Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology. 2009;20(4):555–61. Article PubMed Google Scholar Latouche A, Allignol A, Beyersmann J, Labopin M, Fine JP. A competing risks analysis should report results on all cause-specific ...
Drawing on the decision science literature, we have created a paradigm to measure situational enjoyment during reading. Using this paradigm, we find reading enjoyment is associated with further decision-making about the text and with reading comprehension....
This convention is made throughout this paper for clear visualization because, as far as kinetic rate theory is concerned, only the energy difference between the energy barrier and energy well matters. However, this convention does not mean to suggest that force can only change the energy barrier...
Through behavioral paradigms and scales, two studies were conducted (Study 1: n = 570; Study 2: n = 501). We explored the predictive effect of anxiety on interpersonal curiosity in situations when mandatory isolation measures have led to dramatic changes in interpersonal distancing and autistic ...
Since there is no general plan, it is important to understand which cities are best suited to different autonomous urban mobility scenarios. In this regard, service research gaps remain. First, to the best of our knowledge, there have been only a few studies conducted (Fagnant et al., 2015...
Yet there has been surprisingly little philosophical work done on the nature of heuristics and heuristic reasoning, and a close inspec- tion of the way(s) in which "heuristic" is used throughout the literature reveals a vagueness and uncertainty with respect to what heuristics are and their ...
Such quantification should be conducted to reflect the sensitivity of model projections to the most important known sources of uncertainty in rela- tion to the phenomena being targeted for prediction. Such uncertainty assessments are becoming more common in relation to parameter uncertainty and internal ...
However, unsupervised learning models are suited better for segmenting existing data into classes, since it’s quite difficult to check the model prediction accuracy, especially without a labeled dataset. Written byViktoriia Akhremenko, AI/ML Team Leader atMobiDev. ...
Recently, a number of deep neural network-based KT models have been proposed. The Deep-IRT16is an extended DKT model, which has been inspired by Bayesian deep learning. The Deep-IRT can achieve better prediction performance than shallow structured models, but it lacks personalized descriptions of...
(version 0.16). All our linear mixed effects models were conducted in R Studio using the lmer function (from thelme4package). For these models, we also used the Anova function (from thecarpackage) to conduct a Type III ANOVA with a Kenward-Roger99approximation for degrees of freedom, as...