The discrete-time multifactor Vasiček model is a tractable Gaussian spot rate model. Typically, two- or three-factor versions allow one to capture the dependence structure between yields with different times to maturity in an appropriate way. In practice, re-calibration of the model to the prevailing market conditions …
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22 results for “re-calibration”
New method calibrates probabilistic regression models without restrictive assumptions.
problem Ensuring predictive distributions accurately reflect true uncertainty.
method Nonparametric re-calibration algorithm based on conditional kernel mean embeddings.
result Consistently outperforms prior re-calibration approaches across various benchmarks.
Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regressionastro-ph.IM
New method calibrates photometric redshift PDFs more accurately.
problem Inaccurate photometric redshift uncertainties lead to systematic errors.
method Local re-calibration using feature-space regression of Probability Integral Transform (PIT) distributions.
result Calibrated PDFs are more accurate at all locations in feature space.
Study reduces complexity and uncertainty in human atrial cell models.
problem Uncertainty in parameter estimates from gating kinetics models.
method Approximate Bayesian computation to re-calibrate models, investigate two approaches: more complete datasets and less complex formulations.
result Less complex model with fewer parameters gives better fit and lower uncertainty.
Optimizing full likelihoods adapts loss scales and shapes for robust modeling.
problem Rigid loss functions limit model adaptability and robustness.
method Optimize full likelihoods with adjustable parameters.
result Adaptive tuning of loss scales and shapes improves model robustness.
Binary classification is highly used in credit scoring in the estimation of probability of default. The validation of such predictive models is based both on rank ability, and also on calibration (i.e. how accurately the probabilities output by the model map to the observed probabilities). In this study we cover the cu…
Optimizes model training efficiency with core subset selection.
problem Escalating computational costs in large dataset training.
method Core subset selection for reweighting.
result Efficiently minimizes computational time and improves model performance.
New PFPPs based on rank-dependent utility for better performance control.
problem Improving performance prediction in systems with short-term control.
method Introduces rank-dependent PFPPs, solves integral equations via Volterra theory.
result Existence of rank-dependent PFPPs under specific market conditions.
In this paper a simple model for the evolution of the forward density of the future value of an asset is proposed. The model allows for a straightforward initial calibration to option prices and has dynamics that are consistent with empirical findings from option price data. The model is constructed with the aim of bei…
Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisitedstat.ME
Improves reliability diagrams for probabilistic forecasts.
problem Lack of stability in reliability diagrams hampered their use.
method CORP approach using non-parametric isotonic regression and PAV algorithm.
result Improved reliability diagrams with statistical consistency and reproducibility.
Supervised statistical classification is a vital tool for satellite image processing. It is useful not only when a discrete result, such as feature extraction or surface type, is required, but also for continuum retrievals by dividing the quantity of interest into discrete ranges. Because of the high resolution of mode…
Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins' properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability are necessary to facilitate efficient protein design. However, accuracy of predic…
Study shows uncertainty calibration improves BO performance, but not as much as model type.
problem Effect of model uncertainties on Bayesian optimization performance.
method Extensive study comparing different surrogate models and their uncertainty calibration.
result Gaussian Processes outperform other models in BO, and uncertainty calibration does not significantly improve regret.
Enhances NOAA's Geospace model with machine learning for predicting ground magnetic perturbations.
problem Predicting the probability of ground magnetic field changes.
method Combining physics-based model with machine learning (boosted ensemble of classification trees).
result The ML-enhanced model consistently improves probabilistic forecast metrics.
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysiseess.SP
Surveying low-cost sensors for air quality monitoring and calibration.
problem Limited spatial resolution due to expensive environmental monitoring stations.
method Low-cost sensors with machine learning for calibration.
result Machine learning improves sensor accuracy over time.
Magnetic particle imaging (MPI) data is commonly reconstructed using a system matrix acquired in a time-consuming calibration measurement. The calibration approach has the important advantage over model-based reconstruction that it takes the complex particle physics as well as system imperfections into account. This be…
New test detects differences in heterogeneous datasets.
problem Detecting differences between two samples with unknown heterogeneity.
method Developed a nonparametric testing procedure that handles latent heterogeneity through a composite null.
result The test accurately detects differences in the presence of unknown heterogeneity.
In statistical modelling the biggest threat is concept drift which makes the model gradually showing deteriorating performance over time. There are state of the art methodologies to detect the impact of concept drift, however general strategy considered to overcome the issue in performance is to rebuild or re-calibrate…
This work evaluates and benchmarks calibration metrics for data-driven regression models.
problem Conflicting results from different calibration metrics make it hard to compare and interpret model performance.
method Systematically extracted and benchmarked 14 regression calibration metrics across various data types and recalibration methods.
result Many metrics disagree on the same recalibration result, highlighting the need for careful metric selection.
FRESH combines patient-level and aggregate-level data for better clinical decision making.
problem Combining patient-level and aggregate-level data for clinical decision making.
method FRESH method that re-calibrates a patient-level model to match specified aggregate statistics.
result Unified data-efficient model for clinical decision making.
Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time tra…
We confirm the square-root law of market impact on Apple Inc. using a large dataset.
problem Testing the square-root law of market impact on a single U.S. large-cap equity.
method Using a full market-by-order feed, we reconstruct metaorders and calibrate impact using the square-root formula.
result The square-root law is confirmed with a prefactor of 0.34, consistent with worldwide data.