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In [[statistics]], '''identifiability''' is a property which a [[statistical model|model]] must satisfy in order for precise [[statistical inference|inference]] to be possible. We say that the model is '''identifiable''' if it is theoretically possible to learn the true value of this model’s underlying parameter after obtaining an infinite number of observations from it. Mathematically, this is equivalent to saying that different values of the parameter  must generate different [[probability distribution]]s of the observable variables. Usually the model is identifiable only under certain technical restrictions, in which case the set of these requirements is called the '''identification conditions'''.
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A model that fails to be identifiable is said to be '''non-identifiable''' or '''unidentifiable'''. In some cases, even though a model is non-identifiable, it is still possible to learn the true values of a certain subset of the model parameters. In this case we say that the model is '''partially identifiable'''. In other cases it may be possible to learn the location of the true parameter up to a certain finite region of the parameter space, in which case the model is [[set identifiable]].
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==Definition==
Let ℘ = {''P<sub>θ</sub>'': ''θ''∈Θ} be a [[statistical model]] where the parameter space Θ is either finite- or infinite-dimensional. We say that ℘ is '''identifiable''' if the mapping ''θ'' ↦ ''P<sub>θ</sub>'' is [[bijection|one-to-one]]:<ref>{{harvnb|Lehmann|Casella|1998|loc=Definition 1.5.2}}</ref>
: <math>
    P_{\theta_1}=P_{\theta_2} \quad\Rightarrow\quad \theta_1=\theta_2 \quad\ \text{for all } \theta_1,\theta_2\in\Theta.
  </math>
 
This definition means that distinct values of ''θ'' should correspond to distinct probability distributions: if ''θ''<sub>1</sub>≠''θ''<sub>2</sub>, then also ''P''<sub>''θ''<sub>1</sub></sub>≠''P''<sub>''θ''<sub>2</sub></sub>.<ref>{{harvnb|van der Vaart|1998|page=62}}</ref> If the distributions are defined in terms of the [[probability density function]]s, then two pdfs should be considered distinct only if they differ on a set of non-zero measure (for example two functions ƒ<sub>1</sub>(''x'')='''1'''<sub>0≤''x''<1</sub> and ƒ<sub>2</sub>(''x'')='''1'''<sub>0≤''x''≤1</sub> differ only at a single point ''x''=1 — a set of [[Lebesgue measure|measure]] zero — and thus cannot be considered as distinct pdfs).
 
Identifiability of the model in the sense of invertibility of the map ''θ'' ↦ ''P<sub>θ</sub>'' is equivalent to being able to learn the model’s true parameter if the model can be observed indefinitely long. Indeed, if {''X<sub>t</sub>''}⊆''S'' is the sequence of observations from the model, then by the [[strong law of large numbers]],
: <math>
    \frac{1}{T} \sum_{t=1}^T \mathbf{1}_{\{X_t\in A\}} \ \xrightarrow{a.s.}\ \operatorname{Pr}[X_t\in A],
  </math>
for every measurable set ''A''⊆''S'' (here '''1'''<sub>{…}</sub> is the [[indicator function]]). Thus with an infinite number of observations we will be able to find the true probability distribution ''P''<sub>0</sub> in the model, and since the identifiability condition above requires that the map ''θ'' ↦ ''P<sub>θ</sub>'' be invertible, we will also be able to find the true value of the parameter which generated given distribution ''P''<sub>0</sub>.
 
==Examples==
 
===Example 1===
Let ℘ be the [[normal distribution|normal]] [[location-scale family]]:
: <math>
    \mathcal{P} = \Big\{\ f_\theta(x) = \tfrac{1}{\sqrt{2\pi}\sigma} e^{ -\frac{1}{2\sigma^2}(x-\mu)^2 }\ \Big|\ \theta=(\mu,\sigma): \mu\in\mathbb{R}, \,\sigma\!>0 \ \Big\}.
  </math>
Then
: <math>\begin{align}
    f_{\theta_1}=f_{\theta_2}\ 
    &\Leftrightarrow\ \tfrac{1}{\sqrt{2\pi}\sigma_1}e^{ -\frac{1}{2\sigma_1^2}(x-\mu_1)^2 } = \tfrac{1}{\sqrt{2\pi}\sigma_2}e^{ -\frac{1}{2\sigma_2^2}(x-\mu_2)^2 } \\
    &\Leftrightarrow\ \tfrac{1}{\sigma_1^2}(x-\mu_1)^2 + \ln \sigma_1^2 = \tfrac{1}{\sigma_2^2}(x-\mu_2)^2 + \ln \sigma_2^2 \\
    &\Leftrightarrow\ x^2\big(\tfrac{1}{\sigma_1^2}-\tfrac{1}{\sigma_2^2}\big) - 2x\big(\tfrac{\mu_1}{\sigma_1^2}-\tfrac{\mu_2}{\sigma_2^2}\big) + \big(\tfrac{\mu_1^2}{\sigma_1^2}-\tfrac{\mu_2^2}{\sigma_2^2}+\ln\sigma_1^2-\ln\sigma_2^2\big) = 0
  \end{align}</math>
This expression is equal to zero for almost all ''x'' only when all its coefficients are equal to zero, which is only possible when |''σ''<sub>1</sub>| = |''σ''<sub>2</sub>| and ''μ''<sub>1</sub> = ''μ''<sub>2</sub>. Since in the scale parameter ''σ'' is restricted to be greater than zero, we conclude that the model is identifiable: ƒ<sub>''θ''<sub>1</sub></sub>=ƒ<sub>''θ''<sub>2</sub></sub> ⇔ ''θ''<sub>1</sub>=''θ''<sub>2</sub>.
 
===Example 2===
Let ℘ be the standard [[linear regression model]]:
: <math>
    y = \beta'x + \varepsilon, \quad \operatorname{E}[\,\varepsilon|x\,]=0
  </math>
(where ′ denotes matrix [[transpose]]). Then the parameter ''β'' is identifiable if and only if the matrix E[xx′] is invertible. Thus, this is the '''identification condition''' in the model.
 
===Example 3===
Suppose ℘ is the classical [[errors-in-variables]] [[linear model]]:
: <math>\begin{cases}
    y = \beta x^* + \varepsilon, \\
    x = x^* + \eta,
  \end{cases}</math>
where (''ε'',''η'',''x*'') are jointly normal independent random variables with zero expected value and unknown variances, and only the variables (''x'',''y'') are observed. Then this model is not identifiable,<ref name="riersol">{{harvnb|Reiersøl|1950}}</ref> only the product βσ²<sub style="position:relative;left:-.5em">∗</sub> is (where σ²<sub style="position:relative;left:-.5em">∗</sub> is the variance of the latent regressor ''x*''). This is also an example of [[set identifiability|set identifiable]] model: although the exact value of ''β'' cannot be learned, we can guarantee that it must lie somewhere in the interval (''β''<sub>yx</sub>, ''β''<sub>xy</sub>), where ''β''<sub>yx</sub> is the coefficient in [[Ordinary least squares|OLS]] regression of ''y'' on ''x'', and ''β''<sub>xy</sub> is the coefficient in OLS regression of ''x'' on ''y''.<ref>{{harvnb|Casella|Berger|2001|page=583}}</ref>
 
If we abandon the normality assumption and require that ''x*'' were '''not''' normally distributed, retaining only the independence condition ''ε''⊥''η''⊥''x*'', then the model becomes identifiable.<ref name="riersol"/>
 
==Software==
In the case of parameter estimation in partially observed dynamical systems, the profile likelihood can be also used for structural and practical identifiability analysis.<ref>
{{Citation
|last=Raue |first=A
|title=Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood
|url=http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btp358?ijkey=iYp4jPP50F5vdX0&keytype=ref
|journal=Bioinformatics
|year=2009
|doi=10.1093/bioinformatics/btp358
|pmid=19505944
|last2=Kreutz
|first2=C
|last3=Maiwald
|first3=T
|last4=Bachmann
|first4=J
|last5=Schilling
|first5=M
|last6=Klingmüller
|first6=U
|last7=Timmer
|first7=J
|volume=25
|issue=15
|pages=1923–9
|postscript=.
}}</ref> An implementation of the [http://www.fdmold.uni-freiburg.de/~araue/Main/Software Profile Likelihood Approach] is available in the MATLAB Toolbox [[PottersWheel]].
 
==See also==
* [[Parameter identification problem]]
 
==References==
{{refbegin}}
* {{Citation
  | last = Casella
  | first = George
  | coauthors = Roger L. Berger
  | title = Statistical inference
  | year = 2001
  | edition = 2nd
  | isbn = 0-534-24312-6
  | ref = CITEREFCasellaBerger2001
  | lccn = QA276.C37 2001
  }}
* {{Citation
  | last = Hsiao
  | first = Cheng
  | title = Identification
  | year = 1983
  | series = Handbook of Econometrics, Vol. 1, Ch.4
  | publisher = North-Holland Publishing Company
  }}
* {{Citation
  | last = Lehmann
  | first = E. L.
  | coauthors = G. Casella
  | title = Theory of point estimation
  | edition = 2nd
  | year = 1998
  | publisher = Springer
  | isbn = 0-387-98502-6
  | ref = CITEREFLehmannCasella1998
  }}
* {{Citation
  | last = Reiersøl
  | first = Olav
  | year = 1950
  | title = Identifiability of a linear relation between variables which are subject to error
  | journal = Econometrica
  | volume = 18
  | issue = 4
  | pages = 375–389
  | jstor = 1907835
  | doi = 10.2307/1907835
  | publisher = The Econometric Society
  }}
* {{Citation
  | last = van der Vaart
  | first = A.W.
  | title = Asymptotic Statistics
  | year = 1998
  | publisher = Cambridge University Press
  | isbn = 978-0-521-49603-2
  | ref = CITEREFvan_der_Vaart1998
  }}
{{refend}}
 
===Notes===
{{reflist}}
 
[[Category:Estimation theory]]

Latest revision as of 08:27, 13 January 2015

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