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Acta Applicandae Mathematica, 1999
The aim of this paper is to show how the classical results for sums of independent terms can be applied for ordered random variables. There are discussed methods that enable to express the distributions of ordered random variables (i.e. classical order statistics, induced order statistics, record times and record values, and generalized order ...
Nevzorova, Ludmila, Nevzorov, Valery
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The aim of this paper is to show how the classical results for sums of independent terms can be applied for ordered random variables. There are discussed methods that enable to express the distributions of ordered random variables (i.e. classical order statistics, induced order statistics, record times and record values, and generalized order ...
Nevzorova, Ludmila, Nevzorov, Valery
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1976
In some parts of statistical inference it is customary to speak entirely in random variable terms, as in the analysis of variance. One considers his job finished when he writes the ratio of two independently distributed chi-square random variables, the denominator a central chi-square.
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In some parts of statistical inference it is customary to speak entirely in random variable terms, as in the analysis of variance. One considers his job finished when he writes the ratio of two independently distributed chi-square random variables, the denominator a central chi-square.
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Random Variables: Distributions
1999We will now consider not the probability of observing particular events but rather the events themselves and try to find a particularly simple way of classifying them. We can, for instance, associate the event “heads” with the number 0 and the event “tails” with the number 1.
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Noble-Metal Based Random Alloy and Intermetallic Nanocrystals: Syntheses and Applications
Chemical Reviews, 2021Ming Zhou, Can Li, Jiye Fang
exaly
1994
Abstract Specializing the concepts of Chapter 7 to the case of real variables, this chapter introduces distribution functions, discrete and continuous distributions, and describes examples such as the binomial, uniform, Gaussian, Cauchy, and gamma distributions. It then treats multivariate distributions and the concept of independence.
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Abstract Specializing the concepts of Chapter 7 to the case of real variables, this chapter introduces distribution functions, discrete and continuous distributions, and describes examples such as the binomial, uniform, Gaussian, Cauchy, and gamma distributions. It then treats multivariate distributions and the concept of independence.
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After the great progress in the theory of probability as a science, the passage towards more flexible and representative definitions and the possibility to be implemented numerically gave the need for a new formulation. From there, the random variable came into existence.
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