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The latest content from StatisticsViews.http://www.statisticsviews.com"From the very beginning, the BBC has made statistics a central feature of its election night coverage:" An interview with Sir John Curtice
http://www.statisticsviews.com/details/feature/11118967/From-the-very-beginning-the-BBC-has-made-statistics-a-central-feature-of-its-ele.html
Sir John Curtice is Senior Research Fellow at NatCen, Professor of Politics at Strathclyde University, and Chief Commentator on the What UK Thinks: EU and What Scotland Thinks websites.
He has been a regular contributor to the British Social Attitudes Report series since 1986 and an editor since 1994. He has also been a Co-Director of the Scottish Social Attitudes survey since its foundation in 1999, and his analyses of Scottish public opinion in the run up to the independence referendum were frequently...]]>2018-12-12T13:42:00ZArmitage Lecture 2018
http://www.statisticsviews.com/details/webinar/11118962/Armitage-Lecture-2018.html
Courtesy of the MRC Biostatistics Unit, University of Cambridge. Please note that the webinar has now taken place. Register for FREE to view.
Registrants now have a chance to view the webinar of the lecture which took place on 15th November 2018.
Statistics Views was proud to again host a webinar of the Armitage Lecture, which is named to honour the immense contributions of Professor Peter Armitage CBE who was at the MRC Biostatistics Unit from 1947 to 1961, and whose work is recognised throughout the...]]>2018-12-12T11:03:00ZNonlinear Time Series Analysis
http://www.statisticsviews.com/details/book/11114779/Nonlinear-Time-Series-Analysis.html
A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis
Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series...]]>2018-12-12T00:00:00ZHow machine learning can predict risk of emergency hospital admissions
http://www.statisticsviews.com/details/news/11117179/How-machine-learning-can-predict-risk-of-emergency-hospital-admissions.html
Machine learning -- a field of artificial intelligence that uses statistical techniques to enable computer systems to 'learn' from data -- can be used to analyse electronic health records and predict the risk of emergency hospital admissions, a new study from The George Institute for Global Health at the University of Oxford has found.
The research, published in the journal PLOS Medicine, suggests that using these techniques could help health practitioners accurately monitor the risks faced by patients...]]>2018-12-11T12:15:00ZThe continuum random tree is the scaling limit of unlabeled unrooted trees
http://www.statisticsviews.com/details/journalArticle/11118727/The-continuum-random-tree-is-the-scaling-limit-of-unlabeled-unrooted-trees.html
We show that the uniform unlabeled unrooted tree with n vertices and vertex degrees in a fixed set converges in the Gromov‐Hausdorff sense after a suitable rescaling to the Brownian
continuum random tree. This confirms a conjecture by Aldous (1991). We also establish Benjamini‐Schramm convergence of this
model of random trees and provide a general approximation result, that allows for a transfer of a wide range of asymptotic
properties of...]]>2018-12-11T00:00:00ZGeometry of large Boltzmann outerplanar maps
http://www.statisticsviews.com/details/journalArticle/11118728/Geometry-of-large-Boltzmann-outerplanar-maps.html
We study the phase diagram of random outerplanar maps sampled according to nonnegative Boltzmann weights that are assigned
to each face of a map. We prove that for certain choices of weights the map looks like a rescaled version of its boundary
when its number of vertices tends to infinity. The Boltzmann outerplanar maps are then shown to converge in the Gromov‐Hausdorff
sense towards the α‐stable looptree introduced by Curien and...]]>2018-12-11T00:00:00ZReverse juggling processes
http://www.statisticsviews.com/details/journalArticle/11118726/Reverse-juggling-processes.html
Knutson introduced two families of reverse juggling Markov chains (single and multispecies) motivated by the study of random
semi‐infinite matrices over
F
q
. We present natural generalizations of both chains by placing generic weights that still lead to simple combinatorial expressions
for the stationary distribution. For permutations, this is a seemingly...]]>2018-12-11T00:00:00ZRobust variable screening for regression using factor profiling
http://www.statisticsviews.com/details/journalArticle/11118724/Robust-variable-screening-for-regression-using-factor-profiling.html
Sure independence screening is a fast procedure for variable selection in ultra‐high dimensional regression analysis. Unfortunately,
its performance greatly deteriorates with increasing dependence among the predictors. To solve this issue, Factor Profiled
Sure Independence Screening (FPSIS) models the correlation structure of the predictor variables, assuming that it can be represented
by a few latent factors. The correlations can then be...]]>2018-12-11T00:00:00ZCCC‐ <i xmlns="http://www.wiley.com/namespaces/wiley">r</i> charts' performance with estimated parameter for high‐quality process
http://www.statisticsviews.com/details/journalArticle/11118759/CCC-r-charts-performance-with-estimated-parameter-for-highquality-process.html
Abstract
CCC‐r charts are effective in detecting process shifts in the nonconforming rate especially for a high‐quality process. The implementation
of the CCC‐r charts is usually under the assumption that the in‐control nonconforming rate is known. However, the nonconforming rate is
never known, and accurate estimation is difficult. We investigate the effect of estimation error on the CCC‐r charts' performances through the expected value of the...]]>2018-12-11T00:00:00ZMultivariate process incapability vector
http://www.statisticsviews.com/details/journalArticle/11118760/Multivariate-process-incapability-vector.html
Abstract
Process capability indices evaluate the capability of the processes in satisfying customer's requirements. This paper introduces
a superstructure multivariate process incapability vector for multivariate normal processes and then, compares it with four
recently proposed multivariate process capability indices to show its better performance. In addition, the effects of two
modification factors are investigated. Also,...]]>2018-12-11T00:00:00Z