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Found 9 papers, 1 papers with code
Uncertainty quantification in metric spaces
To illustrate the effectiveness of the proposed uncertainty quantification framework, we use a linear regression model for metric responses (known as the global Fr\'echet model) in various clinical applications related to precision and digital medicine.
Deep Learning Framework with Uncertainty Quantification for Survey Data: Assessing and Predicting Diabetes Mellitus Risk in the American Population
The objectives of this paper are: (i) To propose a general predictive framework for regression and classification using neural network (NN) modeling, which incorporates survey weights into the estimation process; (ii) To introduce an uncertainty quantification algorithm for model prediction, tailored for data from complex survey designs; (iii) To apply this method in developing robust risk score models to assess the risk of Diabetes Mellitus in the US population, utilizing data from the NHANES 2011-2014 cohort.
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
In this paper, we introduce a kNN-based regression method that synergizes the scalability and adaptability of traditional non-parametric kNN models with a novel variable selection technique.
Kernel Biclustering algorithm in Hilbert Spaces
Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functions.
Neural interval-censored Cox regression with feature selection
The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine.
Hypothesis testing for matched pairs with missing data by maximum mean discrepancy: An application to continuous glucose monitoring
A frequent problem in statistical science is how to properly handle missing data in matched paired observations.
Glucose values prediction five years ahead with a new framework of missing responses in reproducing kernel Hilbert spaces, and the use of continuous glucose monitoring technology
Next, we adapt or use existing models of variable selection, regression, and conformal inference to obtain new clinical findings about glucose changes five years ahead with the AEGIS data.
COVID-19: Estimating spread in Spain solving an inverse problem with a probabilistic model
Based on our findings, we can: i) estimate the risk of a new outbreak before Autumn if we lift the quarantine; ii) may know the degree of immunization of the population in each region; and iii) forecast or simulate the effect of the policies to be introduced in the future based on the number of infected or recovered individuals in the population.
Populations and Evolution Quantitative Methods Applications
The FA Quantifier Fuzzification Mechanism: analysis of convergence and efficient implementations
The main contribution of this paper is the proof of a convergence result that links this quantification model with the Zadeh's model when the size of the input sets tends to infinite.
