2,011 research outputs found
Exact testing with random permutations
When permutation methods are used in practice, often a limited number of
random permutations are used to decrease the computational burden. However,
most theoretical literature assumes that the whole permutation group is used,
and methods based on random permutations tend to be seen as approximate. There
exists a very limited amount of literature on exact testing with random
permutations and only recently a thorough proof of exactness was given. In this
paper we provide an alternative proof, viewing the test as a "conditional Monte
Carlo test" as it has been called in the literature. We also provide extensions
of the result. Importantly, our results can be used to prove properties of
various multiple testing procedures based on random permutations
Multiple Testing for Exploratory Research
Motivated by the practice of exploratory research, we formulate an approach
to multiple testing that reverses the conventional roles of the user and the
multiple testing procedure. Traditionally, the user chooses the error
criterion, and the procedure the resulting rejected set. Instead, we propose to
let the user choose the rejected set freely, and to let the multiple testing
procedure return a confidence statement on the number of false rejections
incurred. In our approach, such confidence statements are simultaneous for all
choices of the rejected set, so that post hoc selection of the rejected set
does not compromise their validity. The proposed reversal of roles requires
nothing more than a review of the familiar closed testing procedure, but with a
focus on the non-consonant rejections that this procedure makes. We suggest
several shortcuts to avoid the computational problems associated with closed
testing.Comment: Published in at http://dx.doi.org/10.1214/11-STS356 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Consistent ICP for the registration of sparse and inhomogeneous point clouds
In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain no noise or clutter and moreover are dense and more or less uniformly sampled. In the other case, it is better to employ point-to-plane or other metrics to locally approximate the surface of the objects. However, the point-to-plane metric does not yield a symmetric solution, i.e. the estimated transformation of point cloud p to point cloud q is not necessarily equal to the inverse transformation of point cloud q to point cloud p. In order to improve ICP, we will enforce such symmetry constraints as prior knowledge and make it also robust to noise and clutter. Experimental results show that our method is indeed much more consistent and accurate in presence of noise and clutter compared to existing ICP algorithms
Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack
It has been shown that a random-effects framework can be used to test the
association between a gene's expression level and the number of DNA copies of a
set of genes. This gene-set modelling framework was later applied to find
associations between mRNA expression and microRNA expression, by defining the
gene sets using target prediction information.
Here, we extend the model introduced by Menezes et al (2009) to consider the
effect of not just copy number, but also of other molecular profiles such as
methylation changes and loss-of-heterozigosity (LOH), on gene expression
levels. We will consider again sets of measurements, to improve robustness of
results and increase the power to find associations. Our approach can be used
genome-wide to find associations, yields a test to help separate true
associations from noise and can include confounders.
We apply our method to colon and to breast cancer samples, for which
genome-wide copy number, methylation and gene expression profiles are
available. Our findings include interesting gene expression-regulating
mechanisms, which may involve only one of copy number or methylation, or both
for the same samples. We even are able to find effects due to different
molecular mechanisms in different samples.
Our method can equally well be applied to cases where other types of
molecular (high-dimensional) data are collected, such as LOH, SNP genotype and
microRNA expression data. Computationally efficient, it represents a flexible
and powerful tool to study associations between high-dimensional datasets. The
method is freely available via the SIM BioConductor package
Time for action! ICT integration in formal education : key findings from a region-wide follow-up monitor
This paper is a report on the key findings of a region-wide monitoring study conducted in Dutch-speaking schools in Belgium. First, we elaborate on the building blocks of the instrument, which has been updated and improved since its first deployment in 2007. In particular we focus on the core indicators, along with the multi-actor approach, the sample design and the ways in which new phenomena such as media literacy and gaming have been operationalized. Secondly, we highlight the main trends and patterns within pre-school, primary and secondary education. The first descriptive analyses show quite disappointing results with regard to ICT use at the micro level and the available infrastructure, while headmasters, teachers and pupils reported positive perceptions of different aspects of ICT integration. These results indicate an urgent need to take appropriate action. Therefore, the final part of the paper examines how ICT integration could be improved via structural changes and appropriate policymaking with regard to budgeting, teacher training and the particular role of ICT coordinators in schools
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