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In this paper, we propose a method for predicting the distributions of people's trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people's spatial and temporal mobility patterns, the complexity of a city level's road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method. [ABSTRACT FROM AUTHOR]

The self-updating process (SUP) is a clustering algorithm that stands from the viewpoint of data points and simulates the process how data points move and perform self-clustering. It is an iterative process on the sample space and allows for both time-varying and time-invariant operators. By simulations and comparisons, this paper shows that SUP is particularly competitive in clustering (i) data with noise, (ii) data with a large number of clusters, and (iii) unbalanced data. When noise is present in the data, SUP is able to isolate the noise data points while performing clustering simultaneously. The property of the local updating enables SUP to handle data with a large number of clusters and data of various structures. In this paper, we showed that the blurring mean-shift is a static SUP. Therefore, our discussions on the strengths of SUP also apply to the blurring mean-shift. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new forecasting algorithm for value-at-risk (VaR) based on ARMA-GARCH (autoregressive moving average-generalized autoregressive conditional heteroskedastic) models whose innovations follow a Gaussian mixture distribution. For the parameter estimation, we employ the conditional least squares and quasi-maximum-likelihood estimator (QMLE) for ARMA and GARCH parameters, respectively. In particular, Gaussian mixture parameters are estimated based on the residuals obtained from the QMLE of GARCH parameters. Our algorithm provides a handy methodology, spending much less time in calculation than the existing resampling and bias-correction method developed in Hartz et al. [Accurate value-at-risk forecasting based on the normal-GARCH model, Comput. Stat. Data Anal. 50 (2006), pp. 3032-3052]. Through a simulation study and a real-data analysis, it is shown that our method provides an accurate VaR prediction. [ABSTRACT FROM AUTHOR]

In a changing climate, changes in timing of seasonal events such as floods and flowering should be assessed using circular methods. Six different methods for clustering on a circle and one linear method are compared across different locations, spreads, and sample sizes. Best results are obtained when clusters are well separated and the number of observations in each cluster is approximately equal. Simulations of flood-like distributions are used to assess and explore clustering methods. Generally, k-means provides results that are close to the expected results, some other methods perform well under specific conditions, but no single method is exemplary. [ABSTRACT FROM AUTHOR]

We investigate three interval estimators for binomial misclassification rates in a complementary Poisson model where the data are possibly misclassified: a Wald-based interval, a score-based interval, and an interval based on the profile log-likelihood statistic. We investigate the coverage and average width properties of these intervals via a simulation study. For small Poisson counts and small misclassification rates, the intervals can perform poorly in terms of coverage. The profile log-likelihood confidence interval (CI) is often proved to outperform the other intervals with good coverage and width properties. Lastly, we apply the CIs to a real data set involving traffic accident data that contain misclassified counts. [ABSTRACT FROM AUTHOR]

Although there has been significant research on modelling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set. Specifically, we focus on the depth of objects (i.e. preferences for specific attribute values over others) and on the diversity of sets (i.e. preferences for broad vs. narrow distributions of attribute values). The DD-PREF framework is general and can incorporate additional object- and set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD-PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences, a real-world music playlist collection, and rover image data gathered in desert training exercises. [ABSTRACT FROM AUTHOR]

A new method for detecting the parameter changes in generalized autoregressive heteroskedasticity GARCH (1,1) model is proposed. In the proposed method, time series observations are divided into several segments and a GARCH (1,1) model is fitted to each segment. The goodness-of-fit of the global model composed of these local GARCH (1,1) models is evaluated using the corresponding information criterion (IC). The division that minimizes IC defines the best model. Furthermore, since the simultaneous estimation of all possible models requires huge computational time, a new time-saving algorithm is proposed. Simulation results and empirical results both indicate that the proposed method is useful in analysing financial data. [ABSTRACT FROM AUTHOR]

Sequential application of a statistical test is usually applied in an automated auditory response detection algorithm. The sequential test strategy is very time-efficient but increases the probability of a false rejection of the null-hypothesis. For this reason, it is necessary to correct the critical test value. However, the well-known Bonferroni correction leads to an over-correction when dealing with dependent or partly dependent data. The objective of the study reported here was to develop a method to determine the critical test value for the sequential testing of dependent data. Extensive Monte Carlo simulations were used to develop this method. The simulation results were reviewed and the benefit of the suggested method, in comparison to the Bonferroni correction, was shown using a large sample of real amplitude modulation following response data. The detection rate determined for these data and the ROC curve demonstrate the advantage of using the method suggested here. [ABSTRACT FROM AUTHOR]