Results 11 to 20 of about 137,972 (308)
Development of THC estimation model using FTIR spectrum
Nihon Kikai Gakkai ronbunshu, 2021 A novel total hydrocarbon (THC) emission concentration estimation model is proposed for reduction of engine development cost as well as simplification of measurement system.
Hirotaka YABUSHITA +3 more
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Regularized Machine Learning Models for Prediction of Metabolic Syndrome Using GCKR, APOA5, and BUD13 Gene Variants: Tehran Cardiometabolic Genetic Study [PDF]
Cell Journal, 2023 Objective: Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcaresystems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants ofGCKR, BUD13 and APOA5, and ...
Nadia Alipour +5 more
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npj Microgravity, 2021 Healthy plants are vital for successful, long-duration missions in space, as they provide the crew with life support, food production, and psychological benefits.
Natasha J. Haveman +6 more
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Frontiers in Human Neuroscience, 2021 Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses ...
Matthew Pietrosanu +7 more
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Frontiers in Oncology, 2021 Background and PurposeLower grade glioma (LGG) is one of the leading causes of death world worldwide. We attempted to develop and validate a radiosensitivity model for predicting the survival of lower grade glioma by using spike-and-slab lasso Cox model ...
Zixuan Du +12 more
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Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China
Remote Sensing, 2021 Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial ...
Simeng Wang, Qihang Liu, Chang Huang
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Canadian Journal of Statistics, 2015 AbstractWe propose a new sparse regression method called thecomponent lasso, based on a simple idea. The method uses the connected‐components structure of the sample covariance matrix to split the problem into smaller ones. It then applies the lasso to each subproblem separately, obtaining a coefficient vector for each one.
Nadine Hussami, Robert Tibshirani
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La Técnica: Revista de las Agrociencias, 2020 Exploitative technological evaluation of the Dongfeng DF 151L motor cultivator in soil preparation for sowing maize Resumen La presente investigación se realizó en la “Finca Juanito”, coordenadas 1o19’46’’ LS y 80o35’3’’ LO, cantón Jipijapa ...
Byron Leonardo Quimís Guerrido +4 more
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Statistics and Computing, 2011 We propose a shrinkage procedure for simultaneous variable selection and estimation in generalized linear models (GLMs) with an explicit predictive motivation. The procedure estimates the coefficients by minimizing the Kullback-Leibler divergence of a set of predictive distributions to the corresponding predictive distributions for the full model ...
Minh-Ngoc Tran +2 more
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Biometrika, 2020 Summary The properties of penalized sample covariance matrices depend on the choice of the penalty function. In this paper, we introduce a class of nonsmooth penalty functions for the sample covariance matrix and demonstrate how their use results in a grouping of the estimated eigenvalues.
Tyler, David E., Yi, Mengxi
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