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I have a question regarding the estimation of a latent-class gaussian mixture model, where the model is for three dimensional panel data set with individuals $i$, in country $j$ in time $t$. I want the classes to vary over the individuals, but within the classes there to be a specific effect for the country dimension $j$. However, I have some trouble deriving all the steps for the M-step with the additional dimensions and the parameters. Especially, I don't know how to do the maximization step in the last part.

My specification is as follows:

\begin{equation} \label{eq: mixture spec} y_{ijt} = x_{{s_i}jt}' \beta_{s_i} + \alpha_{s_i} + \gamma_{{s_i}j} + \epsilon_{ijt} \end{equation}

\noindent where $s_i$ is a latent, unobserved variable that we treat as a stochastic variable with $P[S_i = s] = p_s$ for $s = 1, ..., K$ and $\sum_{s=1}^K p_s = 1$. Mixture models are often estimated with the EM algorithm. This is an iterative algorithm that in the E-step calculates the posterior probabilities $\tilde{p}_s = P[s_i = s | y, \hat{\theta}]$ given the current set of parameter estimates $\theta$ and in the M-step maximises the expected log-likelihood function with respect to the set of parameters $\theta$.

Given this specification we have the following likelihood function:

\begin{multline} L(\theta) = \prod_{i=1}^N \sum_{s=1}^K p_s \left(\prod_{j=1}^M \prod_{t=1}^T \phi(y_{ijt}, x_{ijt}; \beta_{s_i}, \alpha_{s_i}, \gamma_{{s_i}j}) \right) \\ = \prod_{i=1}^N \sum_{s=1}^K p_s \left(\prod_{j=1}^M \prod_{t=1}^T \frac{1}{\sigma_{\varepsilon} \sqrt{2 \pi}} \exp \left(-\frac{1}{2 \sigma_{\varepsilon}^{2}}\left( y_{ijt} - x_{{s_i}jt}' \beta_{s_i} - \alpha_{s_i} - \gamma_{{s_i}j}\right)^{2}\right) \right) \end{multline}

For the EM algorithm we consider the complete data likelihood function:

\begin{equation} L_j(\theta) = \prod_{i=1}^N \prod_{s} \left(p_{s} \prod_{j=1}^M \prod_{t=1}^T \phi(y_{ijt}, x_{ijt}; \beta_{s}, \alpha_{s}, \gamma_{{s}j}, \sigma_{\epsilon}) \right) ^{I(s_i = s)} \end{equation}

and the subsequent log-complete likelihood:

\begin{equation} \label{eq: log complete} \ell_{j}(\theta)=\log L_{j}(\theta)= \sum_{i=1}^N \sum_{s=1}^K I(s_i = s) (\log \: p_s + \sum_{j=1}^M \sum_{t=1}^T \left(\log \: \phi(y_{ijt}, x_{ijt}; \beta_{s}, \alpha_{s}, \gamma_{{s}j}, \sigma_{\epsilon}) \right) ) \end{equation}

In the E-step we calculate:

\begin{equation} \pi_{i s} \equiv \mathrm{E}\left[I\left(s_{i}=s\right) \mid y_{ijt}, x_{ijt} \right]= \frac{ \phi(y_{ijt}, x_{ijt}; \beta_{s}, \alpha_{s}, \gamma_{{s}j}, \sigma_{\epsilon}) p_{s}} {\sum_{k=1}^K \phi(y_{ijt}, x_{ijt}; \beta_{k}, \alpha_{k}, \gamma_{{k}j}) p_k} \end{equation}

Substituting this in the log-complete likelihood gives:

\begin{equation} \ell_{j}(\theta)=\log L_{j}(\theta)= \sum_{i=1}^N \sum_{s=1}^K \pi_{i s} (\log \: p_s + \sum_{j=1}^M \sum_{t=1}^T \left(\log \: \phi(y_{ijt}, x_{ijt}; \beta_{s}, \alpha_{s}, \gamma_{{s}j}, \sigma_{\epsilon}) \right) ) \end{equation}

And the M-step results in maximising: \begin{equation} \max _{p, \alpha, \beta, \gamma, \sigma} \left( \left(\sum_{s=1}^{K} \sum_{i=1}^{N} \pi_{i s} \log p_{s}\right)+\left(\sum_{s=1}^{K} \sum_{i=1}^{N} \pi_{is} \sum_{j=1}^M \sum_{t=1}^T \log \phi(y_{ijt}, x_{ijt}; \beta_{s}, \alpha_{s}, \gamma_{{s}j}, \sigma_{\epsilon})\right) \right) \end{equation}

Solving \begin{equation} max_{p}\left(\sum_{s=1}^{K} \sum_{i=1}^{N} \pi_{i s} \log p_{s}\right) \text { subject to } \sum_{s=1}^{K} p_{s}=1 \end{equation}

yields \begin{equation} p_{s}=\frac{1}{i} \sum_{i=1}^{i}\pi_{is} \end{equation}

For the second part: \begin{multline} \max _{p, \alpha, \beta, \gamma, \sigma} \left(\sum_{s=1}^{K} \sum_{i=1}^{N} \pi_{is} \sum_{j=1}^M \sum_{t=1}^T \log \phi(y_{ijt}, x_{ijt}; \beta_{s}, \alpha_{s}, \gamma_{{s}j}, \sigma_{\varepsilon})\right) \\ = max _{p, \alpha, \beta, \gamma, \sigma} \left( \sum_{s=1}^{K} \sum_{i=1}^{N} \pi_{is} \sum_{j=1}^M \sum_{t=1}^T -\frac{1}{2} \log \left( 2 \pi \sigma_{\varepsilon}^{2}\right) - \frac{1}{2 \sigma_{\varepsilon}^{2}} \left( y_{ijt} - x_{sjt}' \beta_{s} - \alpha_{s} - \gamma_{{s}j}\right)^{2} \right) \end{multline}

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  • $\begingroup$ Hi @Xi'an, thanks for your reply, I corrected this. However, the part I am mostly struggling with is finding an analytical expression for the parameters \alpha, \beta, and \gamma. As for \alpha and \beta, there will be K parameters to estimate, one for echt latent class. However, for the gamma variable, there are M x K different parameters, as it runs over the j index. Any clue how to deal with this? $\endgroup$ Commented Jan 3, 2022 at 23:20
  • $\begingroup$ Thank you. I see it is a weighted least squares, but I am not sure how to solve this. Should I take first order differentials and solve those? Rewrite in matrix form and use a generalised result? The extra summation and difference in index confuses me. $\endgroup$ Commented Jan 4, 2022 at 9:14
  • $\begingroup$ Here is a complete derivation of the E-step and M-step for a mixture of two Bernoulli distributions. The derivation includes full explanations, so hopefully that can help. $\endgroup$
    – mhdadk
    Commented Jan 4, 2022 at 10:34

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One can rewrite the target as $$ \sum_{i=1}^{N} \pi_{is} \sum_{j=1}^M \sum_{t=1}^T \left( y_{ijt} - x_{sjt}' \beta_{s} - \alpha_{s} - \gamma_{{s}j}\right)^{2}=\sum_{i=1}^{N} \pi_{is} \vert\vert y_{i\cdot\cdot}-X^\sf{T}\theta_s\vert\vert^2 $$ where is the $M\cdot T$ vector with components $y_{ijt}$ and $X$ is the $(M\cdot T,M+\ell+1)$ matrix with rows $(x_{sjt}^\prime,1,\mathbb I_1(j),\ldots,\mathbb I_M(j))$, $\ell$ being the dimension of $x_{sjt}$

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  • $\begingroup$ Thank you very much, this was the insight I required. $\endgroup$ Commented Jan 24, 2022 at 13:16

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