Showing papers by "Blake LeBaron published in 1997"
TL;DR: This short paper presents a fast algorithm for the BDS statistic, and outlines how these speed improvements are achieved.
Abstract: The BDS statistic has proved to be one of several useful nonlinear diagnostics. It has been shown to have good power against many nonlinear alternatives, and its asymptotic properties as a residual diagnostic are well understood. Furthermore, extensive Monte Carlo results have proved it useful in relatively small samples. However, the BDS test is not trivial to calculate, and is even more difficult to deal with if one wants the speed necessary to make bootstrap resampling feasible. This short paper presents a fast algorithm for the BDS statistic, and outlines how these speed improvements are achieved. Source code in the C programming language is included.
33 citations
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TL;DR: In this article, the authors present results from an experiemtal computer simulated stock market, where artificial intelligence algorithms take on the role of traders and make predictions about the future, and buy and sell stock an indicated by their expectations of future risk and return.
Abstract: This paper presents results from an experiemtal computer simulated stock market. In this market artificial intelligence algorithms take on the role of traders. They make predictions about the future, and buy and sell stock an indicated by their expectations of future risk and return.
5 citations
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TL;DR: This paper combines techniques drawn from the literature on evolutionary optimization algorithms along with bootstrap based statistical tests to create a network estimation and selection procedure which finds parsimonious network structures which generalize well.
Abstract: This paper combines techniques drawn from the literature on evolutionary optimization algorithms along with bootstrap based statistical tests. Bootstrapping is used as a general framework for estimating objectives out of sample by redrawing subsets from a training sample. Evolution is used to search the large number of potential network architectures. The combination of these two methods creates a network estimation and selection procedure which nds parsimonious network structures which generalize well. The bootstrap methodology also allows for objective functions other than usual least squares, since it can estimate the in sample bias for any function. Examples are given for forecasting chaotic time series contaminated with noise.
4 citations