𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Goodness-of-fit test for linear models based on local polynomials

✍ Scribed by J.T. Alcalá; J.A. Cristóbal; W. González-Manteiga

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
92 KB
Volume
42
Category
Article
ISSN
0167-7152

No coin nor oath required. For personal study only.

✦ Synopsis

We test if a regression function belongs to a class of parametric models by measuring the discrepancy between a parametric ÿt and a local polynomial regression. The proposed test is a weighted L 2 -norm of a smoothed function based on the parametric residuals.

📜 SIMILAR VOLUMES

On goodness-of-fit for the linear transf
On goodness-of-fit for the linear transformation and frailty models
✍ Vilijandas Bagdonavičius; Mikhail Nikulin 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 122 KB

Goodness-of-ÿt tests for accelerated life models, generalizing or alternative to the additive accumulation of damages and the proportional hazards models are proposed. Important classes of such models are the linear transformation and frailty models.

A simulation-based goodness-of-fit test
A simulation-based goodness-of-fit test for survival data
✍ Gang Li; Yanqing Sun 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 149 KB

To check the validity of a parametric model for survival data, a number of supremum-type tests have been proposed in the literature using Khmaladze's (1993, Ann. Statist. 18, 582-602) transformation of a test process. However, such a transformation is usually very complicated and lacks a clear inter

Likelihood-Based Local Polynomial Fittin
Likelihood-Based Local Polynomial Fitting for Single-Index Models
✍ J. Huh; B.U. Park 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 226 KB

The parametric generalized linear model assumes that the conditional distribution of a response Y given a d-dimensional covariate X belongs to an exponential family and that a known transformation of the regression function is linear in X. In this paper we relax the latter assumption by considering