A Transformation/Weighting Model for Estimating Michaelis-Menten Parameters,

reportActive / Technical Report | Accession Number: ADA186476 | Open PDF

Abstract:

There has been considerable disagreement about how best to estimate the parameters in Michaelis-Menten models. This document points out that many fitting methods are based on different stochastic models, being weighted least squares estimates after appropriate transformation. The authors propose a flexible model which can be used to help determine the proper transformation and choice of weights. The method is illustrated by examples. Keywords Nonlinear regression Lineweaver Burke transformation.

Author(s):
Carroll, Raymond J. ; Cressie, Noel ; Ruppert, David
Author Organization(s):
NORTH CAROLINA UNIV AT CHAPEL HILL INST OF STATISTICS
Supplementary Note:
DOI: 10.21236/ADA186476
Pagination:
0029

Security Markings

DOCUMENT & CONTEXTUAL SUMMARY

Distribution:
Approved For Public Release

RECORD

Collection: TR
Identifying Numbers
Report Number(s):
MIMEO-SER-1712, AFOSR-TR-87-1414
Contract/Grant Number(s):
F49620-85-C-0144, $NSF-MCS81-00748
Monitor Series:
TR-87-1414
 Subject Terms
Joint Capability Areas:
JCA_5_Command and Control; JCA_3.2_Engagement; JCA_3_Force Application; JCA_3.2.1_Kinetic Means; JCA_5.3_Planning; JCA_1_Force Support; JCA_1.3_Human Capital Management
Modernization Areas:
Fully Networked C3
Communities of Interest:
Energy and Power Technologies
Descriptor(s):
*WEIGHTING FUNCTIONS, ESTIMATES, LEAST SQUARES METHOD, MATHEMATICAL MODELS, FITTING FUNCTIONS(MATHEMATICS), PARAMETERS, NONLINEAR ANALYSIS, REGRESSION ANALYSIS, STOCHASTIC PROCESSES, WEIGHT
Field(s)/Group(s):
Statistics and Probability
Keyword(s):
Lineweaver Burke models, Michaelis Menten parameters
Report Date:
1987 Feb 01