55,120 research outputs found
A Bayesian Approach to Graphical Record Linkage and De-duplication
We propose an unsupervised approach for linking records across arbitrarily
many files, while simultaneously detecting duplicate records within files. Our
key innovation involves the representation of the pattern of links between
records as a bipartite graph, in which records are directly linked to latent
true individuals, and only indirectly linked to other records. This flexible
representation of the linkage structure naturally allows us to estimate the
attributes of the unique observable people in the population, calculate
transitive linkage probabilities across records (and represent this visually),
and propagate the uncertainty of record linkage into later analyses. Our method
makes it particularly easy to integrate record linkage with post-processing
procedures such as logistic regression, capture-recapture, etc. Our linkage
structure lends itself to an efficient, linear-time, hybrid Markov chain Monte
Carlo algorithm, which overcomes many obstacles encountered by previously
record linkage approaches, despite the high-dimensional parameter space. We
illustrate our method using longitudinal data from the National Long Term Care
Survey and with data from the Italian Survey on Household and Wealth, where we
assess the accuracy of our method and show it to be better in terms of error
rates and empirical scalability than other approaches in the literature.Comment: 39 pages, 8 figures, 8 tables. Longer version of arXiv:1403.0211, In
press, Journal of the American Statistical Association: Theory and Methods
(2015
Bayesian outlier detection in Capital Asset Pricing Model
We propose a novel Bayesian optimisation procedure for outlier detection in
the Capital Asset Pricing Model. We use a parametric product partition model to
robustly estimate the systematic risk of an asset. We assume that the returns
follow independent normal distributions and we impose a partition structure on
the parameters of interest. The partition structure imposed on the parameters
induces a corresponding clustering of the returns. We identify via an
optimisation procedure the partition that best separates standard observations
from the atypical ones. The methodology is illustrated with reference to a real
data set, for which we also provide a microeconomic interpretation of the
detected outliers
Support vector machine for functional data classification
In many applications, input data are sampled functions taking their values in
infinite dimensional spaces rather than standard vectors. This fact has complex
consequences on data analysis algorithms that motivate modifications of them.
In fact most of the traditional data analysis tools for regression,
classification and clustering have been adapted to functional inputs under the
general name of functional Data Analysis (FDA). In this paper, we investigate
the use of Support Vector Machines (SVMs) for functional data analysis and we
focus on the problem of curves discrimination. SVMs are large margin classifier
tools based on implicit non linear mappings of the considered data into high
dimensional spaces thanks to kernels. We show how to define simple kernels that
take into account the unctional nature of the data and lead to consistent
classification. Experiments conducted on real world data emphasize the benefit
of taking into account some functional aspects of the problems.Comment: 13 page
- …