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Titlebook: Approximation Methods in Probability Theory; Vydas Čekanavičius Textbook 2016 Springer International Publishing Switzerland 2016 character

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https://doi.org/10.1007/978-3-663-09968-0In this chapter, we consider measures .. Note that if a measure is concentrated on a lattice different from ., then it can be reduced to the integer-lattice case by a simple linear transform.
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Versicherung und RisikoforschungWe recall that, for ., .. One of our aims is to show that many estimates in total variation have the same order of accuracy as for the Kolmogorov norm. In this chapter, we do not consider the Stein method, which is presented in Chap. .
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https://doi.org/10.1007/978-3-663-09967-3In this chapter, we demonstrate that for lattice distributions with a sufficient number of finite moments the non-uniform estimates can be proved via a somewhat modified Tsaregradskii inequality.
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https://doi.org/10.1007/978-3-658-35648-4For absolutely continuous distributions it is more convenient to write all results in terms of distribution functions. We recall that the distribution function of . is . and . Here .(.) is a nonnegative function integrable on the real line, called the density of ..
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,Implikationen für das Vertriebsmanagement,In this chapter we consider the characteristic function method, when moments of higher order exist. For the Stein method, see Sect. .
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