3,761 research outputs found
Inference for feature selection using the Lasso with high-dimensional data
Penalized regression models such as the Lasso have proved useful for variable
selection in many fields - especially for situations with high-dimensional data
where the numbers of predictors far exceeds the number of observations. These
methods identify and rank variables of importance but do not generally provide
any inference of the selected variables. Thus, the variables selected might be
the "most important" but need not be significant. We propose a significance
test for the selection found by the Lasso. We introduce a procedure that
computes inference and p-values for features chosen by the Lasso. This method
rephrases the null hypothesis and uses a randomization approach which ensures
that the error rate is controlled even for small samples. We demonstrate the
ability of the algorithm to compute -values of the expected magnitude with
simulated data using a multitude of scenarios that involve various effects
strengths and correlation between predictors. The algorithm is also applied to
a prostate cancer dataset that has been analyzed in recent papers on the
subject. The proposed method is found to provide a powerful way to make
inference for feature selection even for small samples and when the number of
predictors are several orders of magnitude larger than the number of
observations. The algorithm is implemented in the MESS package in R and is
freely available
Hortibot: Feasibility study of a plant nursing robot performing weeding operations â part IV
Based on the development of a robotic tool carrier (Hortibot) equipped with weeding tools, a feasibility study was carried out to evaluate the viability of this innovative technology. The feasibility was demonstrated through a targeted evaluation adapted to the obtainable knowledge on the system performance in horticulture.
A usage scenario was designed to set the implementation of the robotic system in a row crop of seeded bulb onions considering operational and functional constraints in organic crop, production. This usage scenario together with the technical specifications of the implemented system provided the basis for the feasibility analysis, including a comparison with a conventional weeding system. Preliminary results show that the automation of the weeding tasks within a row crop has the potential of significantly reducing the costs and still fulfill the operational requirements set forth.
The potential benefits in terms of operational capabilities and economic viability have been quantified. Profitability gains ranging from 20 to 50% are achievable through targeted applications. In general, the analyses demonstrate the operational and economic feasibility of using small automated vehicles and targeted tools in specialized production settings
Distributed Plot-Making Creating overview via paper-based and electronic patient records
This paper investigates how physicians create an overview of patient cases through an analysis of physiciansâ work practices. Based on observation of and interviews with physicians, we analyse what overview means to physicians and how they establish it by using different socio-technical systems (paper-based and electronic patient records). Drawing on the theory of distributed cognition and narrative theory, primarily inspired by the work done within health care by Cheryl Mattingly, we propose that the creation of overview may be conceptualised as âdistributed plot-makingâ. Distributed cognition focuses on the role of artefacts, humans and their interaction in information processing, while narrative theory focuses on how humans create narratives through plot construction. Hence, the concept of distributed plot-making highlights the distribution of information processing between different social actors and artefacts, as well as the filtering, sorting and ordering of such information into a narrative that is made coherent by a plot. The analysis shows that the characteristics of paper-based and electronic patient records support or hinder the creation of overview in both similar and different ways. In the light of the current move towards electronic patient records, we explore ways in which the benefits of paper records may be carried over into the electronic patient record as well as the ways in which the possibilities afforded by digital artefacts may be exploited more fully than is currently the case
Having a Ball: evaluating scoring streaks and game excitement using in-match trend estimation
Many popular sports involve matches between two teams or players where each
team have the possibility of scoring points throughout the match. While the
overall match winner and result is interesting, it conveys little information
about the underlying scoring trends throughout the match. Modeling approaches
that accommodate a finer granularity of the score difference throughout the
match is needed to evaluate in-game strategies, discuss scoring streaks, teams
strengths, and other aspects of the game.
We propose a latent Gaussian process to model the score difference between
two teams and introduce the Trend Direction Index as an easily interpretable
probabilistic measure of the current trend in the match as well as a measure of
post-game trend evaluation. In addition we propose the Excitement Trend Index -
the expected number of monotonicity changes in the running score difference -
as a measure of overall game excitement.
Our proposed methodology is applied to all 1143 matches from the 2019-2020
National Basketball Association (NBA) season. We show how the trends can be
interpreted in individual games and how the excitement score can be used to
cluster teams according to how exciting they are to watch
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