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In this paper, the multi-state survival signature is first redefined for multi-state coherent or mixed systems with independent and identically distributed (i.i.d.) multi-state components. With the assumption of independence of component lifetimes at different state levels, transformation formulas of multi-state survival signatures of different sizes are established through the use of equivalent systems and a generalized triangle rule for order statistics from several independent and non-identical distributions. The results obtained facilitate stochastic comparisons of multi-state coherent or mixed systems with different numbers of i.i.d. multi-state components. Specific examples are finally presented to illustrate the transformation formulas established here, and also their use in comparing systems of different sizes.
A hypergraph $\mathcal{F}$ is non-trivial intersecting if every pair of edges in it have a nonempty intersection, but no vertex is contained in all edges of $\mathcal{F}$. Mubayi and Verstraëte showed that for every $k \ge d+1 \ge 3$ and $n \ge (d+1)k/d$ every $k$-graph $\mathcal{H}$ on $n$ vertices without a non-trivial intersecting subgraph of size $d+1$ contains at most $\binom{n-1}{k-1}$ edges. They conjectured that the same conclusion holds for all $d \ge k \ge 4$ and sufficiently large $n$. We confirm their conjecture by proving a stronger statement.
They also conjectured that for $m \ge 4$ and sufficiently large $n$ the maximum size of a $3$-graph on $n$ vertices without a non-trivial intersecting subgraph of size $3m+1$ is achieved by certain Steiner triple systems. We give a construction with more edges showing that their conjecture is not true in general.
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
An introduction to the emergence of heavy-tailed distributions in the context of extremal processes.Max-stable distributions are introduced, and the extremal central limit theory is presented.Further, an example of the emergence of heavy tails in the extremes of random walks is presented.
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
An introduction to the class of heavy-tailed distributions, including formal definitions, basic properties, and examples of common distributions that are heavy-tailed.
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
An introduction to the class of regularly varying distributions and the important properties of this class, including scale invariance. Examples of applying regularly varying distributions to branching processes are included.
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
An introduction to the emergence of heavy-tailed distributions in the context of multiplicative processes.The multiplicative central limit theorem is presented, and variations of multiplicative processes with lower barriers and noise are studied.Further, an example of the emergence of heavy tails in random graphs via preferential attachment is included.
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
Extremal approaches for semi-parametric estimation of power-law tails are presented, including the Hill estimator, the moments estimate, the Pickands estimator, and Peaks over threshold.Further, approaches for estimating where the tail begins are presented, including PLFIT and the double bootstrap method.
We study several parameters of a random Bienaymé–Galton–Watson tree $T_n$ of size $n$ defined in terms of an offspring distribution $\xi$ with mean $1$ and nonzero finite variance $\sigma ^2$. Let $f(s)=\mathbb{E}\{s^\xi \}$ be the generating function of the random variable $\xi$. We show that the independence number is in probability asymptotic to $qn$, where $q$ is the unique solution to $q = f(1-q)$. One of the many algorithms for finding the largest independent set of nodes uses a notion of repeated peeling away of all leaves and their parents. The number of rounds of peeling is shown to be in probability asymptotic to $\log n/\log (1/f'(1-q))$. Finally, we study a related parameter which we call the leaf-height. Also sometimes called the protection number, this is the maximal shortest path length between any node and a leaf in its subtree. If $p_1 = \mathbb{P}\{\xi =1\}\gt 0$, then we show that the maximum leaf-height over all nodes in $T_n$ is in probability asymptotic to $\log n/\log (1/p_1)$. If $p_1 = 0$ and $\kappa$ is the first integer $i\gt 1$ with $\mathbb{P}\{\xi =i\}\gt 0$, then the leaf-height is in probability asymptotic to $\log _\kappa \log n$.
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
An introduction to the class of long-tailed distributions and the important properties of this class, including properties of the hazard rate and the residual life distribution. Examples applying long-tailed distributions to random extrema are included.
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam
Classical approaches for parametric estimation of power-laws distributions are presented, including (weighted) linear regression and maximum likelihood estimation.
Jayakrishnan Nair, Indian Institute of Technology, Bombay,Adam Wierman, California Institute of Technology,Bert Zwart, Stichting Centrum voor Wiskunde en Informatica (CWI), Amsterdam