Fitting of nonlinear regressions by orthogonalized power series
✍ Scribed by Milan Randić
- Publisher
- John Wiley and Sons
- Year
- 1993
- Tongue
- English
- Weight
- 806 KB
- Volume
- 14
- Category
- Article
- ISSN
- 0192-8651
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
We outline a procedure that resolves ambiguities in fitting nonlinear data by power series. As is well known, the coefficients in the regression equations depend upon the truncation of the power series. We outline the procedure in which the coefficients of the regression using a power expansion are independent of the degree of the polynomials used. This is achieved by considering mutual regression of descriptors and using residuals as novel variables. The derived regression equations show unusual numerical stability, i.e., the coefficients of the regression equations are constant and independent of the truncation of the power series. The process is illustrated in an example to show all details and facilitate duplicating the process for interested readers. The method described here complements recently outlined procedures for construction of orthogonal descriptors for use in multivariate regression analysis. © 1993 John Wiley & Sons, Inc.
📜 SIMILAR VOLUMES
This paper is devoted to goodness-of-ÿt tests for parametric possibly nonlinear heteroscedastic regression models. The test statistic is constructed using a marked empirical process based on residuals. We investigate the consistency of this test statistic and of the estimators needed to compute it.
This paper proposes a new nonparametric test for the hypothesis that the regression functions in two or more populations are the same. The test is based on local linear estimates using data-driven bandwidth selectors. The test is applicable to data with random regressors and heteroskedastic response