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Found 9 papers, 3 papers with code
Uncertainty Quantification in Inverse Models in Hydrology
Our inverse model offers 3\% improvement in R$^2$ for the inverse model (basin characteristic estimation) and 6\% for the forward model (streamflow prediction).
Probabilistic Inverse Modeling: An Application in Hydrology
We propose uncertainty based learning method that offers 6\% improvement in $R^2$ for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17\% reduction in uncertainty (40\% in presence of noise) and 4\% higher coverage rate for basin characteristics.
Feature Selection using e-values
In the context of supervised parametric models, we introduce the concept of e-values.
Approximate Bayesian Computation for Physical Inverse Modeling
Using these TFT models to draw inference involves estimating parameters used to fit to the experimental data.
Generalized Multivariate Signs for Nonparametric Hypothesis Testing in High Dimensions
High-dimensional data, where the dimension of the feature space is much larger than sample size, arise in a number of statistical applications.
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series
Traditional approaches focus on finding relationships between two entire time series, however, many interesting relationships exist in small sub-intervals of time and remain feeble during other sub-intervals.
On Weighted Multivariate Sign Functions
Multivariate sign functions are often used for robust estimation and inference.
Methodology
Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks
In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system.
Mining Sub-Interval Relationships In Time Series Data
In this paper, we define the notion of a sub-interval relationship (SIR) to capture inter- actions between two time series that are prominent only in certain sub-intervals of time.
