A newton-based approach for attributing tumor lethality in animal carcinogenicity studies

Hongshik Ahn; Hojin Moon; Sunyoung Kim; Ralph L. Kodell

doi:10.1016/S0167-9473(01)00041-X

A newton-based approach for attributing tumor lethality in animal carcinogenicity studies

Hongshik Ahn, Hojin Moon, Sunyoung Kim, Ralph L. Kodell

Department of Mathematics

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

7 Scopus citations

Abstract

A new Newton-based approach is proposed for finding the global maximum of a nonlinear function subject to various inequality constraints. This method can be applied to nonparametric maximum likelihood estimation problems to attribute tumor lethality in long-term carcinogenicity studies. This method is substantially faster and easier to implement than the Complex Method used in Ahn et al. (2000). This approach is very useful especially when there exist a large number of parameters of interest to be estimated and many nonlinear inequality constraints. A Monte Carlo simulation study is conducted to evaluate the computational efficiency and accuracy of the estimates obtained from the new approach. The advantages of using the Newton-based approach are illustrated with a real data set.

Original language	English
Pages (from-to)	263-283
Number of pages	21
Journal	Computational Statistics and Data Analysis
Volume	38
Issue number	3
DOIs	https://doi.org/10.1016/S0167-9473(01)00041-X
State	Published - 28 Jan 2002

Bibliographical note

Funding Information:
Hongshik Ahn's work was partially supported by NIH Grant 1 R29 CA77289-03 and the Faculty Research Participation Program at the National Center for Toxicological Research administered by the Oak Ridge Institute for Science and Education through an interagency agreement between USDOE and USFDA. Sunyoung Kim's work was supported by Brain Korea 21 and Korea Research Fund Grant KRF-2000-015-DP0023.

Keywords

Cause of death
Inequality constraint
Maximum likelihood
Optimization
Sacrifice

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10.1016/S0167-9473(01)00041-X

Cite this

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title = "A newton-based approach for attributing tumor lethality in animal carcinogenicity studies",

abstract = "A new Newton-based approach is proposed for finding the global maximum of a nonlinear function subject to various inequality constraints. This method can be applied to nonparametric maximum likelihood estimation problems to attribute tumor lethality in long-term carcinogenicity studies. This method is substantially faster and easier to implement than the Complex Method used in Ahn et al. (2000). This approach is very useful especially when there exist a large number of parameters of interest to be estimated and many nonlinear inequality constraints. A Monte Carlo simulation study is conducted to evaluate the computational efficiency and accuracy of the estimates obtained from the new approach. The advantages of using the Newton-based approach are illustrated with a real data set.",

keywords = "Cause of death, Inequality constraint, Maximum likelihood, Optimization, Sacrifice",

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AU - Kim, Sunyoung

AU - Kodell, Ralph L.

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N2 - A new Newton-based approach is proposed for finding the global maximum of a nonlinear function subject to various inequality constraints. This method can be applied to nonparametric maximum likelihood estimation problems to attribute tumor lethality in long-term carcinogenicity studies. This method is substantially faster and easier to implement than the Complex Method used in Ahn et al. (2000). This approach is very useful especially when there exist a large number of parameters of interest to be estimated and many nonlinear inequality constraints. A Monte Carlo simulation study is conducted to evaluate the computational efficiency and accuracy of the estimates obtained from the new approach. The advantages of using the Newton-based approach are illustrated with a real data set.

AB - A new Newton-based approach is proposed for finding the global maximum of a nonlinear function subject to various inequality constraints. This method can be applied to nonparametric maximum likelihood estimation problems to attribute tumor lethality in long-term carcinogenicity studies. This method is substantially faster and easier to implement than the Complex Method used in Ahn et al. (2000). This approach is very useful especially when there exist a large number of parameters of interest to be estimated and many nonlinear inequality constraints. A Monte Carlo simulation study is conducted to evaluate the computational efficiency and accuracy of the estimates obtained from the new approach. The advantages of using the Newton-based approach are illustrated with a real data set.

KW - Cause of death

KW - Inequality constraint

KW - Maximum likelihood

KW - Optimization

KW - Sacrifice

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JO - Computational Statistics and Data Analysis

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ER -

A newton-based approach for attributing tumor lethality in animal carcinogenicity studies

Abstract

Bibliographical note

Keywords

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Cite this