13,312 research outputs found
A nurse-led sleep service for children and young people with disability
Aim: To evaluate the outcomes from a nurse-led, community-based sleep hygiene service for children and young people, which was designed and implemented in a community NHS trust. The project aimed to provide evidence for wider implementation of such a service across the trust.
Method: The project recruited 22 participants to an eight-week programme over six months and collected quantitative and qualitative data. It included evaluating service costs and collecting information about how the child’s sleep problem affected the carer and family pre- and post-intervention.
Findings: There was a significant, positive effect on quality-of-life measures, with two thirds of participants achieving 40% of their expectations by the end of the eight weeks. Parents said they felt ‘less helpless’ and they valued the support given in the home setting.
Conclusion: Cost and benefit analysis showed that the service could reduce costs associated with high-cost prescriptions. It could also positively affect community paediatric waiting lists and clinic appointments
Keyed Non-Parametric Hypothesis Tests
The recent popularity of machine learning calls for a deeper understanding of
AI security. Amongst the numerous AI threats published so far, poisoning
attacks currently attract considerable attention. In a poisoning attack the
opponent partially tampers the dataset used for learning to mislead the
classifier during the testing phase.
This paper proposes a new protection strategy against poisoning attacks. The
technique relies on a new primitive called keyed non-parametric hypothesis
tests allowing to evaluate under adversarial conditions the training input's
conformance with a previously learned distribution . To do so we
use a secret key unknown to the opponent.
Keyed non-parametric hypothesis tests differs from classical tests in that
the secrecy of prevents the opponent from misleading the keyed test
into concluding that a (significantly) tampered dataset belongs to
.Comment: Paper published in NSS 201
Inference with interference between units in an fMRI experiment of motor inhibition
An experimental unit is an opportunity to randomly apply or withhold a
treatment. There is interference between units if the application of the
treatment to one unit may also affect other units. In cognitive neuroscience, a
common form of experiment presents a sequence of stimuli or requests for
cognitive activity at random to each experimental subject and measures
biological aspects of brain activity that follow these requests. Each subject
is then many experimental units, and interference between units within an
experimental subject is likely, in part because the stimuli follow one another
quickly and in part because human subjects learn or become experienced or
primed or bored as the experiment proceeds. We use a recent fMRI experiment
concerned with the inhibition of motor activity to illustrate and further
develop recently proposed methodology for inference in the presence of
interference. A simulation evaluates the power of competing procedures.Comment: Published by Journal of the American Statistical Association at
http://www.tandfonline.com/doi/full/10.1080/01621459.2012.655954 . R package
cin (Causal Inference for Neuroscience) implementing the proposed method is
freely available on CRAN at https://CRAN.R-project.org/package=ci
Implementing a Class of Permutation Tests: The coin Package
The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nominal, ordered, numeric, and censored data as well as multivariate data at mixed scales. Based on a rich and flexible conceptual framework that embeds different permutation test procedures into a common theory, a computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions. As a consequence, the computational tools in coin inherit the flexibility of the underlying theory and conditional inference functions for important special cases can be set up easily. Conditional versions of classical tests---such as tests for location and scale problems in two or more samples, independence in two- or three-way contingency tables, or association problems for censored, ordered categorical or multivariate data---can easily be implemented as special cases using this computational toolbox by choosing appropriate transformations of the observations. The paper gives a detailed exposition of both the internal structure of the package and the provided user interfaces along with examples on how to extend the implemented functionality.
The robusTest package: two-sample tests revisited
The R package robusTest offers corrected versions of several common tests in
bivariate statistics. We point out the limitations of these tests in their
classical versions, some of which are well known such as robustness or
calibration problems, and provide simple alternatives that can be easily used
instead. The classical tests and theirs robust alternatives are compared
through a small simulation study. The latter emphasizes the superiority of
robust versions of the test of interest. Finally, an illustration of
correlation's tests on a real data set is also provided
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