Real‐time road traffic forecasting using regime‐switching space‐time models and adaptive LASSOKamarianakis, Yiannis; Shen, Wei; Wynter, Laura
2012 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.1937
Smart transportation technologies require real‐time traffic prediction to be both fast and scalable to full urban networks. We discuss a method that is able to meet this challenge while accounting for nonlinear traffic dynamics and space‐time dependencies of traffic variables. Nonlinearity is taken into account by a union of non‐overlapping linear regimes characterized by a sequence of temporal thresholds. In each regime, for each measurement location, a penalized estimation scheme, namely the adaptive absolute shrinkage and selection operator (LASSO), is implemented to perform model selection and coefficient estimation simultaneously. Both the robust to outliers least absolute deviation estimates and conventional LASSO estimates are considered. The methodology is illustrated on 5‐minute average speed data from three highway networks. Copyright © 2012 John Wiley & Sons, Ltd.
Test for dispersion constancy in stochastic differential equation modelsLee, Sangyeol; Guo, Meihui
2012 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.908
In this paper, we propose a constancy test for volatility in It ô processes based on discretely sampled data. The test statistic constitutes an integration of the Ljung–Box test statistic and the kurtosis statistic in the Jarque–Bera test. It is shown that under regularity conditions, the proposed test asymptotically follows a chi‐square distribution under the null hypothesis of constant volatility. To evaluate the test, empirical sizes and powers were examined through a simulation study. Analysis of real data including ultra‐high frequency transaction data and interest rates was also conducted for illustration. Copyright © 2011 John Wiley & Sons, Ltd.
How to choose the simulation model for computer experiments: a local approachMühlenstädt, Thomas; Gösling, Marco; Kuhnt, Sonja
2012 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.909
In many scientific areas, non‐stochastic simulation models such as finite element simulations replace real experiments. A common approach is to fit a meta‐model, for example a Gaussian process model, a radial basis function interpolation, or a kernel interpolation, to computer experiments conducted with the simulation model. This article deals with situations where more than one simulation model is available for the same real experiment, with none being the best over all possible input combinations. From fitted models for a real experiment as well as for computer experiments using the different simulation models, a criterion is derived to identify the locally best one. Applying this criterion to a number of design points allows the design space to be split into areas where the individual simulation models are locally superior. An example from sheet metal forming is analyzed, where three different simulation models are available. In this application and many similar problems, the new approach provides valuable assistance with the choice of the simulation model to be used. Copyright © 2011 John Wiley & Sons, Ltd.
Full and 1‐year runoff risk in the credibility‐based additive loss reserving methodMerz, Michael; Wüthrich, Mario V.
2012 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.915
In this paper, we consider the additive loss reserving (ALR) method in a Bayesian and credibility setup. The classical ALR method is a simple claims reserving method that combines prior information (e.g., premiums, number of contracts, market statistics) with claims observations. The Bayesian setup, which we present, in addition, allows for combining the information from a single runoff portfolio (e.g., company‐specific data) with the information from a collective (e.g., industry‐wide data) to analyze the claims reserves and the claims development result. However, in insurance practice, the associated distributions are usually unknown. Therefore, we do not follow the full Bayesian approach but apply credibility theory, which is distribution free and where we only need to know the first and second moments. That is, we derive the credibility predictors that minimize the expected squared loss within the class of affine‐linear functions of the observations (i.e., we derive linear Bayesian predictors). Using non‐informative priors, we link our credibility‐based ALR method to the classical ALR method and show that the credibility predictors coincide with the predictors in the classical ALR method. Moreover, we quantify the 1‐year risk and the full reserve risk by means of the conditional mean square error of prediction. Copyright © 2011 John Wiley & Sons, Ltd.