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Theory of Estimation

In: Multivariate Statistics

Author

Listed:
  • Wolfgang Karl Härdle

    (Humboldt-Universität zu Berlin, C.A.S.E. Centre f. Appl. Stat. & Econ. School of Business and Economics)

  • Zdeněk Hlávka

    (Charles University in Prague, Faculty of Mathematics and Physics Department of Statistics)

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. Random sample

Suggested Citation

  • Wolfgang Karl Härdle & Zdeněk Hlávka, 2015. "Theory of Estimation," Springer Books, in: Multivariate Statistics, edition 2, chapter 0, pages 89-101, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-36005-3_6
    DOI: 10.1007/978-3-642-36005-3_6
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