13. Parametric Bootstrap (Jan 24)

13 Parametric - Parametric Bootstrap If ˆ θ n is an estimator from an iid sample from a parametric model it has a sampling distribution If we

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Unformatted text preview: Parametric Bootstrap If ˆ θ n is an estimator from an iid sample from a parametric model, it has a sampling distribution. If we know the true value of the parameter, say θ , then we can find the distribution of ˆ θ n . This is because it is a statistic, so it is a function of the data. For the purpose of our notation, we thus have ˆ θ n = h ( X 1 ,...,X n ) for the appropriate function h . We then obtain the distribution of ˆ θ n using the methods of transformations. If the methods of transformations are not easy or useful to use, we can use a Monte Carlo simulation method to approximate the distribution of ˆ θ provided we also know θ . This is the method we used to approximate the sampling distribution in one of our earlier examples for an iid exponential example. What happens if we do not know θ ? The simplest way to implement the Monte Carlo method is to use a good guess of θ , in particular ˆ θ n . This method does work provided the distribution of the statistic is continuous...
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This note was uploaded on 01/17/2012 for the course AM 1234 taught by Professor Qqqq during the Spring '11 term at UWO.

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13 Parametric - Parametric Bootstrap If ˆ θ n is an estimator from an iid sample from a parametric model it has a sampling distribution If we

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