Title :
Complexity of simple nonlogarithmic loss functions
Author_Institution :
Helsinki Inst. for Inf. Technol., Finland
Abstract :
The loss complexity for nonlogarithmic loss functions is defined analogously to the stochastic complexity for logarithmic loss functions such that its mean provides an achievable lower bound for estimation, the mean taken with respect to the worst case data generating distribution. The loss complexity also provides a lower bound for the worst case mean prediction error for all predictors. For the important α-loss functions |y-ˆy|α, where y-yˆ denotes the prediction or fitting error and α is in the interval, an accurate asymptotic formula for the loss complexity is given.
Keywords :
computational complexity; function evaluation; parameter estimation; prediction theory; stochastic processes; asymptotic formula; fitting error; loss complexity; lower bound; nonlogarithmic loss functions; parameter estimation; prediction error; stochastic complexity; worst case data generating distribution; worst case mean prediction error; Autoregressive processes; Computer errors; Density functional theory; Density measurement; Entropy; Fitting; Information technology; Loss measurement; Stochastic processes; Upper bound;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2002.807281