10,240 research outputs found
APOM-project : a study of pharmacy practice
In 1994, a Ph.D-study started regarding pharmacy, organization and management (APOM) in the Netherlands. The APOM-project deals with the structuring and steering of pharmacy organization. This article describes the summary of the empirical results of a survey in a relatively large sample (n=169). Generalization to the population of pharmacies in the Netherlands was made. The results for thought, the perceived importance of activities, comprised a total number of seven clusters of priorities of pharmacy mixes. Most pharmacy managers perceived the product (pharmaceutical) activities and the customer activities as the most important. The results for action, the actual performance of activities, comprised a total number of five clusters of activities of pharmacy mixes. Most pharmacy managers performed the product activities and the process (financial-economic) activities most frequently. The results showed that the traditional conception of the work in the community pharmacy is still vividly present.
Sparsity with sign-coherent groups of variables via the cooperative-Lasso
We consider the problems of estimation and selection of parameters endowed
with a known group structure, when the groups are assumed to be sign-coherent,
that is, gathering either nonnegative, nonpositive or null parameters. To
tackle this problem, we propose the cooperative-Lasso penalty. We derive the
optimality conditions defining the cooperative-Lasso estimate for generalized
linear models, and propose an efficient active set algorithm suited to
high-dimensional problems. We study the asymptotic consistency of the estimator
in the linear regression setup and derive its irrepresentable conditions, which
are milder than the ones of the group-Lasso regarding the matching of groups
with the sparsity pattern of the true parameters. We also address the problem
of model selection in linear regression by deriving an approximation of the
degrees of freedom of the cooperative-Lasso estimator. Simulations comparing
the proposed estimator to the group and sparse group-Lasso comply with our
theoretical results, showing consistent improvements in support recovery for
sign-coherent groups. We finally propose two examples illustrating the wide
applicability of the cooperative-Lasso: first to the processing of ordinal
variables, where the penalty acts as a monotonicity prior; second to the
processing of genomic data, where the set of differentially expressed probes is
enriched by incorporating all the probes of the microarray that are related to
the corresponding genes.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS520 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Clustering in an Object-Oriented Environment
This paper describes the incorporation of seven stand-alone clustering programs into S-PLUS, where they can now be used in a much more flexible way. The original Fortran programs carried out new cluster analysis algorithms introduced in the book of Kaufman and Rousseeuw (1990). These clustering methods were designed to be robust and to accept dissimilarity data as well as objects-by-variables data. Moreover, they each provide a graphical display and a quality index reflecting the strength of the clustering. The powerful graphics of S-PLUS made it possible to improve these graphical representations considerably. The integration of the clustering algorithms was performed according to the object-oriented principle supported by S-PLUS. The new functions have a uniform interface, and are compatible with existing S-PLUS functions. We will describe the basic idea and the use of each clustering method, together with its graphical features. Each function is briefly illustrated with an example.
Assessing multivariate predictors of financial market movements: A latent factor framework for ordinal data
Much of the trading activity in Equity markets is directed to brokerage
houses. In exchange they provide so-called "soft dollars," which basically are
amounts spent in "research" for identifying profitable trading opportunities.
Soft dollars represent about USD 1 out of every USD 10 paid in commissions.
Obviously they are costly, and it is interesting for an institutional investor
to determine whether soft dollar inputs are worth being used (and indirectly
paid for) or not, from a statistical point of view. To address this question,
we develop association measures between what broker--dealers predict and what
markets realize. Our data are ordinal predictions by two broker--dealers and
realized values on several markets, on the same ordinal scale. We develop a
structural equation model with latent variables in an ordinal setting which
allows us to test broker--dealer predictive ability of financial market
movements. We use a multivariate logit model in a latent factor framework,
develop a tractable estimator based on a Laplace approximation, and show its
consistency and asymptotic normality. Monte Carlo experiments reveal that both
the estimation method and the testing procedure perform well in small samples.
The method is then used to analyze our dataset.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS213 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Revenue Function for Comparison-Based Hierarchical Clustering
Comparison-based learning addresses the problem of learning when, instead of
explicit features or pairwise similarities, one only has access to comparisons
of the form: \emph{Object is more similar to than to .} Recently, it
has been shown that, in Hierarchical Clustering, single and complete linkage
can be directly implemented using only such comparisons while several
algorithms have been proposed to emulate the behaviour of average linkage.
Hence, finding hierarchies (or dendrograms) using only comparisons is a well
understood problem. However, evaluating their meaningfulness when no
ground-truth nor explicit similarities are available remains an open question.
In this paper, we bridge this gap by proposing a new revenue function that
allows one to measure the goodness of dendrograms using only comparisons. We
show that this function is closely related to Dasgupta's cost for hierarchical
clustering that uses pairwise similarities. On the theoretical side, we use the
proposed revenue function to resolve the open problem of whether one can
approximately recover a latent hierarchy using few triplet comparisons. On the
practical side, we present principled algorithms for comparison-based
hierarchical clustering based on the maximisation of the revenue and we
empirically compare them with existing methods.Comment: 26 pages, 6 figures, 5 tables. Transactions on Machine Learning
Research (2023
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding
Human face exhibits an inherent hierarchy in its representations (i.e.,
holistic facial expressions can be encoded via a set of facial action units
(AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown
great results in unsupervised extraction of hierarchical latent representations
from large amounts of image data, while being robust to noise and other
undesired artifacts. Potentially, this makes VAEs a suitable approach for
learning facial features for AU intensity estimation. Yet, most existing
VAE-based methods apply classifiers learned separately from the encoded
features. By contrast, the non-parametric (probabilistic) approaches, such as
Gaussian Processes (GPs), typically outperform their parametric counterparts,
but cannot deal easily with large amounts of data. To this end, we propose a
novel VAE semi-parametric modeling framework, named DeepCoder, which combines
the modeling power of parametric (convolutional) and nonparametric (ordinal
GPs) VAEs, for joint learning of (1) latent representations at multiple levels
in a task hierarchy1, and (2) classification of multiple ordinal outputs. We
show on benchmark datasets for AU intensity estimation that the proposed
DeepCoder outperforms the state-of-the-art approaches, and related VAEs and
deep learning models.Comment: ICCV 2017 - accepte
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