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Parameter Identifiability and Redundancy: Theoretical Considerations

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Parameter Identifiability and Redundancy: Theoretical Considerations Parameter Identifiability and Redundancy: Theoretical Considerations Mark P. Little1*, Wolfgang F. Heidenreich2, Guangquan Li1 1 Department of Epidemiology and Public Health, Imperial College Faculty of Medicine, St Mary’s Campus, London, United Kingdom, 2 Institut fu¨r Strahlenschutz, Helmholtz Zentrum Mu¨nchen, German Research Center for Environmental Health, Ingolsta¨dter Landstrasse, Neuherberg, Germany 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. These are based on local properties of the likelihood, in particular the rank of the Hessian matrix. We relate these to the notions of parameter identifiability and redundancy previously introduced by Rothenberg (Econometrica 39 (1971) 577–591) and Catchpole and Morgan (Biometrika 84 (1997) 187–196). Within the widely used exponential family, parameter irredundancy, local identifiability, gradient weak local identifiability and weak local identifiability are shown to be largely equivalent. We consider applications to a recently developed class of cancer models of Little and Wright (Math Biosciences 183 (2003) 111–134) and Little et al. (J Theoret Biol 254 (2008) 229–238) that generalize a large number of other recently used quasi-biological cancer models. Conclusions/Significance: We have shown that the previously developed concepts of parameter local identifiability and redundancy are closely related to the apparently weaker properties of weak local identifiability and gradient weak local identifiability—within the widely used exponential family these concepts largely coincide. Citation: Little MP, Heidenreich WF, Li G (2010) Parameter Identifiability and Redundancy: Theoretical Considerations. PLoS ONE 5(1): e8915. doi:10.1371/ journal.pone.0008915 Editor: Fabio Rapallo, University of East Piedmont, Italy Received November 23, 2009; Accepted January 8, 2010; Published January 27, 2010 Copyright: ß 2010 Little et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was funded partially by the European Commission under contracts FI6R-CT-2003-508842 (RISC-RAD) and FP6-036465 (NOTE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] Introduction Bellu [6], which we shall not consider further.] Catchpole et al. [7] and Gimenez et al. [8] outlined use of computer algebra techniques Models for complex biological systems may involve a large to determine numbers of identifiable parameters in the exponential number of parameters. It may well be that some of these parameters family. Viallefont et al. [9] considered parameter identifiability issues cannot be derived from observed data via regression techniques. in a general setting, and outlined a method based on considering the Such parameters are said to be unidentifiable or non-identifiable, rank of the Hessian for determining identifiable parameters; the remaining parameters being identifiable. Closely related to this however, some of their claimed results are incorrect (as we outline idea is that of redundancy, that a set of parameters can be expressed briefly later). Gimenez et al. [8] used Hessian-based techniques, as in terms of some smaller set. Before data is analysed it is critical to well as a number of purely numerical techniques, for determining determine which model parameters are identifiable or redundant to the number of identifiable parameters. Further general observations avoid ill-defined and poorly convergent regression. on parameter identifiability and its relation to properties of sufficient Identifiability in stochastic models has been considered previously statistics are given by Picci [10], and a more recent review of the in various contexts. Rothenberg [1] and Silvey [2] (pp. 50, 81) literature is given by Paulino and de Braganc¸a Pereira [11]. defined a set of parameters for a model to be identifiable if no two In this paper we outline some general considerations on sets of parameter values yield the same distribution of the data. parameter identifiability. We shall demonstrate that the concepts Catchpole and Morgan [3] considered identifiability and parameter of parameter local identifiability and redundancy are closely redundancy and the relations between them in a general class of related to apparently weaker properties of weak local identifiability (exponential family) models. Rothenberg [1], Jacquez and Perry [4] and gradient weak local identifiability that we introduce in the and Catchpole and Morgan [3] also defined a notion of local Analysis Section. These latter properties relate to the uniqueness of identifiability, which we shall extend in the Analysis Section. [There likelihood maxima and likelihood turning points within the vicinity is also a large literature on identifability in deterministic (rather than of sets of parameter values, and are shown to be based on local stochastic) models, for example the papers of Audoly et al. [5], and properties of the likelihood, in particular the rank of the Hessian PLoS ONE | www.plosone.org 1 January 2010 | Volume 5 | Issue 1 | e8915 Parameter Identifiability n matrix. Within the widely-used exponential family we demonstrate N[Qh and data x~ðÞx1,:::,xn [ S such that the log-likelihood Pn that these concepts (local identifiability, redundancy, weak local _ L~LxðÞjh ~ LxðÞl jh is maximized by at most one set of h [ N. identifiability, gradient weak local identifiability) largely coincide. l~1 We briefly consider applications of all these ideas to a recently If L~LxðÞjh is C1 as a function of h[V a set of parameters p developed general class of carcinogenesis models [12,13,14], ðÞhi i~1 [ intðÞV is gradient weakly locally identifiable if there ~ n presenting results that generalize those of Heidenreich [15] and exists0 a neighborhood1 N [Qh and data x ðÞx1,:::,xn [ S such Heidenreich et al. [16] in the context of the two-mutation cancer _ p BLLxjh C _ model [17]. These are outlined in the later parts of the Analysis that @ A ~0 (i.e., h is a turning point of L(xjh)) for at and the Discussion, and in more detail in a companion paper [12]. _ Lh i i~1 _ Analysis most one set of h [ N. Our definitions of identifiability and local identifiability coincide General Considerations on Parameter Identifiability with those of Rothenberg [1], Silvey [2](pp. 50, 81) and Catchpole As outlined in the Introduction, a general criterion for parameter and Morgan [3]. Rothenberg [1] proved that if the Fisher identifiability has been set out by Jacquez and Perry [4]. They information matrix, I~I(h), in a neighborhood of h [ intðÞV is of proposed a simple linearization of the problem, in the context of constant rank and satisfies various other more minor regularity models with normal error. They defined a notion of local conditions, then h [ intðÞV is locally identifiable if and only if I(h) identifiability, which is that in a local region of the parameter is non-singular. Clearly identifiability implies local identifiability, space, there is a unique h0 that fits some specified body of data, n which in turn implies weak local identifiability. By the Mean Value ðÞxi,yi i~1, i.e. for which the model predicted mean hxðÞjh is such Theorem [19](p. 107) gradient weak local identifiability implies that the residual sum of squares: weak local identifiability. Heuristically, (gradient) weak local identifiability happens when: Xn 2 S~ ½yl {hxðÞljh ð1Þ "# Xn Xn Xp 2 ~ LL LL(x jh) LL(x jh ) L L(x jh ) l 1 0~ ~ l ~ l 0 z l 0 :Dh Lh Lh Lh Lh Lh j has a unique minimum. We present here a straightforward i l~1 i l~1 i j~1 i j ð4Þ generalization of this to other error structures. If the model zO(jDhj2), 1ƒiƒp prediction hxðÞ~hxðÞjh for theÀÁ observed data y is a function of p some vector parameters h~ h then in general it can be j j~1 and inÀÁ general this system of p equations has a unique solution in p assumed, under the general equivalence of likelihood maximization Dh~ Dh in the neighborhood of h (assumed [ intðÞV ) j j~1 ! 0 and iteratively reweighted least squares for generalized linear p Pn L2L(x jh ) models [18](chapter 2) that one is trying to minimize: whenever l 0 has full rank (= p). This turns out l~1 LhiLhj i, j~1 "# Xn Xp 2 to be (nearly) the case, and will be proved later (Corollary 2). More 1 LhxðÞljh : S~ yl {hxðÞl jh0 { Dhj ð2Þ rigorously, we have the following result. vl Lhj l~1 j~1 h~h0 Theorem 1. Suppose that the log-likelihood L(xjh) is p C2 as a function of the parameter vector h [ V 5R , for all ƒ ƒ § n where ylðÞ1 l n ðÞn p is the observed measurement (e.g., the x~(x1, :::, xn) [ S . numbers of observed cases in the case of binomial or Poisson models) l v ƒlƒn (i) Suppose that for some x and h [ int(V) it is the case that at point and the l ðÞ1 are the current estimates of variance 2 !3 at each point.
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