Monte Carlo simulations of magnetization state of ellipsoidal CoCu particles in disordered self-assembled arrays

2016 ◽  
Vol 31 (14) ◽  
pp. 2058-2064 ◽  
Author(s):  
V.Z.C. Paes ◽  
J. Varalda ◽  
P. Schio ◽  
J.T. Matsushima ◽  
E.C. Pereira ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Magnetization State ◽  
Self Assembled ◽  
Image Position

Abstract

Monte Carlo Simulations of Self-Assembled Surfactant Aggregates

Langmuir ◽  
2006 ◽  
Vol 22 (6) ◽  
pp. 2523-2527 ◽  
Author(s):  
T. Zehl ◽  
M. Wahab ◽  
H.-J. Mögel ◽  
P. Schiller
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Surfactant Aggregates ◽  
Self Assembled

Kinetic Monte Carlo simulations of three-dimensional self-assembled quantum dot islands

2014 ◽  
Vol 23 (1) ◽  
pp. 016802
Author(s):  
Xin Song ◽  
Hao Feng ◽  
Yu-Min Liu ◽  
Zhong-Yuan Yu ◽  
Hao-Zhi Yin
Keyword(s):  
Monte Carlo ◽  
Quantum Dot ◽  
Monte Carlo Simulations ◽  
Kinetic Monte Carlo ◽  
Three Dimensional ◽  
Kinetic Monte Carlo Simulations ◽  
Self Assembled

scholarly journals Critical behavior of self-assembled rigid rods on two-dimensional lattices: Bethe-Peierls approximation and Monte Carlo simulations

2013 ◽  
Vol 138 (23) ◽  
pp. 234706 ◽  
Author(s):  
L. G. López ◽  
D. H. Linares ◽  
A. J. Ramirez-Pastor ◽  
D. A. Stariolo ◽  
S. A. Cannas
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Critical Behavior ◽  
Two Dimensional ◽  
Rigid Rods ◽  
Self Assembled

scholarly journals Phase diagram of self-assembled rigid rods on two-dimensional lattices: Theory and Monte Carlo simulations

2010 ◽  
Vol 133 (13) ◽  
pp. 134706 ◽  
Author(s):  
L. G. López ◽  
D. H. Linares ◽  
A. J. Ramirez-Pastor ◽  
S. A. Cannas
Keyword(s):  
Phase Diagram ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Two Dimensional ◽  
Rigid Rods ◽  
Self Assembled

scholarly journals STRUCTURAL CHANGE IN NONSTATIONARY AR(1) MODELS

2017 ◽  
Vol 34 (5) ◽  
pp. 985-1017 ◽  
Author(s):  
Tianxiao Pang ◽  
Terence Tai-Leung Chong ◽  
Danna Zhang ◽  
Yanling Liang
Keyword(s):  
Monte Carlo ◽  
Structural Change ◽  
Monte Carlo Simulations ◽  
Change Point ◽  
Indicator Function ◽  
The Other ◽  
Finite Sample ◽  
Mild Conditions ◽  
Finite Sample Properties ◽  
Image Position

This article revisits the asymptotic inference for nonstationary AR(1) models of Phillips and Magdalinos (2007a) by incorporating a structural change in the AR parameter at an unknown time k0. Consider the model ${y_t} = {\beta _1}{y_{t - 1}}I\{ t \le {k_0}\} + {\beta _2}{y_{t - 1}}I\{ t > {k_0}\} + {\varepsilon _t},t = 1,2, \ldots ,T$, where I{·} denotes the indicator function, one of ${\beta _1}$ and ${\beta _2}$ depends on the sample size T, and the other is equal to one. We examine four cases: Case (I): ${\beta _1} = {\beta _{1T}} = 1 - c/{k_T}$, ${\beta _2} = 1$; (II): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 - c/{k_T}$; (III): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 + c/{k_T}$; and case (IV): ${\beta _1} = {\beta _{1T}} = 1 + c/{k_T}$, ${\beta _2} = 1$, where c is a fixed positive constant, and kT is a sequence of positive constants increasing to ∞ such that kT = o(T). We derive the limiting distributions of the t-ratios of ${\beta _1}$ and ${\beta _2}$ and the least squares estimator of the change point for the cases above under some mild conditions. Monte Carlo simulations are conducted to examine the finite-sample properties of the estimators. Our theoretical findings are supported by the Monte Carlo simulations.

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