Preprints of journal discussion papers
The preprints here are available to download to encourage discussion at Ordinary Meetings of the Society before publication in the Journal of the Royal Statistical Society. Certain other papers which will appear in the journal, such as Presidential addresses, are provided for early dissemination of material that may be of wide interest. However, these preprints will typically be removed from this site as soon as they have appeared in print. All preprints available here are provisional and therefore subject to later amendment by the authors. If you would like to receive a preprint for each meeting automatically by e-mail, please contact Abdel Khairoun at firstname.lastname@example.org.
The meetings are usually held at the Society's premises in London and normally begin at 5.00 p.m., with tea available from 4.30 p.m. Fellows and guests are welcome to attend the free informal drinks reception which follows the Ordinary Meeting at about 7 p.m. The meetings may be preceded by an informal session at 3.00 p.m. on the issues raised by the papers.
Contributions to the discussion at Ordinary Meetings are welcome, whether in person at the meeting or subsequently in writing. If you would like to speak at a meeting, please contact Abdel Khairoun at email@example.com, preferably at least a week before the date of the meeting. Contributions must not exceed 5 minutes' speaking time and 400 words for publication in the journal (excluding details of any references quoted). In either case, written versions should be sent to the Executive Editor at the Royal Statistical Society, 12 Errol Street, London, EC1Y 8LX, UK, or by e-mail as PostScript or PDF file attachments to firstname.lastname@example.org to arrive no later than 2 weeks after the meeting. If time allows, contributions that are received before the day of the meeting may be read out by the Secretary for the meeting on behalf of anyone who cannot attend.
Research Section Ordinary Meeting, Wednesday, February 13th, 2013
J. Fan, Y. Liao and M. Mincheva
Ordinary Meeting, Wednesday, November 14th, 2012
C. Hennig and T. F. Liao
The Address of the President, Wednesday, December 7th, 2011
V. Isham (The evolving Society: united we stand) (PDF, 4.14 MB)
The Address of the President, Wednesday, December 10th, 2008
D. J. Hand (Modern statistics: the myth and the magic) (PDF, 1.7 MB)
The Address of the President, Wednesday, December 12th, 2007
D. Tim Holt (Official statistics, public policy and public trust) (PDF, 580 kB)
The Address of the President, Wednesday, June 15th, 2005
A. P. Grieve (The professionalization of the 'shoe clerk') (PDF, 160 kB)
RESEARCH SECTION ORDINARY MEETING, Wednesday, February 13th, 2013
J. Fan (Princeton University), Y. Liao (university of Maryland, College Park) and M. Mincheva (Princeton University)
Large covariance estimation by thresholding principal orthogonal complements
The paper deals with the estimation of a high dimensional covariance with a conditional sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the principal orthogonal complement thresholding method ‘POET’ to explore such an approximate factor structure with sparsity. The POET-estimator includes the sample covariance matrix, the factor based covariance matrix, the thresholding estimator and the adaptive thresholding estimator as specific examples.We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the effect of estimating the unknown factors vanishes as the dimensionality increases.The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.
To be published in Series B.
Large covariance estimation by thresholding principal orthogonal complements (PDF 2.30 MB) and associated data files and computer code (ZIP 970 KB)
ORDINARY MEETING, Wednesday, November 14th, 2012
C. Hennig (University College London) and T. F. Liao (University of Illinois, Champaign)
How to find an appropriate clustering for mixed type variables with application to socio-economic stratification
Data with mixed-type (metric–ordinal–nominal) variables are typical for social stratification,i.e. partitioning a population into social classes. Approaches to cluster such data are compared, namely a latent class mixture model assuming local independence and dissimilarity-based methods such as k -medoids. The design of an appropriate dissimilarity measure and the estimation of the number of clusters are discussed as well, comparing the Bayesian information criterion with dissimilarity-based criteria. The comparison is based on a philosophy of cluster analysis that connects the problem of a choice of a suitable clustering method closely to the application by considering direct interpretations of the implications of the methodology. The application of this philosophy to economic data from the 2007 US Survey of Consumer Finances demonstrates techniques and decisions required to obtain an interpretable clustering. The clustering is shown to be significantly more structured than a suitable null model. One result is that the data-based strata are not as strongly connected to occupation categories as is often assumed in the literature.
To be published in Series C.
How to find an appropriate clustering for mixed type variables with application to socio-economic stratification (PDF, 4.6 MB) and
Data file and computer code (zip, 100 KB)