๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Dynamic plots for displaying the roles of variables and observations in regression model

โœ Scribed by S.S. Kim; S.H. Park

Publisher
Elsevier Science
Year
1995
Tongue
English
Weight
1012 KB
Volume
19
Category
Article
ISSN
0167-9473

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

A dynamic plot for the specification of
A dynamic plot for the specification of curvature in linear regression
โœ Han Son Seo ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 101 KB

We assume a multiple regression model in which the relation between a response variable and one of independent variables is nonlinear. CERES plot, an acronym for "Combining Conditional Expectations and RESiduals" is proposed by Cook (1993, Technometrics, 35, 351-362) for uncovering the function of n

Some aspects of measurement error in exp
Some aspects of measurement error in explanatory variables for continuous and binary regression models
โœ G. K. Reeves; D. R. Cox; S. C. Darby; E. Whitley ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 189 KB ๐Ÿ‘ 2 views

A simple form of measurement error model for explanatory variables is studied incorporating classical and Berkson cases as particular forms, and allowing for either additive or multiplicative errors. The work is motivated by epidemiological problems, and therefore consideration is given not only to

Bootstrap-based Qฬ‚kh2 for the selection
Bootstrap-based Qฬ‚kh2 for the selection of components and variables in PLS regression
โœ Silvano Amato; Vincenzo Esposito Vinzi ๐Ÿ“‚ Article ๐Ÿ“… 2003 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 249 KB

The aim of this paper is to suggest a bootstrap-based method for choosing the number of components in Partial Least Squares Regression (PLSR). Cross-validated Q h 2 statistic is used, for which is intended to derive a bootstrap distribution and to perform a hypothesis testing. Monte Carlo approximat

Proxy and Instrumental Variable Methods
Proxy and Instrumental Variable Methods in a Regression Model with One of the Regressors Missing
โœ R.N. Bhattacharya; D.K. Bhattacharyya ๐Ÿ“‚ Article ๐Ÿ“… 1993 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 435 KB

Suppose \(Y\) has a linear regression on \(X_{1}, X_{2}\), but observations are only available on \(\left(Y, X_{1}\right)\). If large scale data on \(\left(X_{1}, X_{2}\right)\) are available, which do not include \(Y\), and if the regression of \(X_{2}\), given \(X_{1}\), is nonlinear, then one may