Naïve Nonparametric Bootstrap Model Weights Are Biased

Näıve Nonparametric Bootstrap Model Weights

The plausibility of competing statistical models may be assessed using penalized log-likelihood criteria such as the AIC, which is given by AIC = −2lnL + 2k (L being the maximum likelihood estimate and k the number of free parameters). The raw AIC values can be transformed to AIC model weights by wi = exp(− 2∆AICi)/ ∑R r=1 exp(− 2∆AICr), where ∆AICi = AICi − min(AIC) and R is the total number o...

Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm

This paper presents approximate confidence intervals for each function of parameters in a Banach space based on a bootstrap algorithm. We apply kernel density approach to estimate the persistence landscape. In addition, we evaluate the quality distribution function estimator of random variables using integrated mean square error (IMSE). The results of simulation studies show a significant impro...

Bootstrap Methods for the Nonparametric

A completely nonparametric approach to population bioequivalence in crossover trials has been suggested by Munk and Czado (1999). It is based on the Mallows (1972) metric as a nonparametric distance measure which allows the comparison between the entire distribution functions of test and reference formulations. It was shown that a separation between carry-over and period eeects is not possible ...

The nonparametric bootstrap for the current status model

Gradient Weights help Nonparametric Regressors

where An,i(X) ≡ we are confident in both estimates fn,h(X ± tei). •Fast preprocessing, and online: just 2 estimates of fn,h at X . Metric learning optimizes over a space of possible metrics. •Only 2× 2× 1 param. search grid to adapt to d dimensions. KR with bandwidths {hi}d1 needs d× d param. search grid. •General: preprocessing for any distance-based regressor. Other methods apply to particula...