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14.3: Assumptions of GLMs

  • Page ID
    57770
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    When we were creating ordinary least squares (OLS) regression, we made one assumption: \(\varepsilon \stackrel{\text{iid}}{\sim} N(0, \sigma^2)\). After learning the mathematics of fitting the models, we went back and figured out how to test these assumptions. The same will be true here.

    When performing generalized linear modeling, you make at least three assumptions:

    1. you assume the linear predictor is correct;
    2. you assume the conditional distribution of the dependent variable is correct; and
    3. you assume the link function is correct.

    If these assumptions are not met by the data and model, then there is information in the data that you are ignoring.

    Testing these is usually not as easy as in the case of OLS regression. The linear predictor and the link function, together, determine the functional form. It can sometimes be tested using a runs test. That is the easy part. Testing the correctness of the conditional distribution is much more involved. It requires that one understands the hypothesized distribution, especially in terms of range, expected values, and variances. Note that tests of heteroskedasticity may not be useful here; many distributions are heteroskedastic by definition.

    The testing must be done, however.

    Caution

    As you read through this part of the book, always keep in mind what we are assuming. That will help you determine the requirements and how to test them.


    This page titled 14.3: Assumptions of GLMs is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Ole Forsberg.

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