A New Pearson-Type QMLE for Conditionally Heteroscedastic Models

This article proposes a novel Pearson-type quasi-maximum likelihood estimator (QMLE) of GARCH(p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non-Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not just the heavy-tailed but also the skewed innovations. Under strict stationarity and some weak moment conditions, the strong consistency and asymptotic normality of the PQMLE are obtained. With no further efforts, the PQMLE can be applied to other conditionally heteroscedastic models. A simulation study is carried out to assess the performance of the PQMLE. Two applications to four major stock indexes and two exchange rates further highlight the importance of our new method. Heavy-tailed and skewed innovations are often observed together in practice, and the PQMLE now gives us a systematic way to capture these two coexisting features. © 2015 American Statistical Association.postprin

A dynamic priority-based approach to concurrent toolpath planning for multi-material layered manufacturing

This paper presents an approach to concurrent toolpath planning for multi-material layered manufacturing (MMLM) to improve the fabrication efficiency of relatively complex prototypes. The approach is based on decoupled motion planning for multiple moving objects, in which the toolpaths of a set of tools are independently planned and then coordinated to deposit materials concurrently. Relative tool positions are monitored and potential tool collisions detected at a predefined rate. When a potential collision between a pair of tools is detected, a dynamic priority scheme is applied to assign motion priorities of tools. The traverse speeds of tools along the x-axis are compared, and a higher priority is assigned to the tool at a higher traverse speed. A tool with a higher priority continues to deposit material along its original path, while the one with a lower priority gives way by pausing at a suitable point until the potential collision is eliminated. Moreover, the deposition speeds of tools can be adjusted to suit different material properties and fabrication requirements. The proposed approach has been incorporated in a multi-material virtual prototyping (MMVP) system. Digital fabrication of prototypes shows that it can substantially shorten the fabrication time of relatively complex multi-material objects. The approach can be adapted for process control of MMLM when appropriate hardware becomes available. It is expected to benefit various applications, such as advanced product manufacturing and biomedical fabrication. © 2010 Elsevier Ltd. All rights reserved.postprin

A new estimation method for multivariate Markov chain model with application in demand predictions

In this paper, we propose a new estimation method for the parameters of a multivariate Markov chain model. In the new method, we calculate the correlations of the sequences first and establish multivariate Markov chain models for those positively correlated sequences. The parameters are estimated by minimizing the error of prediction. We apply the method to demand predictions for a soft-drink company in Hong Kong. Numerical experiments are given to show the effectiveness of our proposed method. © 2010 IEEE.published_or_final_versionThe 3rd International Conference on Business Intelligence and Financial Engineering (BIFE 2010), Hong Kong, 13-15 August 2010. In Proceedings of the 3rd BIFE, 2010, p. 126-13

A bootstrapped spectral test for adequacy in weak ARMA models

This paper proposes a Cramér-von Mises (CM) test statistic to check the adequacy of weak ARMA models. Without posing a martingale difference assumption on the error terms, the asymptotic null distribution of the CM test is obtained. Moreover, this CM test is consistent, and has nontrivial power against the local alternative of order n-1/2. Due to the unknown dependence of error terms and the estimation effects, a new block-wise random weighting method is constructed to bootstrap the critical values of the test statistic. The new method is easy to implement and its validity is justified. The theory is illustrated by a small simulation study and an application to S&P 500 stock index. © 2015 Elsevier B.V.postprin

An auction-based approach with closed-loop bid adjustment to dynamic task allocation in robot teams

Dynamic task allocation is among the most difficult issues in multi-robot coordination, although it is imperative for a multitude of applications. Auction-based approaches are popular methods that allocate tasks to robots by assembling team information at a single location to make practicable decisions. However, a main deficiency of auction-based methods is that robots generally do not have sufficient information to estimate reliable bids to perform tasks, particularly in dynamic environments. While some techniques have been developed to improve bidding, they are mostly open-looped without feed-back adjustments to tune the bid prices for subsequent tasks of the same type. Robots' bids, if not assessed and adjusted accordingly, may not be trustworthy and would indeed impede team performance. To address this issue, we propose a closed-loop bid adjustment mechanism for auction-based multi-robot task allocation, with an aim to evaluate and improve robots' bids, and hence enhance the overall team performance. Each robot in a team maintains and uses its own track record as closed-loop feedback information to adjust and improve its bid prices. After a robot has completed a task, it assesses and records its performance to reflect the discrepancy between the bid price and the actual cost of the task. Such performance records, with time-discounting factors, are taken into account to damp out fluctuations of bid prices. Adopting this adjustment mechanism, a task would be more likely allocated to a competent robot that submits a more accurate bid price, and hence improve the overall team performance. Simulation of task allocation of free-range automated guided vehicles serving at a container terminal is presented to demonstrate the effectiveness of the adjustment mechanism.postprintThe World Congress on Engineering (WCE 2011), London, U.K., 6-8 July 2011. In Proceedings of WCE, 2011, v. 2, p. 1061-106

A multi-material virtual prototyping system for biomedical applications

This paper describes a multi-material virtual prototyping (MMVP) system for modelling and digital fabrication of discrete and functionally graded multi-material objects for biomedical applications. The MMVP system consists of a DMMVP module, an FGMVP module, and a virtual reality (VR) simulation module. The DMMVP module is used for design and process planning of discrete multi-material (DMM) objects, while the FGMVP module is for functionally graded multimaterial (FGM) objects. The VR simulation module integrates these two modules to perform digital fabrication of multimaterial objects, which can be subsequently visualized and analyzed in a virtual environment to optimize MMLM processes for fabrication of product prototypes. Using the MMVP system, two biomedical objects, including a human dextrocardic heart made of discrete multi-materials and a hip joint assembly of FGM are modelled and digitally fabricated for visualization and analysis in a VR environment. These studies show the MMVP system is a practical tool for modelling, visualization, process planning, and subsequent fabrication of biomedical objects of discrete and functionally graded multi-materials for biomedical applications. ©2009 IEEE.published_or_final_versionThe IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems (VECIMS) 2009, Hong Kong, 11-13 May 2009. In Proceedings of the IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems, 2009, p. 73-7

Testing for the Buffered Autoregressive Processes

published_or_final_versio

Variable selection in robust joint mean and covariance model for longitudinal data analysis

In longitudinal data analysis, a correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this article, we consider robust variable selection method in a joint mean and covariance model. We propose a set of penalized robust generalized estimating equations to simultaneously estimate the mean regression coefficients, the generalized autoregressive coefficients, and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, we develop the oracle property of the proposed robust variable selection method. Finally, a simulation study and a detailed data analysis are carried out to assess and illustrate the small sample performance; they show that the proposed method performs favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model.published_or_final_versio

Asset allocation under regime-switching models

We discuss an optimal asset allocation problem in a wide class of discrete-time regime-switching models including the hidden Markovian regime-switching (HMRS) model, the interactive hidden Markovian regime-switching (IHMRS) model and the self-exciting threshold autoregressive (SETAR) model. In the optimal asset allocation problem, the object of the investor is to select an optimal portfolio strategy so as to maximize the expected utility of wealth over a finite investment horizon. We solve the optimal portfolio problem using a dynamic programming approach in a discrete-time set up. Numerical results are provided to illustrate the practical implementation of the models and the impacts of different types of regime switching on optimal portfolio strategies. © 2012 IEEE.published_or_final_versio

Interplay between elastic fields due to gravity and a partial dislocation for a hard-sphere crystal coherently grown under gravity: driving force for defect disappearance

We previously observed that an intrinsic staking fault shrunk through a glide of a Shockley partial dislocation terminating its lower end in a hard-sphere crystal under gravity coherently grown in by Monte Carlo simulations [Mori et al., Molec. Phys. 105, 1377 (2007)]; it was an answer to a one-decade long standing question why the stacking disorder in colloidal crystals reduced under gravity [Zhu et al., Nature 387, 883 (1997)]. Here, we present an elastic energy calculation; in addition to the self-energy of the partial dislocation [Mori et al., Prog. Theor. Phys. Suppl. 178, 33 (2009)] we calculate the cross-coupling term between elastic field due to gravity and that due to a Shockley partial dislocation. The cross term is a increasing function of the linear dimension R over which the elastic field expands, showing that a driving force arises for the partial dislocation moving toward the upper boundary of a grain.Comment: 8pages, 4figures, to be published in Molecular Physic