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12.4: End-of-Chapter Materials

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    57766
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    R Functions

    This chapter had no R functions. It was all mathematics and concepts. Yay!!

    Exercises

    1. Prove that the maximum likelihood estimator of \(\pi\) is \(x/n\) in a Binomial experiment.
    2. Prove Theorem 12.2.3: The MLE of σ².
    3. Prove \(\hat{\beta}_0\) is unbiased for \(\beta_0\).
    4. Prove \(\hat{\beta}_1\) is unbiased for \(\beta_1\).
    5. Prove \(\hat{\sigma}^2\) is biased for \(\sigma^2\) and that the bias is exactly \(\frac{n-1}{n}\sigma^2\).

    Theory Readings

    • Bin Dai, Shilin Ding, and Grace Wahba (2012). "Multivariate Bernoulli Distribution." Bernoulli. 19(4): 1465--1483.
      doi: 10.3150/12-BEJSP10
    • Dmitry Panchenko (2006). "Lecture 3: Properties of MLE: consistency, asymptotic normality. Fisher information." Open Courseware/MIT.
    • Aaron Thode, Michele Zanolin, Eran Naftali, Ian Ingram, Purnima Ratilal, and Nicholas C. Makris (2002). "Necessary conditions for a maximum likelihood estimate to become asymptotically unbiased and attain the Cramer-Rao lower bound. II. Range and depth localization of a sound source in an ocean waveguide." The Journal of the Acoustical Society of America. 112(5): 1890--1910.
      doi: 10.1121/1.1496765

    This page titled 12.4: End-of-Chapter Materials 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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