A comparative study for the estimation of parameters in nonlinear models

Kuldeep Kumar; Mohammed A Alsaleh

doi:10.1016/S0096-3003(95)00211-1

A comparative study for the estimation of parameters in nonlinear models

Kuldeep Kumar^*
, Mohammed A Alsaleh

^*Corresponding author for this work

Centre for Data Analytics

Research output: Contribution to journal › Article › Research › peer-review

4 Citations (Scopus)

Abstract

The most commonly used numerical optimization techniques include the methods of Gauss-Newton, Newton-Raphson, gradient methods, including methods of steepest ascent and descent, and Marquardt algorithm. Kumar [1] has recently proposed a new technique based on optimum exponential regression. Another noniterative procedure proposed in this paper is based on the principle of internal regression. In this paper, we have compared these methods using real data sets.

Original language	English
Pages (from-to)	179-184
Number of pages	6
Journal	Applied Mathematics and Computation
Volume	77
Issue number	2-3
DOIs	https://doi.org/10.1016/S0096-3003(95)00211-1
Publication status	Published - 1 Jan 1996

Access to Document

10.1016/S0096-3003(95)00211-1

Cite this

@article{3a7ccf967bcf43259a7a869d75ebc7e9,

title = "A comparative study for the estimation of parameters in nonlinear models",

abstract = "The most commonly used numerical optimization techniques include the methods of Gauss-Newton, Newton-Raphson, gradient methods, including methods of steepest ascent and descent, and Marquardt algorithm. Kumar [1] has recently proposed a new technique based on optimum exponential regression. Another noniterative procedure proposed in this paper is based on the principle of internal regression. In this paper, we have compared these methods using real data sets.",

author = "Kuldeep Kumar and Alsaleh, \{Mohammed A\}",

year = "1996",

month = jan,

day = "1",

doi = "10.1016/S0096-3003(95)00211-1",

language = "English",

volume = "77",

pages = "179--184",

journal = "Applied Mathematics and Computation",

issn = "0096-3003",

publisher = "Elsevier",

number = "2-3",

}

TY - JOUR

T1 - A comparative study for the estimation of parameters in nonlinear models

AU - Kumar, Kuldeep

AU - Alsaleh, Mohammed A

PY - 1996/1/1

Y1 - 1996/1/1

N2 - The most commonly used numerical optimization techniques include the methods of Gauss-Newton, Newton-Raphson, gradient methods, including methods of steepest ascent and descent, and Marquardt algorithm. Kumar [1] has recently proposed a new technique based on optimum exponential regression. Another noniterative procedure proposed in this paper is based on the principle of internal regression. In this paper, we have compared these methods using real data sets.

AB - The most commonly used numerical optimization techniques include the methods of Gauss-Newton, Newton-Raphson, gradient methods, including methods of steepest ascent and descent, and Marquardt algorithm. Kumar [1] has recently proposed a new technique based on optimum exponential regression. Another noniterative procedure proposed in this paper is based on the principle of internal regression. In this paper, we have compared these methods using real data sets.

UR - https://www.scopus.com/pages/publications/16344391849

U2 - 10.1016/S0096-3003(95)00211-1

DO - 10.1016/S0096-3003(95)00211-1

M3 - Article

AN - SCOPUS:16344391849

SN - 0096-3003

VL - 77

SP - 179

EP - 184

JO - Applied Mathematics and Computation

JF - Applied Mathematics and Computation

IS - 2-3

ER -

A comparative study for the estimation of parameters in nonlinear models

Abstract

Access to Document

Other files and links

Fingerprint

Cite this