Abstract
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 \(\{x_{i}\}_{i=1}^{n}\) 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 θ (x) = f(x; θ): this could be the mean, the covariance matrix, kurtosis, or something else.
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© 2015 Springer-Verlag Berlin Heidelberg
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Härdle, W.K., Hlávka, Z. (2015). Theory of Estimation. In: Multivariate Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36005-3_6
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DOI: https://doi.org/10.1007/978-3-642-36005-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36004-6
Online ISBN: 978-3-642-36005-3
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