search

lanl / PyBNF

An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.
Other
22 stars 18 forks source link

Wild Bootstrap #261

Open emitra17 opened 5 years ago

emitra17 commented 5 years ago

Our current bootstrapping implementation is non-parametric bootstrapping (the normal kind), which works under the assumption that (x,y) pairs are independently drawn from a distribution.

A colleague suggested adding an option for the "wild bootstrap", which assumes the independent variable is at fixed values, and only the dependent variable is drawn from a distribution.

emitra17 commented 5 years ago

The best reference I've found for this is Givens and Hoeting Chapter 9 of Computational Statistics (2013). They call it "Bootstrapping Regression" (Section 9.2.3).

Briefly how it works: You do the fitting once. Each point has a best-fit estimate y_i_hat and an associated error epsilon_i_hat. Then your resampled data point y_i_resampled = y_i_hat + epsilon_j_hat, where epsilon_j_hat is the error associated with some other random point. You are "resampling the errors".