Parameter identifiability and redundancy: theoretical considerations.

ABSTRACT:

Background

Models for complex biological systems may involve a large number of parameters. It may well be that some of these parameters cannot be derived from observed data via regression techniques. Such parameters are said to be unidentifiable, the remaining parameters being identifiable. Closely related to this idea is that of redundancy, that a set of parameters can be expressed in terms of some smaller set. Before data is analysed it is critical to determine which model parameters are identifiable or redundant to avoid ill-defined and poorly convergent regression.

Methodology/principal findings

In this paper we outline general considerations on parameter identifiability, and introduce the notion of weak local identifiability and gradient weak local identifiability. Thes

SUBMITTER: Little MP 

PROVIDER: S-EPMC2811744 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature