4,198 research outputs found
On Abandoning XLISP-STAT
In'98 the UCLA Department of Statistics, which had been one of the major users of Lisp-Stat, and one of the main producers of Lisp-Stat code, decided to switch to S/R. This paper discusses why this decision was made, and what the pros and the cons were.
Interdependent binary choices under social influence: phase diagram for homogeneous unbiased populations
Coupled Ising models are studied in a discrete choice theory framework, where
they can be understood to represent interdependent choice making processes for
homogeneous populations under social influence. Two different coupling schemes
are considered. The nonlocal or group interdependence model is used to study
two interrelated groups making the same binary choice. The local or individual
interdependence model represents a single group where agents make two binary
choices which depend on each other. For both models, phase diagrams, and their
implications in socioeconomic contexts, are described and compared in the
absence of private deterministic utilities (zero opinion fields).Comment: 17 pages, 3 figures. This is the pre-peer reviewed version of the
following article: Ana Fern\'andez del R\'io, Elka Korutcheva and Javier de
la Rubia, Interdependent binary choices under social influence, Wiley's
Complexity, 2012; which has been published in final form at
http://onlinelibrary.wiley.com/doi/10.1002/cplx.21397/abstrac
An Evaluation of The Host Response to An Interspinous Process Device Based on A Series of Spine Explants: Device for Intervertebral Assisted Motion (DIAM®)
Background:
The objective of this study was to evaluate the host response to an interspinous process device [Device for Intervertebral Assisted Motion (DIAM®)] based on a series of nine spine explants with a mean post-operative explant time of 35 months. Methods:
Explanted periprosthetic tissues were processed for histology and stained with H&E, Wright-Giemsa stain, and Oil Red O. Brightfield and polarized light microscopy were used to evaluate the host response to the device and the resultant particulate debris. The host response was graded per ASTM F981-04. Quantitative histomorphometry was used to characterize particle size, shape, and area per ASTM F1877-05. The presence or absence of bone resorption was also evaluated when bony tissue samples were provided. Results:
Periprosthetic tissues demonstrated a non-specific foreign body response composed of macrophages and foreign body giant cells to the DIAM® device in most of the accessions. The foreign body reaction was not the stated reason for explantation in any of the accessions. Per ASTM F981-04, a “very slight” to “mild” to “moderate” chronic inflammatory response was observed to the biomaterials and particulate, and this varied by tissue sample and accession. Particle sizes were consistent amongst the explant patients with mean particle size on the order of several microns. Osteolysis, signs of toxicity, necrosis, an immune response, and/or device related infection were not observed. Conclusions:
Cyclic loading of the spine can cause wear in dynamic stabilization systems such as DIAM®. The fabric nature of the DIAM® device’s polyethylene terephthalate jacket coupled with the generation of polymeric particulate debris predisposes the device to a foreign body reaction consisting of macrophages and foreign body giant cells. Although not all patients are aware of symptoms associated with a foreign body reaction to a deeply implanted device, surgeons should be aware of the host response to this device
Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): linking condensation, evaporation and chemical reactions of organics, oxidants and water
We present a novel kinetic multi-layer model for gas-particle interactions in aerosols and clouds (KM-GAP) that treats explicitly all steps of mass transport and chemical reaction of semi-volatile species partitioning between gas phase, particle surface and particle bulk. KM-GAP is based on the PRA model framework (Pöschl-Rudich-Ammann, 2007), and it includes gas phase diffusion, reversible adsorption, surface reactions, bulk diffusion and reaction, as well as condensation, evaporation and heat transfer. The size change of atmospheric particles and the temporal evolution and spatial profile of the concentration of individual chemical species can be modelled along with gas uptake and accommodation coefficients. Depending on the complexity of the investigated system, unlimited numbers of semi-volatile species, chemical reactions, and physical processes can be treated, and the model shall help to bridge gaps in the understanding and quantification of multiphase chemistry and microphysics in atmo- spheric aerosols and clouds.
In this study we demonstrate how KM-GAP can be used to analyze, interpret and design experimental investigations of changes in particle size and chemical composition in response to condensation, evaporation, and chemical reaction. For the condensational growth of water droplets, our kinetic model results provide a direct link between laboratory observations and molecular dynamic simulations, confirming that the accommodation coefficient of water at 270 K is close to unity. Literature data on the evaporation of dioctyl phthalate as a function of particle size and time can be reproduced, and the model results suggest that changes in the experimental conditions like aerosol particle concentration and chamber geometry may influence the evaporation kinetics and can be optimized for eĂ°cient probing of specific physical effects and parameters. With regard to oxidative aging of organic aerosol particles, we illustrate how the formation and evaporation of volatile reaction products like nonanal can cause a decrease in the size of oleic acid particles exposed to ozone
topicmodels: An R Package for Fitting Topic Models
Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.
Evolution of statistical analysis in empirical software engineering research: Current state and steps forward
Software engineering research is evolving and papers are increasingly based
on empirical data from a multitude of sources, using statistical tests to
determine if and to what degree empirical evidence supports their hypotheses.
To investigate the practices and trends of statistical analysis in empirical
software engineering (ESE), this paper presents a review of a large pool of
papers from top-ranked software engineering journals. First, we manually
reviewed 161 papers and in the second phase of our method, we conducted a more
extensive semi-automatic classification of papers spanning the years 2001--2015
and 5,196 papers. Results from both review steps was used to: i) identify and
analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well
as relevant trends in usage of specific statistical methods (e.g.,
nonparametric tests and effect size measures) and, ii) develop a conceptual
model for a statistical analysis workflow with suggestions on how to apply
different statistical methods as well as guidelines to avoid pitfalls. Lastly,
we confirm existing claims that current ESE practices lack a standard to report
practical significance of results. We illustrate how practical significance can
be discussed in terms of both the statistical analysis and in the
practitioner's context.Comment: journal submission, 34 pages, 8 figure
Learning Large-Scale Bayesian Networks with the sparsebn Package
Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure
- …