• Corpus ID: 10394967

Journal of the Royal Statistical Society Notes on the Submission of Papers

  title={Journal of the Royal Statistical Society Notes on the Submission of Papers},
  author={Arthur P. Dempster and Roland Askew and R. R. Burridge and William Bluethmann and Myron A. Diftler and Chris Lovchik and Daniel Summer Magruder and Frederik Rehnmark and Frank E. Pollick},
Some journals have policies requiring authors of submitted papers to declare potential conflicts of interest. The purpose is not to remove the conflict but to publicize it, and to allow readers to form their own conclusions on whether any conflict of interest exists. For many of the papers submitted to the Journal of the Royal Statistical Society this is unlikely to be an issue. However, such interests may take many forms, including financial considerations and situations where one or more of… 

Assessing significance in a Markov chain without mixing

A statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution is presented, and it is proved that observing that the presented state is an ε-outlier on the walk is significant at p=2ε under the null hypothesis that the state was chosen from an stationary distribution.

Statistical physics of inference: thresholds and algorithms

The connection between inference and statistical physics is currently witnessing an impressive renaissance and the current state-of-the-art is reviewed, with a pedagogical focus on the Ising model which, formulated as an inference problem, is called the planted spin glass.

Mathematics in the London/Royal Statistical Society 1834-1934

This paper considers the place of mathematical methods based on probability in the work of the London (later Royal) Statistical Society in the first century of its existence, 1834-1934. Regular

Description of the Labour Market Status of Young People in Selected Countries of the European Union – The Taxonomic Approach

Abstract The primary objective of the article was to classify objects that are labour markets of young people in selected EU countries in order to create relatively homogeneous groups based on the

Optimal Belief Approximation

This work seeks a loss function that quantifies how "embarrassing" it is to communicate a given approximation, and reproduces and discusses an old proof showing that there is only one ranking under the requirements that the best ranked approximation is the non-approximated belief.

On the ranking property and underlying dynamics of complex systems

An agent-based model is proposed, which can simulate the competitions of players indifferent matches, and results from the authors' simulations are consistent with the empirical Findings, which indicate that the model is quite robust with respect to the modifications of some parameters.

Research Paper No . 2006 / 151 Concentration among the Rich

The aim of this paper is to examine the concentration of wealth among the group of top wealth holders, defined as those with wealth in excess of a high cut off. The paper begins by considering the

On the cost of Bayesian posterior mean strategy for log-concave models

Some quantitative statistical bounds related to the underlying Poincar\'e constant of the model are established and new results about the numerical approximation of Gibbs measures by Cesaro averages of Euler schemes of (over-damped) Langevin diffusions are established.

Bayesian epidemic models for spatially aggregated count data

This paper utilizes a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros and develops branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastically epidemic processes, useful in policy-focused decision making.

Comparing Bayes factors and hierarchical inference for testing general relativity with gravitational waves

In the context of testing general relativity with gravitational waves, constraints obtained with multiple events are typically combined either through a hierarchical formalism or though a combined



Inference from Iterative Simulation Using Multiple Sequences

The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.

What Is a Knowledge Representation?

It is argued that keeping in mind all five of these roles that a representation plays provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.

Reasoning about uncertainty

This second edition has been updated to reflect Halpern's recent research and includes a consideration of weighted probability measures and how they can be used in decision making.

Detection of Outbreaks from Time Series Data Using Wavelet Transform

In this paper, we developed a new approach to detection of disease outbreaks based on wavelet transform. It is capable of dealing with two problems found in real-world time series data, namely,

Machine learning

Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.

A possibilistic approach to clustering

An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.

Training HMM structure with genetic algorithm for biological sequence analysis

The proposed GA for hidden Markov models (GA-HMM) allows, HMMs with different numbers of states to evolve, and it was capable of finding an HMM comparable to a hand-coded HMM designed for the same task, which has been published previously.

Competitive fuzzy clustering

A new approach called Competitive Agglomeration (CA), which combines the advantages of hierarchical and partitional clustering techniques, is introduced, which can incorporate different distance measures in the objective function of the CA algorithm to find an unknown number of clusters of various shapes.

The possibilistic C-means algorithm: insights and recommendations

The underlying principles of the PCM and the possibilistic approach, in general are examined and the results reported by Barni et al. are interpreted in the light of their findings.

A Robust Competitive Clustering Algorithm With Applications in Computer Vision

This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity