Another update
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@@ -232,21 +232,22 @@ $$
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For the models above, these are:
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+--------------+---------------------+--------------------+
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| Type of Data | Distribution Family | Link Function |
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+==============+=====================+====================+
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+---------------+-------------------+------------------+
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| Type of Data | Distribution | Link Function |
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| | Family | |
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+===============+===================+==================+
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| continuous | Normal | identity: |
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| | | |
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| | | $$ |
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| | | g(x)=x |
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| | | $$ |
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+--------------+---------------------+--------------------+
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+---------------+-------------------+------------------+
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| count | Poisson | log: |
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| | | |
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| | | $$ |
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| | | g(x) = \log(x) |
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| | | $$ |
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+--------------+---------------------+--------------------+
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+---------------+-------------------+------------------+
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| binary | Bernoulli | logit: |
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| | | |
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| | | $$ |
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@@ -254,9 +255,10 @@ For the models above, these are:
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| | | ( |
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| | | \ |
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| | | f |
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| | | rac{x}{1-x}\right) |
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| | | ra |
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| | | c{x}{1-x}\right) |
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| | | $$ |
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+--------------+---------------------+--------------------+
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+---------------+-------------------+------------------+
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In general, the parameter vector $\beta$ is estimated via maximizing the
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likelihood, i.e.,
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