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<h1 id="other-models-miscmodels"><span id="miscmodels"></span><span id="module-statsmodels.miscmodels"></span>Other Models <code class="xref py py-mod docutils literal notranslate"><span class="pre">miscmodels</span></code><a class="headerlink" href="#other-models-miscmodels" title="Link to this heading">¶</a></h1>
<p><a class="reference internal" href="#module-statsmodels.miscmodels" title="statsmodels.miscmodels (Python module)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">statsmodels.miscmodels</span></code></a> contains model classes and that do not yet fit into
any other category, or are basic implementations that are not yet polished and will most
likely still change. Some of these models were written as examples for the generic
maximum likelihood framework, and there will be others that might be based on general
method of moments.</p>
<p>The models in this category have been checked for basic cases, but might be more exposed
to numerical problems than the complete implementation. For example, count.Poisson has
been added using only the generic maximum likelihood framework, the standard errors
are based on the numerical evaluation of the Hessian, while discretemod.Poisson uses
analytical Gradients and Hessian and will be more precise, especially in cases when there
is strong multicollinearity.
On the other hand, by subclassing GenericLikelihoodModel, it is easy to add new models,
another example can be seen in the zero inflated Poisson model, miscmodels.count.</p>
<h2 id="module-statsmodels.miscmodels.count"><span id="count-models-count"></span>Count Models <code class="xref py py-mod docutils literal notranslate"><span class="pre">count</span></code><a class="headerlink" href="#module-statsmodels.miscmodels.count" title="Link to this heading">¶</a></h2>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.miscmodels.count.PoissonGMLE.html#statsmodels.miscmodels.count.PoissonGMLE" title="statsmodels.miscmodels.count.PoissonGMLE (Python class) — Maximum Likelihood Estimation of Poisson Model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PoissonGMLE</span></code></a>(endog[, exog, loglike, score, ...])</p></td>
<td><p>Maximum Likelihood Estimation of Poisson Model</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.miscmodels.count.PoissonOffsetGMLE.html#statsmodels.miscmodels.count.PoissonOffsetGMLE" title="statsmodels.miscmodels.count.PoissonOffsetGMLE (Python class) — Maximum Likelihood Estimation of Poisson Model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PoissonOffsetGMLE</span></code></a>(endog[, exog, offset, missing])</p></td>
<td><p>Maximum Likelihood Estimation of Poisson Model</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.miscmodels.count.PoissonZiGMLE.html#statsmodels.miscmodels.count.PoissonZiGMLE" title="statsmodels.miscmodels.count.PoissonZiGMLE (Python class) — Maximum Likelihood Estimation of Poisson Model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PoissonZiGMLE</span></code></a>(endog[, exog, offset, missing])</p></td>
<td><p>Maximum Likelihood Estimation of Poisson Model</p></td>
</tr>
</tbody>
</table>
<h2 id="linear-model-with-t-distributed-errors">Linear Model with t-distributed errors<a class="headerlink" href="#linear-model-with-t-distributed-errors" title="Link to this heading">¶</a></h2>
<p>This is a class that shows that a new model can be defined by only specifying the
method for the loglikelihood. All result statistics are inherited from the generic
likelihood model and result classes. The results have been checked against R for a
simple case.</p>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.miscmodels.tmodel.TLinearModel.html#statsmodels.miscmodels.tmodel.TLinearModel" title="statsmodels.miscmodels.tmodel.TLinearModel (Python class) — Maximum Likelihood Estimation of Linear Model with t-distributed errors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TLinearModel</span></code></a>(endog[, exog, loglike, score, ...])</p></td>
<td><p>Maximum Likelihood Estimation of Linear Model with t-distributed errors</p></td>
</tr>
</tbody>
</table>
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