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Theory of Estimation

No, no; I never guess. It is a shocking habit—destructive to the logical faculty. Sherlock Holmes in “The Sign of Four” The basic objective of statistics is to understand and model the underlying processes that generate the data. This involves statistical inference, where we extract information contained in a sample by applying a model. In general, we assume an i.i.d. random sample {xi}n i=1 from which we extract unknown characteristics of its distribution. In parametric statistics these are condensed in a p-variate vector θ characterizing the unknown properties of the population pdf f(x, θ): this could be the mean, the covariance matrix, kurtosis, or something else.

Keywords

Logical Faculty Score Function Maximum Likelihood Estimator Fisher Information Unbiased Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

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