Products of distribution functions attracted to extreme value laws

1971 ◽  
Vol 8 (04) ◽  
pp. 781-793 ◽  
Author(s):  
Sidney I. Resnick
Keyword(s):  
Equivalence Relation ◽  
Complete Convergence ◽  
Convex Sets ◽  
Domain Of Attraction ◽  
Extreme Value ◽  
Distribution Functions ◽  
Image Position

When is the product of the d.f.'s H 1(·), ···, Hm (·) attracted to an extreme value law φ(x)? We associate with each Hi (·) its A-function Hi (x) is attracted to φ(x) if each Hi (x) is in the domain of attraction of φ(x) and Ai (z) ~ Aj (z), 1 ≦ i, j ≦ m. Equivalence of A-functions determines an equivalence relation which partitions the domain of attraction of φ(x)into one or more convex sets. These sets fail to be closed under passages to the limit (complete convergence).

Products of distribution functions attracted to extreme value laws

1971 ◽  
Vol 8 (4) ◽  
pp. 781-793 ◽  
Author(s):  
Sidney I. Resnick
Keyword(s):  
Equivalence Relation ◽  
Complete Convergence ◽  
Convex Sets ◽  
Domain Of Attraction ◽  
Extreme Value ◽  
Distribution Functions ◽  
Image Position

When is the product of the d.f.'s H1(·), ···, Hm(·) attracted to an extreme value law φ(x)? We associate with each Hi(·) its A-function Hi(x) is attracted to φ(x) if each Hi(x) is in the domain of attraction of φ(x) and Ai(z) ~ Aj(z), 1 ≦ i, j ≦ m. Equivalence of A-functions determines an equivalence relation which partitions the domain of attraction of φ(x)into one or more convex sets. These sets fail to be closed under passages to the limit (complete convergence).

Tail equivalence and its applications

1971 ◽  
Vol 8 (01) ◽  
pp. 136-156 ◽  
Author(s):  
Sidney I. Resnick
Keyword(s):  
Markov Chain ◽  
Random Variables ◽  
Domain Of Attraction ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
Normalizing Constants ◽  
Independent Identically Distributed

If for two c.d.f.'s F(·) and G(·), 1 – F(x)/1 – G(x) → A, 0 <A <∞ , as x → ∞, then for normalizing constants an > 0, bn, n > 1, Fn (anx + bn ) → φ(x), φ(x) non-degenerate, iff Gn (anx + bn )→ φ A−1(x). Conversely, if Fn (anx+bn )→ φ(x), Gn (anx + bn ) → φ'(x), φ(x) and φ'(x) non-degenerate, then there exist constants C >0 and D such that φ'(x) =φ(Cx + D) and limx→∞ 1 — F(x)/1 — G(x) exists and is expressed in terms of C and D, depending on which type of extreme value distribution φ(x) is. These results are used to study domain of attraction questions for products of distribution functions and to reduce the limit law problem for maxima of a sequence of random variables defined on a Markov chain (M.C.) to the independent, identically distributed (i.i.d.) case.

Tail equivalence and its applications

1971 ◽  
Vol 8 (1) ◽  
pp. 136-156 ◽  
Author(s):  
Sidney I. Resnick
Keyword(s):  
Markov Chain ◽  
Random Variables ◽  
Domain Of Attraction ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
Normalizing Constants ◽  
Independent Identically Distributed

If for two c.d.f.'s F(·) and G(·), 1 – F(x)/1 – G(x) → A, 0 <A <∞, as x → ∞, then for normalizing constants an > 0, bn, n > 1, Fn(anx + bn) → φ(x), φ(x) non-degenerate, iff Gn(anx + bn)→ φ A−1(x). Conversely, if Fn(anx+bn)→ φ(x), Gn(anx + bn) → φ'(x), φ(x) and φ'(x) non-degenerate, then there exist constants C >0 and D such that φ'(x) =φ(Cx + D) and limx→∞ 1 — F(x)/1 — G(x) exists and is expressed in terms of C and D, depending on which type of extreme value distribution φ(x) is. These results are used to study domain of attraction questions for products of distribution functions and to reduce the limit law problem for maxima of a sequence of random variables defined on a Markov chain (M.C.) to the independent, identically distributed (i.i.d.) case.

scholarly journals Asymptotic Laws for Upper and Strong Record Values in the Extreme Domain of Attraction and Beyond

2021 ◽  
Vol 14 (1) ◽  
pp. 19-42
Author(s):  
Gane Samb Lo ◽  
Mohammad Ahsanullah ◽  
Moumouni Diallo ◽  
Modou Ngom
Keyword(s):  
Domain Of Attraction ◽  
Rates Of Convergence ◽  
Record Values ◽  
Extreme Value ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Cumulative Distribution Functions ◽  
Functional Representations

Asymptotic laws of records values have usually been investigated as limits in type. In this paper, we use functional representations of the tail of cumulative distribution functions in the extreme value domain of attraction to directly establish asymptotic laws of records value, not necessarily as limits in type and their rates of convergences. Results beyond the extreme value value domain are provided. Explicit asymptotic laws concerning very usual laws and related rates of convergence are listed as well. Some of these laws are expected to be used in fitting distribution.

scholarly journals An elementary renewal theorem for random compact convex sets

1995 ◽  
Vol 27 (4) ◽  
pp. 931-942 ◽  
Author(s):  
Ilya S. Molchanov ◽  
Edward Omey ◽  
Eugene Kozarovitzky
Keyword(s):  
Support Function ◽  
Convex Sets ◽  
Compact Convex ◽  
Renewal Function ◽  
Renewal Theorem ◽  
Closed Sets ◽  
Random Closed Sets ◽  
Minkowski Sums ◽  
Image Position ◽  
Unit Vectors

A set-valued analog of the elementary renewal theorem for Minkowski sums of random closed sets is considered. The corresponding renewal function is defined as where are Minkowski (element-wise) sums of i.i.d. random compact convex sets. In this paper we determine the limit of H(tK)/t as t tends to infinity. For K containing the origin as an interior point, where hK(u) is the support function of K and is the set of all unit vectors u with EhA(u) > 0. Other set-valued generalizations of the renewal function are also suggested.

DIscrepancy of Gear Tooth Allowable Bending Stress Using the Extreme Value Distribution From Different Test Methods

1991 ◽  
Author(s):  
Chienann A. Hou ◽  
Shijun Ma
Keyword(s):  
Gear Tooth ◽  
Extreme Value Distribution ◽  
Test Methods ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
Test Method ◽  
Bending Stress ◽  
Gear Design ◽  
Experimental Approaches

Abstract The allowable bending stress Se of a gear tooth is one of the basic factors in gear design. It can be determined by either the pulsating test or the gear-running test. However, some differences exist between the allowable bending stress Se obtained from these different test methods. In this paper, the probability distribution functions corresponding to each test method are analyzed and the expressions for the minimum extreme value distribution are presented. By using numerical integration, Se values from the population of the same tested gear tooth are obtained. Based on this investigation it is shown that the differences in Se obtained from the different test methods are significant. A proposed correction factor associated with the different experimental approaches is also presented.

scholarly journals A Double-indexed Functional Hill Process and Applications

2016 ◽  
Vol 8 (4) ◽  
pp. 144
Author(s):  
Modou Ngom ◽  
Gane Samb Lo
Keyword(s):  
Stochastic Process ◽  
Distribution Function ◽  
Asymptotic Behaviour ◽  
Stochastic Processes ◽  
Order Statistics ◽  
Finite Dimension ◽  
Domain Of Attraction ◽  
Extreme Value ◽  
Extreme Value Index

<div>Let $X_{1,n} \leq .... \leq X_{n,n}$ be the order statistics associated with a sample $X_{1}, ...., X_{n}$ whose pertaining distribution function (\textit{df}) is $F$. We are concerned with the functional asymptotic behaviour of the sequence of stochastic processes</div><div> </div><div>\begin{equation}<br />T_{n}(f,s)=\sum_{j=1}^{j=k}f(j)\left( \log X_{n-j+1,n}-\log<br />X_{n-j,n}\right)^{s} ,  \label{fme}<br />\end{equation}</div><div> </div><div>indexed by some classes $\mathcal{F}$ of functions $f:\mathbb{N}%^{\ast}\longmapsto \mathbb{R}_{+}$ and $s \in ]0,+\infty[$ and where $k=k(n)$ satisfies</div><div> </div><div>\begin{equation*}<br />1\leq k\leq n,k/n\rightarrow 0\text{ as }n\rightarrow \infty .<br />\end{equation*}</div><div> </div><div>We show that this is a stochastic process whose margins generate estimators of the extreme value index when $F$ is in the extreme domain of attraction. We focus in this paper on its finite-dimension asymptotic law and provide a class of new estimators of the extreme value index whose performances are compared to analogous ones. The results are next particularized for one explicit class $\mathcal{F}$.</div>

Some Functional Stable Limit Theorems

1973 ◽  
Vol 16 (2) ◽  
pp. 173-177 ◽  
Author(s):  
D. R. Beuerman
Keyword(s):  
Weak Convergence ◽  
Limit Theorems ◽  
Stable Process ◽  
Random Variables ◽  
Domain Of Attraction ◽  
Stable Processes ◽  
Stable Limit ◽  
Stable Law ◽  
Partial Sum Process ◽  
Image Position

Let Xl,X2,X3, … be a sequence of independent and identically distributed (i.i.d.) random variables which belong to the domain of attraction of a stable law of index α≠1. That is,1whereandwhere L(n) is a function of slow variation; also take S0=0, B0=l.In §2, we are concerned with the weak convergence of the partial sum process to a stable process and the question of centering for stable laws and drift for stable processes.

A Note on Coverings of En by Convex Sets

1967 ◽  
Vol 10 (5) ◽  
pp. 669-673 ◽  
Author(s):  
J.H.H. Chalk
Keyword(s):  
Convex Sets ◽  
Independent Set ◽  
Linearly Independent ◽  
Image Position ◽  
Take All

Let (x1, x2, …, xn) denote the coordinates of a point of Euclidean n-space En. Let be a set of n+1 points of En with the property thatform a linearly independent set and define a lattice Λ of pointsby allowing u1, …, un to take all integer values.

Fluctuation Microscopy in the STEM

2001 ◽  
Vol 7 (S2) ◽  
pp. 226-227
Author(s):  
P. M. Voyles ◽  
D. A. Muller
Keyword(s):  
Random Network ◽  
Dark Field ◽  
Distribution Functions ◽  
Disordered Materials ◽  
Microscopy Technique ◽  
Medium Range ◽  
Fluctuation Microscopy ◽  
Image Position ◽  
Continuous Random Network ◽  
Vector Magnitude

Fluctuation microscopy is an electron microscopy technique sensitive to medium-range order (MRO) in disordered materials. It has been applied to study amorphous germanium and silicon, leading to the conclusion that these materials exhibit more MRO than the conventional continuous random network model for their structure.As originally proposed by Treacy and Gibson, fluctuation microscopy utilizes mesoscopicresolution (1.5 nm) hollow-cone dark field (HCDF) imaging in a TEM. The normalized variance of such images,is a measure of the magnitude of fluctuations in the diffracted intensity from mesoscopic volumes of the sample and is sensitive to MRO via the three- and four-body atom distribution functions. Studying V as a function of the diffraction vector magnitude k gives information about the degree of MRO and the internal structure of ordered regions. V as a function of the inverse resolution Q gives information about the characteristic MRO length scale.

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