Goodness-of-Fit Testing Task

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A Goodness-of-Fit Testing Task is a statistical hypothesis test task that ....



References

2016

2013

  • http://onlinecourses.science.psu.edu/stat504/node/60
    • QUOTE: A goodness-of-fit test, in general, refers to measuring how well do the observed data correspond to the fitted (assumed) model. We will use this concept throughout the course as a way of checking the model fit. Like in a linear regression, in essence, the goodness-of-fit test compares the observed values to the expected (fitted or predicted) values.

      A goodness-of-fit statistic tests the following hypothesis: H0: the model M0 fits vs. HA: the model M0 does not fit (or, some other model MA fits)

      Most often the observed data represent the fit of the saturated model, the most complex model possible with the given data. Thus, most often the alternative hypothesis (HA) will represent the saturated model MA which fits perfectly because each observation has a separate parameter. Later in the course we will see that MA could be a model other than the saturated one. Let us now consider the simplest example of the goodness-of-fit test with categorical data.

      In the setting for one-way tables, we measure how well an observed variable X corresponds to a Mult (n, π) model for some vector of cell probabilities, π. We will consider two cases:

      1. when vector π is known, and
      2. when vector π is unknown.
    • In other words, we assume that under the null hypothesis data come from a Mult (n, π) distribution, and we test whether that model fits against the fit of the saturated model. The rationale behind any model fitting is the assumption that a complex mechanism of data generation may be represented by a simpler model. The goodness-of-fit test is applied to corroborate our assumption.

      Consider our Dice Example from the Introduction (below). We want to test the hypothesis that there is an equal probability of six sides; that is compare the observed frequencies to the assumed model: X ∼ Multi (n = 30, π0 = (1/6, 1/6, 1/6, 1/6, 1/6, 1/6)). You can think of this as simultaneously testing that the probability in each cell is being equal or not to a specified value, e.g.
      H0: ( π1, π2, π3, π4, π5, π6) = (1/6, 1/6, 1/6, 1/6, 1/6, 1/6) vs. HA: ( π1, π2, π3, π4, π5, π6) ≠ (1/6, 1/6, 1/6, 1/6, 1/6, 1/6).

      Most software packages will already have built-in functions that will do this for you; see the next section for examples in SAS and R. Here is a step-by step procedure to help you conceptually understand this test better and what is going on behind these functions.

      1. Step 1: If vector π is unknown we need to estimate these unknown parameters, and proceed to Step 2; If vector π is known proceed to Step 2.
      2. Step 2: Calculate the estimated (fitted) cell probabilities [math]\hat{π}_js[/math], and expected cell frequencies, Ej's under H0.
      3. Step 3: Calculate the Pearson goodness-of-fit statistic, X 2 and/or the deviance statistic, G2 and compare them to appropriate chi-squared distributions to make a decision.
      4. Step 4: If the decision is borderline or if the null hypothesis is rejected, further investigate which observations may be influential by looking, for example, at residuals.