## LINEAR REGRESSION 2 Table 5.1 Mineral data: copper (Cu) and zinc (Zn) contents Obs. Cu Zn Obs. Cu Zn
Robust Statistics || Linear Regression 1
β Scribed by Maronna, Ricardo A.; Martin, Douglas R.; Yohai, Victor J.
- Publisher
- Wiley
- Year
- 2006
- Tongue
- English
- Weight
- 377 KB
- Edition
- 1
- Category
- Article
- ISBN
- 0470010924
No coin nor oath required. For personal study only.
β¦ Synopsis
Robust Statistics Sets Out To Explain The Use Of Robust Methods And Their Theoretical Justification. It Provides An Up-to-date Overview Of The Theory And Practical Application Of Robust Statistical Methods In Regression, Multivariate Analysis, Generalized Linear Models And Time Series. Robust Statistics Aims To Stimulate The Use Of Robust Methods As A Powerful Tool To Increase The Reliability And Accuracy Of Statistical Modelling And Data Analysis. It Is Ideal For Researchers, Practitioners And Graduate Students Of Statistics, Electrical, Chemical And Biochemical Engineering, And Computer Vision. There Is Also Much To Benefit Researchers From Other Sciences, Such As Biotechnology, Who Need To Use Robust Statistical Methods In Their Work.--jacket. Location And Scale -- Measuring Robustness -- Linear Regression 1 -- Linear Regression 2 -- Multivariate Analysis -- Generalized Linear Models -- Time Series -- Numerical Algorithms -- Asymptotic Theory Of M-estimates -- Robust Methods In S-plus -- Description Of Data Sets. Ricardo A. Maronna, R. Douglas Martin, VΓctor J. Yohai. Includes Bibliographical References (p. [383]-396) And Index.
π SIMILAR VOLUMES
In a number of problems, interest is centered on only a few of the coefficients of the multiple linear regression model, while the remaining parameters are treated as nuisance parameters. At the same time, the experimenter is interested in estimating the parameters robustly. We propose a new weighti
Scientific Data Gathering -- Displaying And Summarizing Data -- Logic, Probability, And Uncertainty -- Discrete Random Variables -- Bayesian Inference For Discrete Random Variables -- Continuous Random Variables -- Bayesian Inference For Binomial Proportion -- Comparing Bayesian And Frequentist Infe