parametertracking
Parametertracking, in statistics and engineering, denotes the ongoing estimation and updating of model parameters as new data become available or as the underlying system evolves over time. It intentionally treats parameters as potentially time-varying rather than fixed, to reflect non-stationary environments.
Common approaches include recursive estimation techniques such as recursive least squares, and state-space methods using the
Applications span finance, engineering, and data science. In finance, parameter tracking helps adapt pricing and risk
Challenges include selecting an appropriate dynamics model for parameter evolution, avoiding excessive sensitivity to noise, preventing
Related concepts include dynamic parameter estimation, time-varying parameter models, adaptive control, Kalman filtering, recursive least squares,