scholarly journals Exact moderate and large deviations for linear random fields

2018 ◽  
Vol 55 (2) ◽  
pp. 431-449 ◽  
Author(s):  
Hailin Sang ◽  
Yimin Xiao

Abstract By extending the methods of Peligrad et al. (2014), we establish exact moderate and large deviation asymptotics for linear random fields with independent innovations. These results are useful for studying nonparametric regression with random field errors and strong limit theorems.

2019 ◽  
Vol 56 (01) ◽  
pp. 223-245 ◽  
Author(s):  
Aleksandr Beknazaryan ◽  
Hailin Sang ◽  
Yimin Xiao

AbstractWe study the Cramér type moderate deviation for partial sums of random fields by applying the conjugate method. The results are applicable to the partial sums of linear random fields with short or long memory and to nonparametric regression with random field errors.


2016 ◽  
Vol 16 (05) ◽  
pp. 1650017 ◽  
Author(s):  
Mohamed El Machkouri ◽  
Davide Giraudo

We provide a new projective condition for a stationary real random field indexed by the lattice [Formula: see text] to be well approximated by an orthomartingale in the sense of Cairoli (1969). Our main result can be viewed as a multidimensional version of the martingale-coboundary decomposition method which the idea goes back to Gordin (1969). It is a powerful tool for proving limit theorems or large deviations inequalities for stationary random fields when the corresponding result is valid for orthomartingales.


1989 ◽  
Vol 21 (03) ◽  
pp. 491-512
Author(s):  
B. Gail Ivanoff

We consider a multitype branching random walk with independent Poisson random fields of each type of particle initially. The existence of limiting random fields as the generation number, is studied, when the intensity of the initial field is renormalized in such a way that the mean measures converge. Spatial laws of large numbers and central limit theorems are given for the limiting random field, when it is non-trivial.


2008 ◽  
Vol 49 (4) ◽  
pp. 533-541 ◽  
Author(s):  
MI-HWA KO ◽  
HYUN-CHULL KIM ◽  
TAE-SUNG KIM

AbstractFor a linear random field (linear p-parameter stochastic process) generated by a dependent random field with zero mean and finite qth moments (q>2p), we give sufficient conditions that the linear random field converges weakly to a multiparameter standard Brownian motion if the corresponding dependent random field does so.


2013 ◽  
Vol 23 (2) ◽  
pp. 161-200 ◽  
Author(s):  
MAHSHID ATAPOUR ◽  
NEAL MADRAS

For a fixed permutation τ, let$\mathcal{S}_N(\tau)$be the set of permutations onNelements that avoid the pattern τ. Madras and Liu (2010) conjectured that$\lim_{N\rightarrow\infty}\frac{|\mathcal{S}_{N+1}(\tau)|}{ |\mathcal{S}_N(\tau)|}$exists; if it does, it must equal the Stanley–Wilf limit. We prove the conjecture for every permutation τ of length 5 or less, as well as for some longer cases (including 704 of the 720 permutations of length 6). We also consider permutations drawn at random from$\mathcal{S}_N(\tau)$, and we investigate properties of their graphs (viewing permutations as functions on {1,. . .,N}) scaled down to the unit square [0,1]2. We prove exact large deviation results for these graphs when τ has length 3; it follows, for example, that it is exponentially unlikely for a random 312-avoiding permutation to have points above the diagonal strip |y−x| < ε, but not unlikely to have points below the strip. For general τ, we show that some neighbourhood of the upper left corner of [0,1]2is exponentially unlikely to contain a point of the graph if and only if τ starts with its largest element. For patterns such as τ=4231 we establish that this neighbourhood can be extended along the sides of [0,1]2to come arbitrarily close to the corner points (0,0) and (1,1), as simulations had suggested.


1989 ◽  
Vol 21 (3) ◽  
pp. 491-512 ◽  
Author(s):  
B. Gail Ivanoff

We consider a multitype branching random walk with independent Poisson random fields of each type of particle initially. The existence of limiting random fields as the generation number, is studied, when the intensity of the initial field is renormalized in such a way that the mean measures converge. Spatial laws of large numbers and central limit theorems are given for the limiting random field, when it is non-trivial.


1987 ◽  
Vol 19 (01) ◽  
pp. 106-122 ◽  
Author(s):  
Simeon M. Berman

Let Xt , be a Markov random field assuming values in RM. Let In be a rectangular box in Zm with its center at 0 and corner points with coordinates ±n. Let (An ) be a sequence of measurable subsets of RM such that neighborhood of t) → 0, for n → ∞; and let fn (x) be the indicator of An. Under appropriate conditions on the nearest-neighbor distributions of (Xt ), the conditional distribution of given the values of Xs , for s on the boundary of In , converges to the Poisson distribution. An immediate application is an extreme value limit theorem for a real-valued Markov random field.


1987 ◽  
Vol 19 (1) ◽  
pp. 106-122 ◽  
Author(s):  
Simeon M. Berman

Let Xt, be a Markov random field assuming values in RM. Let In be a rectangular box in Zm with its center at 0 and corner points with coordinates ±n. Let (An) be a sequence of measurable subsets of RM such that neighborhood of t) → 0, for n → ∞; and let fn(x) be the indicator of An. Under appropriate conditions on the nearest-neighbor distributions of (Xt), the conditional distribution of given the values of Xs, for s on the boundary of In, converges to the Poisson distribution. An immediate application is an extreme value limit theorem for a real-valued Markov random field.


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