Theory of Estimation

Wolfgang Karl Härdle and Zdeněk Hlávka
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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

Chapter Chapter 6 in Multivariate Statistics, 2015, pp 89-101 from Springer

Abstract: 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

Keywords: Marginal Distribution; Score Function; Maximum Likelihood Estimator; Fisher Information; Unbiased Estimator (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-642-36005-3_6

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