The Annotated Bibliography: Find at least 5 original, peer-review papers related to your final paper projectï¼ two can be from books if that’s all you can find. It’s okay if one or two of your sources are from subject matter journals where they’re using this technique, but I’d like most to be from statistics sources. . For each paper, please cite using APA guidelines and write a paragraph summarizing points and ideas from the paper that you feel will be important to your final paper. This should include at least one quote and at least one paraphrasing of ideas. These should be properly referenced. Using only the abstract from the paper is not acceptable.
The idea of final paper: I want to focus on the difference between the frequentist confidence interval and the Bayesian credible interval. And I think the Frequentist considers model parameters to be fixed and data to be random, while Bayesian considers model parameters to be random and data to be fixed. And I would like to force on the difference and how these two works and collect some data and try both approaches, and use that to illustrate the differences in understanding of the probability statements for both.
Here’s an example of a single source. Yours should be no shorter than this; this would be a minimum.
Morgan, Kari Lock and Rubin, Donald B. (2015) Rerandomization to Balance Tiers of Covariates, Journal of the American Statistical Association, 110:512, 1412-1421, DOI: 10.1080/01621459.2015.1079528
Although in a completely randomized design, covariates will on average be equally distributed between the treatment groups, this does not mean that they will be exactly distributed. It’s possible that due to the randomization, a covariate could be confounded considerably with the treatment. Morgan and Rubin (2015) consider this problem and propose new ways to rerandomize and measures to allow one to decide if it is balanced enough. In particular, they propose a method that allows one to give different covariates different levels of importance in the balancing. However, “the threshold for acceptable balance for large samples involves a trade-off between better covariate balance and computational time” (Morgan and Rubin (2015).
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