MATH 321 Lecture Notes - Lecture 50: Statistic, Statistical Inference, Simple Random Sample
14 Jul 2019
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9. 5 estimating with bootstrapping learning objectives: estimate a parameter using the bootstrap method. 9. 5. 1 estimate a parameter using the bootstrap method (1 of 12) Bootstrapping is a computer-intensive approach to statistical inference whereby parameters are estimated by treating a set of sample data as a population. A computer is used to resample with replacement n observations from the sample data. This process is repeated many (say, 1000) times. For each resample, the statistic (such as the sample mean) is obtained. 9. 5. 1 estimate a parameter using the bootstrap method (2 of 12) To use a bootstrap, two basic requirements must be satisfied: the center of the bootstrap distribution must be close to the center of the original sample data. For example, the mean of all bootstrap means must be close to the mean of the original data: the distribution of the bootstrap sample statistic must be symmetric. 9. 5. 1 estimate a parameter using the bootstrap method (3 of 12)
