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Global Optimization in Any Minkowski Metric: A Permutation-Translation Simulated Annealing Algorithm for Multidimensional Scaling 总被引:3,自引:1,他引:2
It is well known that considering a non-Euclidean Minkowski metric in Multidimensional Scaling, either for the distance model
or for the loss function, increases the computational problem of local minima considerably. In this paper, we propose an algorithm
in which both the loss function and the composition rule can be considered in any Minkowski metric, using a multivariate randomly
alternating Simulated Annealing procedure with permutation and translation phases. The algorithm has been implemented in Fortran
and tested over classical and simulated data matrices with sizes up to 200 objects. A study has been carried out with some
of the common loss functions to determine the most suitable values for the main parameters. The experimental results confirm
the theoretical expectation that Simulated Annealing is a suitable strategy to deal by itself with the optimization problems
in Multidimensional Scaling, in particular for City-Block, Euclidean and Infinity metrics. 相似文献
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The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out‐of‐sample forecasting ability of the SETAR‐Threshold GARCH (SETAR‐TGARCH) and the SETAR‐Threshold Stochastic Volatility (SETAR‐THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ‘true’ underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR‐THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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