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Lecture 5: Estimation - University of Washington

Lecture 5: Estimation Goals Basic concepts of Estimation Statistical approaches for estimating parameters Parametric interval Estimation Nonparametric interval Estimation (bootstrap). Central Dogma of Statistics Probability Population Descriptive Statistics Sample Inferential Statistics Estimation Estimator: Statistic whose calculated value is used to estimate a population parameter, ". Estimate: A particular realization of an estimator, " . ! Types of Estimators: - point estimate: single number that can be regarded ! as the most plausible value of ". - interval estimate: a range of numbers, called a confidence interval indicating, can be regarded as likely containing the true value of ". ! Properties of Good Estimators In the Frequentist world view parameters are fixed, statistics are rv and vary from sample to sample ( , have an associated sampling distribution). In theory, there are many potential estimators for a population parameter What are characteristics of good estimators?

¥Estimation proceeds by Þnding the value of that makes the observed data most likely! " LetÕs Play T/F ¥True or False: The maximum likelihood estimate (mle) of ... The likelihood is the probability of the data given the parameter and represents the data now available.

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