Comments on: [MADS] plug-in estimator
http://hea-www.harvard.edu/AstroStat/slog/2009/mads-plug-in-estimator/
Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing bordersFri, 01 Jun 2012 18:47:52 +0000hourly1http://wordpress.org/?v=3.4By: hlee
http://hea-www.harvard.edu/AstroStat/slog/2009/mads-plug-in-estimator/comment-page-1/#comment-888
hleeTue, 09 Jun 2009 22:35:59 +0000http://hea-www.harvard.edu/AstroStat/slog/?p=2199#comment-888I cannot give you a short answer but can say that it is jargon in nonparametric statistics. Before parameterization or setting models (likelihoods and priors from a distribution family), one wishes to characterize a sample distribution by looking its central location and scale (skewness, kurtosis, empirical cdf, contours, principle components, covariance matrix, etc), which are navigated via computing average and sample standard deviation. The equation of computing average, for example, is a plug-in estimator. Since we haven't posed Gaussian or Poisson distribution as the ground truth, we cannot say the average plug-in estimator is the mean (mu in Gaussian or lambda in Poisosn) estimator. We can locate the center of data distribution from a plug-in estimator and thus, without parameterization but based on probability theory and asymptotics, we can quantify the uncertainty of that location plug-in estimator.I cannot give you a short answer but can say that it is jargon in nonparametric statistics. Before parameterization or setting models (likelihoods and priors from a distribution family), one wishes to characterize a sample distribution by looking its central location and scale (skewness, kurtosis, empirical cdf, contours, principle components, covariance matrix, etc), which are navigated via computing average and sample standard deviation. The equation of computing average, for example, is a plug-in estimator. Since we haven’t posed Gaussian or Poisson distribution as the ground truth, we cannot say the average plug-in estimator is the mean (mu in Gaussian or lambda in Poisosn) estimator. We can locate the center of data distribution from a plug-in estimator and thus, without parameterization but based on probability theory and asymptotics, we can quantify the uncertainty of that location plug-in estimator.
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http://hea-www.harvard.edu/AstroStat/slog/2009/mads-plug-in-estimator/comment-page-1/#comment-887
yaserTue, 09 Jun 2009 21:07:01 +0000http://hea-www.harvard.edu/AstroStat/slog/?p=2199#comment-887what is plug-in estimator?what is plug-in estimator?
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