35,967 research outputs found
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data
The regression discontinuity (RD) design is a quasi-experimental design that
estimates the causal effects of a treatment by exploiting naturally occurring
treatment rules. It can be applied in any context where a particular treatment
or intervention is administered according to a pre-specified rule linked to a
continuous variable. Such thresholds are common in primary care drug
prescription where the RD design can be used to estimate the causal effect of
medication in the general population. Such results can then be contrasted to
those obtained from randomised controlled trials (RCTs) and inform prescription
policy and guidelines based on a more realistic and less expensive context. In
this paper we focus on statins, a class of cholesterol-lowering drugs, however,
the methodology can be applied to many other drugs provided these are
prescribed in accordance to pre-determined guidelines. NHS guidelines state
that statins should be prescribed to patients with 10 year cardiovascular
disease risk scores in excess of 20%. If we consider patients whose scores are
close to this threshold we find that there is an element of random variation in
both the risk score itself and its measurement. We can thus consider the
threshold a randomising device assigning the prescription to units just above
the threshold and withholds it from those just below. Thus we are effectively
replicating the conditions of an RCT in the area around the threshold, removing
or at least mitigating confounding. We frame the RD design in the language of
conditional independence which clarifies the assumptions necessary to apply it
to data, and which makes the links with instrumental variables clear. We also
have context specific knowledge about the expected sizes of the effects of
statin prescription and are thus able to incorporate this into Bayesian models
by formulating informative priors on our causal parameters.Comment: 21 pages, 5 figures, 2 table
Make-or-buy configurational approaches in product-service ecosystems and performance
This research examines firm boundary configurations for manufacturers' product-service offerings. We argue that the building of a product-service ecosystem through collaboration with service providers in certain types of business services can increase performance as a result of the superior knowledge-based resources coming from specialized partners. By using fuzzy set qualitative analysis on a sample of 370 multinational manufacturing enterprises (MMNEs), the results reveal that effective servitization is heterogeneous across manufacturing industries and across business service offerings. The findings indicate that most industries achieve their highest performance through collaborations with value-added service providers in two out of three of the service continuum stages (Base and Intermediate services); while keeping the development of Advanced services in-house. The results help to contextualize the best practices for implementing service business models in MMNEs by detailing which service capabilities should be retained in-house and which should be outsourced to specialized partners in various industrial contexts.Peer ReviewedPreprin
The Importance of the Wording of the ECB
This paper analyses the ECB communication, focusing in particular on its transparency dimension. We posit that if the ECB is transparent about its future policy decisions, then we should be able to forecast fairly well its future interest rate setting behaviour. We find that the predicting ability of the European monetary authority's words, is similar to the one implied by market-based measures of monetary policy expectations. Moreover, the ECB's wording provides complementary, rather than substitute, information with respect to economic and monetary variables.ECB communication, transparency, monetary policy forecast, empirical reaction function, Euribor rate curve
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