Chapman  Hall/CRC Monographs on Statistics and Applied Probability) download
Author:
David Ruppert,Raymond J. Carroll
ISBN13:
978-0412014215
ISBN:
0412014211
Language:
Publisher:
Chapman and Hall/CRC; 1 edition (August 1, 1988)
Category:
Subcategory:
Mathematics
ePub file:
1391 kb
Fb2 file:
1951 kb
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4.7
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584

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Raymond James Carroll is an American statistician, and Distinguished Professor of Statistics, Nutrition and Toxicology at Texas A&M . Chapman & Hall/CRC Monographs on Statistics & Applied Probability (2 e.

Raymond James Carroll is an American statistician, and Distinguished Professor of Statistics, Nutrition and Toxicology at Texas A&M University. He is a recipient of 1988 COPSS Presidents' Award and 2002 R. A. Fisher Lectureship. He has made fundamental contributions to measurement error model, nonparametric and semiparametric modeling. Liang, Faming; Liu, Chuanhai; Carroll, Raymond (August 2010).

Start by marking Transformation and Weighting in Regression as Want to Read .

Start by marking Transformation and Weighting in Regression as Want to Read: Want to Read savin. ant to Read. 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.

Transformation and Weighting in Regression. by Raymond J. Carroll and David Ruppert. The authors have developed techniques to deal with these types of problems, the complications of which can be observed in diverse fields.

Nonparametric Regression and Generalized Linear Models: A roughness penalty approach (Chapman & Hall CRC Monographs on Statistics & Applied Probability). Nonparametric Regression and Generalized Linear Models: A roughness penalty approach (Chapman & Hall CRC Monographs on Statistics & Applied Probability).

Paperback – 2019-10-18 Chapman and Hall/CRC Chapman & Hall/CRC Monographs on Statistics and Applied .

Paperback – 2019-10-18 Chapman and Hall/CRC Chapman & Hall/CRC Monographs on Statistics and Applied Probability. Multivariate Dependencies.

Barlett (1960) 2 Queues . Smith (1961) 3 Monte Carlo Methods . Monographs on Statistics and Applied Probability 114. Introduction to Time Series Modeling. Further, in this book, a unified approach to model evaluation is introduced based on the entropy maximization principle advocated by Dr. Akaike.

Carroll, R. J. and Ruppert, D. (1988) Transformation and Weighting in Regression, Chapman & Hall, New York. Ruppert D. (2004) Regression. In: Statistics and Finance. Springer Texts in Statistics. Springer, New York, NY. Congdon, P. (2003) Applied Bayesian Modelling, Wiley, Chichester. Cook, R. D. and Weisberg, S. (1982) Residuals and Influence in Regression, Chapman & Hall, New York. zbMATHGoogle Scholar.

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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.