scholarly journals SOME ASSOCIATIVITY RESULTS FOR RANDOM VARIABLES IN STRANGE GROUPS

2019 ◽  
Author(s):  
Anil Kumar Bheemaiah

Abstract. Let ̃z be a bare-foot dependent path. Recently, there has been muchinterest in the derivation of isomorphisms on strange groups that come inthe way of ~z. Let there exist a countable infinity of such groups, [[G]]. Weshow that [[G]] has one or more isomorphisms to a Dojo D.Keywords; Bare-foot paths, Measure Theory, Higher Operators, StrangeGroups, Techno Gibberish, model theory, Dojo Theory

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 134
Author(s):  
Tomer Shushi

In risk theory, risks are often modeled by risk measures which allow quantifying the risks and estimating their possible outcomes. Risk measures rely on measure theory, where the risks are assumed to be random variables with some distribution function. In this work, we derive a novel topological-based representation of risks. Using this representation, we show the differences between diversifiable and non-diversifiable. We show that topological risks should be modeled using two quantities, the risk measure that quantifies the predicted amount of risk, and a distance metric which quantifies the uncertainty of the risk.


1986 ◽  
Vol 51 (4) ◽  
pp. 869-882 ◽  
Author(s):  
Robert L. Vaught

We will consider Tarski's work in pure model theory and classical logic. His work in applied model theory—the model theory of various special theories—is discussed by Doner and van den Dries [1987], and McNulty [1986]. (However, the separation of “pure” and “applied” only becomes natural as the subjects mature; so we shall discuss applied model theory at least to some extent in Tarski's earlier work.)Alfred Tarski (1901–1983) was awarded a Ph.D. in mathematics at Warsaw University in 1924. His teachers included the two leaders of the renowned Polish logic school, the logician-philosophers L. Leśniewski and J. Łukasiewicz. (Very soon Tarski was recognized as the third leader of the school.) Another teacher was the philosopher T. Kotarbiński, to whom Tarski dedicated his collected papers [56m]. Leśniewski was Tarski's thesis advisor; he transmitted to Tarski his interests in the metalanguage and in the theory of definition. Tarski's thesis ([23a], [24]) was about protothetic—the sentential calculus augmented by quantifiable variables ranging over truth functions. Its main result was that all the connectives are definable using only ↔ and ∀. By the same year, 1924, Tarski also had begun his prolific writings in set theory, and had discovered together with S. Banach, the leader of the Polish mathematicians, their famous “paradox” [24d] in measure theory. (For details see Lévy [1987].)In 1927-29 Tarski held a seminar at Warsaw University on results he obtained in 1926-28. The seminar lay at the heart of what is now called model theory. The brief history of model theory up to that time had begun with the paper of L. Löwenheim [1915].


1986 ◽  
Vol 23 (04) ◽  
pp. 1013-1018
Author(s):  
B. G. Quinn ◽  
H. L. MacGillivray

Sufficient conditions are presented for the limiting normality of sequences of discrete random variables possessing unimodal distributions. The conditions are applied to obtain normal approximations directly for the hypergeometric distribution and the stationary distribution of a special birth-death process.


1985 ◽  
Vol 24 (03) ◽  
pp. 120-130 ◽  
Author(s):  
E. Brunner ◽  
N. Neumann

SummaryThe mathematical basis of Zelen’s suggestion [4] of pre randomizing patients in a clinical trial and then asking them for their consent is investigated. The first problem is to estimate the therapy and selection effects. In the simple prerandomized design (PRD) this is possible without any problems. Similar observations have been made by Anbar [1] and McHugh [3]. However, for the double PRD additional assumptions are needed in order to render therapy and selection effects estimable. The second problem is to determine the distribution of the statistics. It has to be taken into consideration that the sample sizes are random variables in the PRDs. This is why the distribution of the statistics can only be determined asymptotically, even under the assumption of normal distribution. The behaviour of the statistics for small samples is investigated by means of simulations, where the statistics considered in the present paper are compared with the statistics suggested by Ihm [2]. It turns out that the statistics suggested in [2] may lead to anticonservative decisions, whereas the “canonical statistics” suggested by Zelen [4] and considered in the present paper keep the level quite well or may lead to slightly conservative decisions, if there are considerable selection effects.


2020 ◽  
pp. 9-13
Author(s):  
A. V. Lapko ◽  
V. A. Lapko

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.


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