764 research outputs found
Fast spatial inference in the homogeneous Ising model
The Ising model is important in statistical modeling and inference in many
applications, however its normalizing constant, mean number of active vertices
and mean spin interaction are intractable. We provide accurate approximations
that make it possible to calculate these quantities numerically. Simulation
studies indicate good performance when compared to Markov Chain Monte Carlo
methods and at a tiny fraction of the time. The methodology is also used to
perform Bayesian inference in a functional Magnetic Resonance Imaging
activation detection experiment.Comment: 18 pages, 1 figure, 3 table
Beta Linear Failure Rate Geometric Distribution with Applications
This paper introduces the beta linear failure rate geometric (BLFRG) distribution, which contains a number of distributions including the exponentiated linear failure rate geometric, linear failure rate geometric, linear failure rate, exponential geometric, Rayleigh geometric, Rayleigh and exponential distributions as special cases. The model further generalizes the linear failure rate distribution. A comprehensive investigation of the model properties including moments, conditional moments, deviations, Lorenz and Bonferroni curves and entropy are presented. Estimates of model parameters are given. Real data examples are presented to illustrate the usefulness and applicability of the distribution
Judging a book by its cover: how much of REF `research quality' is really `journal prestige'?
The Research Excellence Framework (REF) is a periodic UK-wide assessment of
the quality of published research in universities. The most recent REF was in
2014, and the next will be in 2021. The published results of REF2014 include a
categorical `quality profile' for each unit of assessment (typically a
university department), reporting what percentage of the unit's REF-submitted
research outputs were assessed as being at each of four quality levels
(labelled 4*, 3*, 2* and 1*). Also in the public domain are the original
submissions made to REF2014, which include -- for each unit of assessment --
publication details of the REF-submitted research outputs.
In this work, we address the question: to what extent can a REF quality
profile for research outputs be attributed to the journals in which (most of)
those outputs were published? The data are the published submissions and
results from REF2014. The main statistical challenge comes from the fact that
REF quality profiles are available only at the aggregated level of whole units
of assessment: the REF panel's assessment of each individual research output is
not made public. Our research question is thus an `ecological inference'
problem, which demands special care in model formulation and methodology. The
analysis is based on logit models in which journal-specific parameters are
regularized via prior `pseudo-data'. We develop a lack-of-fit measure for the
extent to which REF scores appear to depend on publication venues rather than
research quality or institution-level differences. Results are presented for
several research fields.Comment: 50 pages, 19 figure
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