Titlebook: Decoupling; From Dependence to I Víctor H. Peña,Evarist Giné Book 1999 Springer Science+Business Media New York 1999 Law of large numbers.M

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https://doi.org/10.1007/978-3-642-57471-9e use of each of the main theorems in this chapter by deriving properties of certain multidimensional generalizations of the median, and in general, of .-estimators, and by studying estimators of the cumulative hazard and distribution functions of a random variable based on truncated data.

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Der bankbetriebliche Zahlungsverkehrhows that, in certain aspects, .-statistics behave more like sums of independent random variables than like martingales (in other aspects like, e.g., regarding the law of large numbers, converse part--see Section 4.1—, .-statistics do not behave at all as sums of independent variables).

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Randomly Stopped Processes with Independent Increments,d with the processes in question. The aim of this chapter is to provide a decoupling reinterpretation and extensions of these classical inequalities. We will present an extension of these results to all processes continuous on the right with limits from the left, with independent increments and valu

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Limit Theorems for , -rocesses,e use of each of the main theorems in this chapter by deriving properties of certain multidimensional generalizations of the median, and in general, of .-estimators, and by studying estimators of the cumulative hazard and distribution functions of a random variable based on truncated data.

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Further Applications of Decoupling,hows that, in certain aspects, .-statistics behave more like sums of independent random variables than like martingales (in other aspects like, e.g., regarding the law of large numbers, converse part--see Section 4.1—, .-statistics do not behave at all as sums of independent variables).

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Probability and Its Applicationshttp://image.papertrans.cn/d/image/264500.jpg

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https://doi.org/10.1007/978-3-658-20520-1y stopped processes with independent increments. Concerning randomly stopped sums of independent random variables, the key result behind the theory of sequential analysis is Wald’s first equation. In this chapter we discuss this result and provide (in some sense) the weakest conditions under which i

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