Authors: Margaret Gamalo, Yoonji Kim, Fan Zhang, Junjing Lin
arXiv
Among many efforts to facilitate timely access to safe and effective medicines to children, increased attention has been given to extrapolation. Loosely, it is the leveraging of conclusions or availab...
Authors: Xinwei Sun, Xiangyu Zheng, Jim Weinstein
arXiv
Causal decomposition has provided a powerful tool to analyze health disparity problems, by assessing the proportion of disparity caused by each mediator. However, most of these methods lack \emph{poli...
Authors: Evan Ehrenberg, Kleovoulos Leo Tsourides, Hossein Nejati, Ngai-Man Cheung, Pawan Sinha
arXiv
In the domain of face recognition, there exists a puzzling timing discrepancy between results from macaque neurophysiology on the one hand and human electrophysiology on the other. Single unit recordi...
Authors: M. Gomtsyan, C. Lévy-Leduc, S. Ouadah, L. Sansonnet, C. Bailly, L. Rajjou
arXiv
We propose a novel and efficient iterative two-stage variable selection approach for multivariate sparse GLARMA models, which can be used for modelling multivariate discrete-valued time series. Our ap...
Authors: Mayari Montes de Oca, Jennifer Hill, Lawrence Aber, Carly Tubbs Dolan, Kalina Gjicali
arXiv
This article estimates, for a sample of 1,777 Syrian refugee children, the impact on basic reading assessments of attending a remedial support program in Lebanon that was infused with social and emoti...
Authors: Ezekiel Barnett, Olga Kaiser, Jonathan Masci, Ernst Wit, Stephany Fulda
arXiv
We introduce a new periodicity detection algorithm for binary time series of event onsets, the Gaussian Mixture Periodicity Detection Algorithm (GMPDA). The algorithm approaches the periodicity detect...
Authors: Mariya Mamajiwala, Debasish Roy, Serge Guillas
arXiv
Markov Chain Monte Carlo (MCMC) is one of the most powerful methods to sample from a given probability distribution, of which the Metropolis Adjusted Langevin Algorithm (MALA) is a variant wherein the...
Authors: ShengLi Tzeng, Hao-Yun Hsu
arXiv
The HCV package implements the hierarchical clustering for spatial data. It requires clustering results not only homogeneous in non-geographical features among samples but also geographically close to...
Authors: Jinkyung Yoo, Zequn Sun, Michael Greenacre, Qin Ma, Dongjun Chung, Young Min Kim
arXiv
The study of immune cellular composition has been of great scientific interest in immunology because of the generation of multiple large-scale data. From the statistical point of view, such immune cel...
Authors: Laura Martinez-Sanchez, Daniele Borio, Raphaël d'Andrimont, Marijn van der Velde
arXiv
Approximate distance estimation can be used to determine fundamental landscape properties including complexity and openness. We show that variations in the skyline of landscape photos can be used to e...
Authors: Spyros Makridakis, Evangelos Spiliotis, Ross Hollyman, Fotios Petropoulos, Norman Swanson, Anil Gaba
arXiv
The M6 forecasting competition, the sixth in the Makridakis' competition sequence, is focused on financial forecasting. A key objective of the M6 competition was to contribute to the debate surroundin...
Authors: Yuteng Zhang, Yongchang Hui, Junrong Song, Shurong Zheng
arXiv
Large-scale matrix data has been widely discovered and continuously studied in various fields recently. Considering the multi-level factor structure and utilizing the matrix structure, we propose a mu...
Authors: Ian Lundberg, Rachel Brown-Weinstock, Susan Clampet-Lundquist, Sarah Pachman, Timothy J. Nelson, Vicki Yang, Kathryn Edin, Matthew J. Salganik
arXiv
Why are life trajectories difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multi-decade longitudinal study. Our sampling and...
Authors: Brendan Conway-Smith, Robert L. West
arXiv
This paper will explore ways of computationally accounting for the metacognitive threshold -- the minimum amount of stimulus needed for a mental state to be perceived -- and discuss potential cognitiv...
Authors: Jinfeng Zhong, Elsa Negre
arXiv
Feature attribution is a fundamental task in both machine learning and data analysis, which involves determining the contribution of individual features or variables to a model's output. This process...