Difference between revisions of "Estimation of the observed Fisher information matrix"
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{{EquationWithRef | {{EquationWithRef | ||
− | |equation=<div id="eq: | + | |equation=<div id="eq:fim_diff_a"><math>\begin{eqnarray} |
− | \partial_{\theta_j}{ {\llike}(\theta)} &\approx& \displaystyle{ \frac{ {\llike}(\theta+\nu^{(j)})- {\llike}(\theta-\nu^{(j)})}{2\nu} } \ , \\ | + | \partial_{\theta_j}{ {\llike}(\theta)} &\approx& \displaystyle{ \frac{ {\llike}(\theta+\nu^{(j)})- {\llike}(\theta-\nu^{(j)})}{2\nu} } \ , |
+ | \end{eqnarray}</math></div> | ||
+ | |reference=(6) }} | ||
+ | {{EquationWithRef | ||
+ | |equation=<div id="eq:fim_diff_b"><math>\begin{eqnarray} | ||
\partial^2_{\theta_j,\theta_k}{ {\llike}(\theta)} &\approx& \displaystyle{ \frac{ {\llike}(\theta+\nu^{(j)}+\nu^{(k)})-{\llike}(\theta+\nu^{(j)}-\nu^{(k)}) | \partial^2_{\theta_j,\theta_k}{ {\llike}(\theta)} &\approx& \displaystyle{ \frac{ {\llike}(\theta+\nu^{(j)}+\nu^{(k)})-{\llike}(\theta+\nu^{(j)}-\nu^{(k)}) | ||
-{\llike}(\theta-\nu^{(j)}+\nu^{(k)})+{\llike}(\theta-\nu^{(j)}-\nu^{(k)})}{4\nu^2} } . | -{\llike}(\theta-\nu^{(j)}+\nu^{(k)})+{\llike}(\theta-\nu^{(j)}-\nu^{(k)})}{4\nu^2} } . | ||
\end{eqnarray}</math></div> | \end{eqnarray}</math></div> | ||
− | |reference=( | + | |reference=(7) }} |
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<blockquote> | <blockquote> | ||
1. for $i=1,2,\ldots N$, get some estimate $(\hpsi_i)$ of the individual parameters $(\psi_i)$ (we can average for example the last terms of the sequence $(\psi_i^{(k)})$ drawn during the last iterations of the SAEM algorithm), | 1. for $i=1,2,\ldots N$, get some estimate $(\hpsi_i)$ of the individual parameters $(\psi_i)$ (we can average for example the last terms of the sequence $(\psi_i^{(k)})$ drawn during the last iterations of the SAEM algorithm), | ||
+ | |||
2. for $i=1,2,\ldots N$, compute $\hphi_i=h(\hpsi_i)$, compute the mean and the variance of the normal distribution defined in [[#eq:fim_approx|(5)]], and compute ${\llike}(\theta)$ using this normal approximation, | 2. for $i=1,2,\ldots N$, compute $\hphi_i=h(\hpsi_i)$, compute the mean and the variance of the normal distribution defined in [[#eq:fim_approx|(5)]], and compute ${\llike}(\theta)$ using this normal approximation, | ||
− | 3. use [[#eq: | + | |
+ | 3. use [[#eq:fim_diff_a|(6)]] to approximate the matrix of second-order derivatives of ${\llike}(\theta)$. | ||
</blockquote> | </blockquote> | ||
}} | }} |
Revision as of 13:36, 16 May 2013
Estimation of the observed F.I.M. using a stochastic approximation
The observed Fisher information matrix (F.I.M.) is a function of $\theta$ defined as
\(\begin{eqnarray}
I(\theta) &=& -\DDt{\log (\like(\theta;\by))} \\
&=& -\DDt{\log (\py(\by;\theta))}
\end{eqnarray}\)
|
(1) |
Due to the complex expression of the likelihood, $I(\theta)$ has no closed form expression. It is however possible to estimate it using a stochastic approximation procedure, based on Louis' formula:
where
$\Dt{\log (\pmacro(\by,\bpsi;\theta))}$ is defined as a combination of conditional expectations. Each of these conditional expectations can be estimated by Monte-Carlo, or equivalently approximated using a stochastic approximation algorithm.
We can then draw a sequence $(\psi_i^{(k)})$ using a Metropolis-Hasting algorithm and estimate the observed F.I.M on-line. At iteration $k$ of the algorithm:
- $\textbf{Simulation-step}$: for $i=1,2,\ldots N$, draw $\psi_i^{(k)}$ from $m$ iterations of the Metropolis-Hastings algorithm described in The Metropolis-Hastings algorithm for simulating the individual parameters with $\pmacro(\psi_i |y_i ;{\theta})$ as limiting distribution.
- $\textbf{Stochastic approximation}$: update $D_k$, $G_k$ and $\Delta_k$ according to the following recurrent relations:
- where $(\gamma_k)$ is a decreasing sequence of positive numbers such that $\gamma_1=1$, $ \sum_{k=1}^{\infty} \gamma_k = \infty$ and $\sum_{k=1}^{\infty} \gamma_k^2 < \infty$.
- $\textbf{Estimation-step}$: update the estimate $H_k$ of the F.I.M. according to
Implementing this algorithm therefore requires to compute the first and second derivatives of
Assume first that the joint distribution of $\by$ and $\bpsi$ decomposes as
\(
\pypsi(\by,\bpsi;\theta) = \pcypsi(\by | \bpsi)\ppsi(\bpsi;\theta).
\)
|
(2) |
his assumption means that, for any $i=1,2,\ldots N$, all the components of $\psi_i$ are random and that there exists a sufficient statistic ${\cal S}(\bpsi)$ for the estimation of $\theta$. It is then sufficient to compute the first and second derivatives of $\log (\pmacro(\bpsi;\theta))$ for estimating the F.I.M. This can be done relatively simply in a closed form when the individual parameters are normally distributed (eventually up to a transformation $h$).
If some component of $\psi_i$ has no variability, (2) does not hold anymore but we will decompose $\theta$ into $(\theta_y,\theta_\psi)$ such that
We will then need to compute the first and second derivatives of $\log(\pcyipsii(y_i |\psi_i ; \theta_y))$ and $\log(\ppsii(\psi_i;\theta_\psi))$. Derivatives of $\log(\pcyipsii(y_i |\psi_i ; \theta_y))$ that don't have closed form expressions can be obtained by central differences.
Estimation of the F.I.M. using a linearization of the model
Consider here a model for continuous data, using the $\phi$-parametrization for the individual parameters:
Let $\hphi_i$ be some predicted value of $\phi_i$ ($\hphi_i$ can for instance be the estimated mean or the estimated mode of the conditional distribution $\pmacro(\phi_i |y_i ; \hat{\theta})$).
We can therefore linearize the model for the observations $(y_{ij}, 1\leq j \leq n_i)$ of individual $i$ around the vector of predicted individual parameters. Let $\Dphi{f(t , \phi)}$ be the row vector of derivatives of $f(t , \phi)$ with respect to $\phi$. Then,
Then, we can approximate the marginal distribution of the vector $y_i$ as a normal distribution:
\(
y_{i} \approx {\cal N}\left(f(t_{i} , \hphi_i) + \Dphi{f(t_{i} , \hphi_i)} \, (\phi_{\rm pop} - \hphi_i) ,
\Dphi{f(t_{i} , \hphi_i)} \Omega \Dphi{f(t_{i} , \hphi_i)}^{\transpose} + g(t_{i} , \hphi_i)\Sigma_{n_i} g(t_{ij} , \hphi_i)^{\transpose} \right).
\)
|
(5) |
where $\Sigma_{n_i}$ is the variance-covariance matrix of $\teps_{i,1},\ldots,\teps_{i,n_i})$. If the $\teps_{ij}$'s are i.i.d, then $\Sigma_{n_i}$ is the identity matrix.
We can equivalently use the original $\psi$-parametrization using the fact that $\phi_i=h(\psi_i)$. Then,
where $J_h$ is the Jacobian of $h$.
We then can approximate the observed log-likelihood ${\llike}(\theta) = \log({\like}(\theta;\by))=\sum_{i=1}^N \log(\pyi(y_i;\theta))$ using this normal approximation. We can also derive the F.I.M by computing the matrix of second-order partial derivatives of ${\llike}(\theta)$.
Except for very simple models, computing these second-order order derivatives in a closed form is not straightforward. Then, finite differences can be used for approximating numerically these quantities. We can use for instance a central difference approximation of the second derivative of ${\llike}(\theta)$:
Let $\nu>0$. For $j=1,2,\ldots, m$, let $\nu^{(j)}=(\nu^{(j)}_{k}, 1\leq k \leq m)$ be the $m$-vector such that
Then, for $\nu$ small enough,
\(\begin{eqnarray}
\partial_{\theta_j}{ {\llike}(\theta)} &\approx& \displaystyle{ \frac{ {\llike}(\theta+\nu^{(j)})- {\llike}(\theta-\nu^{(j)})}{2\nu} } \ ,
\end{eqnarray}\)
|
(6) |
\(\begin{eqnarray}
\partial^2_{\theta_j,\theta_k}{ {\llike}(\theta)} &\approx& \displaystyle{ \frac{ {\llike}(\theta+\nu^{(j)}+\nu^{(k)})-{\llike}(\theta+\nu^{(j)}-\nu^{(k)})
-{\llike}(\theta-\nu^{(j)}+\nu^{(k)})+{\llike}(\theta-\nu^{(j)}-\nu^{(k)})}{4\nu^2} } .
\end{eqnarray}\)
|
(7) |