𝔖 Bobbio Scriptorium
✦   LIBER   ✦

On Bootstrapping M-Estimated Residual Processes in Multiple Linear-Regression Models

✍ Scribed by H.L. Koul; S.N. Lahiri

Publisher
Elsevier Science
Year
1994
Tongue
English
Weight
345 KB
Volume
49
Category
Article
ISSN
0047-259X

No coin nor oath required. For personal study only.

✦ Synopsis

It is shown, under fairly general conditions, that Efron's bootstrap procedure captures the limit distribution of weighted empirical processes based on (M)-estimated residuals in multiple linear regression models. As an application, we construct bootstrap confidence bands for the error distribution function (F). The main result can also be used to design distribution-free goodness-of-fit tests for (F) without any recourse to the split-sample estimation of the regression parameters.
c. 1994 Academic Press, lnc.

πŸ“œ SIMILAR VOLUMES

On Edgeworth Expansion and Moving Block
On Edgeworth Expansion and Moving Block Bootstrap for StudentizedM-Estimators in Multiple Linear Regression Models
✍ Soumendra Nath Lahiri πŸ“‚ Article πŸ“… 1996 πŸ› Elsevier Science 🌐 English βš– 870 KB

where x i 's are known p\_1 vectors, ; is a p\_1 vector of parameters, and = 1 , = 2 , ... are stationary, strongly mixing random variables. Let ; n denote an M-estimator of ; corresponding to some score function . Under some conditions on , x i 's and = i 's, a two-term Edgeworth expansion for Stud

Estimation of a Conditional Mean in a Li
Estimation of a Conditional Mean in a Linear Regression Model after a Preliminary Test on Regression Coefficient
✍ Dinesh S. Bhoj; Mohammad Ahsanullah πŸ“‚ Article πŸ“… 1994 πŸ› John Wiley and Sons 🌐 English βš– 449 KB πŸ‘ 2 views

## Abstract Consider the two linear regression models of __Y__~__ij__~ on __X__~__ij__~, namely __Y__~__ij__~ = Ξ²~__io__~ + Ξ²~__ij__~, __X__~__ij__~ + __E__~__ij__~ = 1, 2,…, __n__~__i__~, __i__ = 1, 2, where __E__~__ij__~ are assumed to be normally distributed with zero mean and common unknown var

On Joint Estimation of Regression and Ov
On Joint Estimation of Regression and Overdispersion Parameters in Generalized Linear Models for Longitudinal Data
✍ Brajendra C. Sutradhar; R.Prabhakar Rao πŸ“‚ Article πŸ“… 1996 πŸ› Elsevier Science 🌐 English βš– 646 KB

Liang and Zeger introduced a class of estimating equations that gives consistent estimates of regression parameters and of their variances in the class of generalized linear models for longitudinal data. When the response variable in such models is subject to overdispersion, the oerdispersion parame

Limiting Behavior of M-Estimators of Reg
Limiting Behavior of M-Estimators of Regression-Coefficients in High Dimensional Linear Models II. Scale-Invariant Case
✍ Z.D. Bai; Y. Wu πŸ“‚ Article πŸ“… 1994 πŸ› Elsevier Science 🌐 English βš– 317 KB

Asymptotics of M-estimators of the regression coefficients in linear models (both scale-variant and scale-invariant) when the number of regression coefficients tends to infinity as the sample size increases are investigated. The main purpose of this study is to establish the asymptotic properties un