7,218 research outputs found
Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation
In this paper, some vital methodological issues of spatial microsimulation modelling for small area estimation have been addressed, with a particular emphasis given to the reweighting techniques. Most of the review articles in small area estimation have highlighted methodologies based on various statistical models and theories. However, spatial microsimulation modelling is emerging as a very useful alternative means of small area estimation. Our findings demonstrate that spatial microsimulation models are robust and have advantages over other type of models used for small area estimation. The technique uses different methodologies typically based on geographic models and various economic theories. In contrast to statistical model-based approaches, the spatial microsimulation model-based approaches can operate through reweighting techniques such as GREGWT and combinatorial optimization. A comparison between reweighting techniques reveals that they are using quite different iterative algorithms and that their properties also vary. The study also points out a new method for spatial microsimulation modellingBayesian prediction approach; combinatorial optimisation; GREGWT; microdata; small area estimation; spatial microsimulation
Statistical unfolding of elementary particle spectra: Empirical Bayes estimation and bias-corrected uncertainty quantification
We consider the high energy physics unfolding problem where the goal is to
estimate the spectrum of elementary particles given observations distorted by
the limited resolution of a particle detector. This important statistical
inverse problem arising in data analysis at the Large Hadron Collider at CERN
consists in estimating the intensity function of an indirectly observed Poisson
point process. Unfolding typically proceeds in two steps: one first produces a
regularized point estimate of the unknown intensity and then uses the
variability of this estimator to form frequentist confidence intervals that
quantify the uncertainty of the solution. In this paper, we propose forming the
point estimate using empirical Bayes estimation which enables a data-driven
choice of the regularization strength through marginal maximum likelihood
estimation. Observing that neither Bayesian credible intervals nor standard
bootstrap confidence intervals succeed in achieving good frequentist coverage
in this problem due to the inherent bias of the regularized point estimate, we
introduce an iteratively bias-corrected bootstrap technique for constructing
improved confidence intervals. We show using simulations that this enables us
to achieve nearly nominal frequentist coverage with only a modest increase in
interval length. The proposed methodology is applied to unfolding the boson
invariant mass spectrum as measured in the CMS experiment at the Large Hadron
Collider.Comment: Published at http://dx.doi.org/10.1214/15-AOAS857 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org). arXiv admin note:
substantial text overlap with arXiv:1401.827
On nonparametric estimation of a mixing density via the predictive recursion algorithm
Nonparametric estimation of a mixing density based on observations from the
corresponding mixture is a challenging statistical problem. This paper surveys
the literature on a fast, recursive estimator based on the predictive recursion
algorithm. After introducing the algorithm and giving a few examples, I
summarize the available asymptotic convergence theory, describe an important
semiparametric extension, and highlight two interesting applications. I
conclude with a discussion of several recent developments in this area and some
open problems.Comment: 22 pages, 5 figures. Comments welcome at
https://www.researchers.one/article/2018-12-
Estimating effective connectivity in linear brain network models
Contemporary neuroscience has embraced network science to study the complex
and self-organized structure of the human brain; one of the main outstanding
issues is that of inferring from measure data, chiefly functional Magnetic
Resonance Imaging (fMRI), the so-called effective connectivity in brain
networks, that is the existing interactions among neuronal populations. This
inverse problem is complicated by the fact that the BOLD (Blood Oxygenation
Level Dependent) signal measured by fMRI represent a dynamic and nonlinear
transformation (the hemodynamic response) of neuronal activity. In this paper,
we consider resting state (rs) fMRI data; building upon a linear population
model of the BOLD signal and a stochastic linear DCM model, the model
parameters are estimated through an EM-type iterative procedure, which
alternately estimates the neuronal activity by means of the Rauch-Tung-Striebel
(RTS) smoother, updates the connections among neuronal states and refines the
parameters of the hemodynamic model; sparsity in the interconnection structure
is favoured using an iteratively reweighting scheme. Experimental results using
rs-fMRI data are shown demonstrating the effectiveness of our approach and
comparison with state of the art routines (SPM12 toolbox) is provided
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