1,741 research outputs found
Migration in a Small World: A Network Approach to Modeling Immigration Processes
Existing theories of migration either focus on micro- or macroscopic behavior
of populations; that is, either the average behavior of entire population is
modeled directly, or decisions of individuals are modeled directly. In this
work, we seek to bridge these two perspectives by modeling individual agents
decisions to migrate while accounting for the social network structure that
binds individuals into a population. Pecuniary considerations combined with the
decisions of peers are the primary elements of the model, being the main
driving forces of migration. People of the home country are modeled as nodes on
a small-world network. A dichotomous state is associated with each node,
indicating whether it emigrates to the destination country or it stays in the
home country. We characterize the emigration rate in terms of the relative
welfare and population of the home and destination countries. The time
evolution and the steady-state fraction of emigrants are also derived
Comparisons of Estimation Procedures for Nonlinear Multilevel Models
We introduce General Multilevel Models and discuss the estimation procedures that may be used to fit multilevel models. We apply the proposed procedures to three-level binary data generated in a simulation study. We compare the procedures by two criteria, Bias and efficiency. We find that the estimates of the fixed effects and variance components are substantially and significantly biased using Longford's Approximation and Goldstein's Generalized Least Squares approaches by two software packages VARCL and ML3. These estimates are not significantly biased and are very close to real values when we use Markov Chain Monte Carlo (MCMC) using Gibbs sampling or Nonparametric Maximum Likelihood (NPML) approach. The Gaussian Quadrature (GQ) approach, even with small number of mass points results in consistent estimates but computationally problematic. We conclude that the MCMC and the NPML approaches are the recommended procedures to fit multilevel models.
Degree Correlation in Scale-Free Graphs
We obtain closed form expressions for the expected conditional degree
distribution and the joint degree distribution of the linear preferential
attachment model for network growth in the steady state. We consider the
multiple-destination preferential attachment growth model, where incoming nodes
at each timestep attach to existing nodes, selected by
degree-proportional probabilities. By the conditional degree distribution
, we mean the degree distribution of nodes that are connected to a
node of degree . By the joint degree distribution , we mean the
proportion of links that connect nodes of degrees and . In addition
to this growth model, we consider the shifted-linear preferential growth model
and solve for the same quantities, as well as a closed form expression for its
steady-state degree distribution
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