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In this paper we study the cure rate survival model involving a competitive risk structure with missing categorical covariates. A parametric distribution that can be written as a sequence of one-dimensional conditional distributions is specified for the missing covariates. We consider the missing data at random situation so that the missing covariates may depend only on the observed ones. Parameter estimates are obtained by using the EM algorithm via the method of weights. Extensive simulation studies are conducted and reported to compare estimates efficiency with and without missing data. As expected, the estimation approach taking into consideration the missing covariates presents much better efficiency in terms of mean square errors than the complete case situation. Effects of increasing cured fraction and censored observations are also reported. We demonstrate the proposed methodology with two real data sets. One involved the length of time to obtain a BS degree in Statistics, and another about the time to breast cancer recurrence. [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]

This paper proposed a novel two-level iterative reconstruction method for compressed sensing magnetic resonance imaging (CS-MRI). For the model of this method, we incorporated the phase correction matrix and region of support (ROS) to guarantee more accurate reconstruction. For the algorithm of the method, we proposed an iterative strategy to reduce memory and computation time, and a two-level strategy to take into account both low and high frequency k-space data separately. Simulation results on normal brain image and angiogram of cerebral demonstrated that the median square error of this proposed method was much less than the traditional method. The error reduction ratios are 11.94% for brain image and 4.53% for angiogram of cerebral, respectively. [ABSTRACT FROM AUTHOR]

In this paper we investigate a numerical algorithm for the pricing of swing options, relying on the so-called optimal quantization method. The numerical procedure is described in detail and numerous simulations are provided to assert its efficiency. In particular, we carry out a comparison with the Longstaff-Schwartz algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new registration algorithm of two-dimensional (2-D) color images and 3-D geometric models for the navigation system of a surgical robot. A 2-D–3-D registration procedure is used to precisely superimpose a tumor model on an endoscopic image and is, therefore, indispensable for the surgical navigation system. Thus, the performance of the 2-D–3-D registration procedure influences directly the usability of the surgical robot operating system. One of the typical techniques that has been developed is the use of external markers. However, the accuracy of this method is reduced by the breathing or heartbeat of the patient, as well as other unknown factors. For precise registration of 3-D models and 2-D images without external markers or special measurement devices, a new registration method is proposed that utilizes the 2-D images and their distance maps as created by the Fast Marching Method. Here, we present results of fundamental experiments performed using simulated models and actual images of the endoscopic operation. [ABSTRACT FROM AUTHOR]

Model selection is concerned with the choice of a mathematical model from a set of candidates that best describes a given set of experimental data. Large families of models arise in the context of structured mechanistic modelling in several application fields. In this situation the model selection problem cannot be solved by brute force testing of all possible models because of the high computational costs. However, more information on the different models of a family is available by their interdependencies, given by generalization or simplification relations. Large-scale model selection algorithms should exploit these relations for navigation in the discrete space of all model candidates. This paper presents a general approach for large-scale model selection by specifying the necessary computational primitives for navigating in large model families. As a non-trivial example it is shown how families of biochemical network models arising from the evaluation of stimulus response experiments are mapped to the general formalism. Finally, a first model selection algorithm based on the mentioned computational primitives is introduced and applied to complex biochemical network experiments. It is based on a load-balancing algorithm by making use of grid computing facilities. [ABSTRACT FROM AUTHOR]

Significant progress has been achieved in terms of both theory and industrial applications of iterative learning control (ILC) in the past decade. However, the techniques of solving non-linear ILC problems are still under development. The main result of this paper is a novel non-linear ILC algorithm that utilizes the capability of the Newton method. By setting up links between non-linear ILC problems and non-linear multivariable equations, the Newton method is introduced into the ILC framework. The implementation of the new algorithm allows one to decompose a nonlinear ILC problem into a sequence of linear time-varying ILC problems. Simulations on a discrete non-linear system and a manipulator model display its advantages. Conditions for its semi-local convergence are analysed. Links of ILC with existing non-linear topics are pointed out as ways to construct new non-linear ILC schemes. Potential improvements are discussed for future work. [ABSTRACT FROM AUTHOR]

This paper describes the simulation of described autonomous robots for search and rescue operations. The simulation system is utilized to perform experiments with various control strategies for the robot and team organizations, evaluating the comparative performance of the strategies and organizations. The objective of the robot team is, once deployed in an environment (floor-plan) with multiple rooms, to cover as many rooms as possible. The simulated robots are capable of navigating through the environment, and can communicate using simple messages. The simulator maintains the world, provides each robot with sensory information, and carries out the actions of the robots. The simulator keeps track of the rooms visited by robots and the elapsed time, in order to evaluate the performance of the robot teams. The robot teams are composed of homogenous robots, i.e., identical control strategies are used to generate the behavior of each robot in the team. The ability to deploy autonomous robots, as opposed to humans, in hazardous search and rescue missions could provide immeasurable benefits [ABSTRACT FROM AUTHOR]

This paper develops an automated approach to plan for mass tactical airborne operations. This proposed tool enables the user to properly load aircraft according to the mission and user specifications, so that the minimum amount of time is required to seize all assigned objectives. The methodology is based on a hybrid approach in which the first portion is a mathematical model that provides the optimal manifest under "perfect conditions." This mathematical model is represented by a transportation network, and can be optimized using a transportation algorithm. The optimum solution from the mathematical model is input to a simulation model that introduces the inherent variability induced by wind conditions, drift, aircraft location and speed, and delays between jumper exit times. The simulation returns the expected, best, and worst arrival times to the assigned objectives. This hybrid approach allows a large problem to be solved efficiently with a great deal of time saving. [ABSTRACT FROM AUTHOR]

The Steiner Problem in graphs is the problem of finding a set of edges (arcs) with minimum total weight which connects a given set of nodes in an edge-weighted graph (directed or undirected). This paper develops models for the directed Steiner tree problem on graphs. New and old models are examined in terms of their amenability to solution schemes based on Lagrangean relaxation. As a result, three alogrithms are presented and their performance compared on a number of problems originally tested by Beasley (1984, 1987) in the case of undirected graphs. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the joint distribution function of the event time and a continuous mark variable based on censored data. More specifically, the event time is subject to current status censoring and the continuous mark is only observed in case inspection takes place after the event time. The nonparametric maximum likelihood estimator in this model is known to be inconsistent. We propose and study an alternative likelihood-based estimator, maximising a smoothed log-likelihood, hence called a maximum smoothed likelihood estimator (MSLE). This estimator is shown to be well defined and consistent, and a simple algorithm is described that can be used to compute it. The MSLE is compared with other estimators in a small simulation study. [ABSTRACT FROM PUBLISHER]

In order to model the designer's autonomous task scheduling behaviour and use it in an agent-based product development process simulation, in this article a utility function is constructed, in which task urgency, task importance, agent individual preference and recovery cost are considered. Algorithms to calculate the utility function are developed and verified in detail. To validate and evaluate the scheduling behaviour based on utility functions in agent-based simulation, comparative simulation experiments are carried out on the basis of a case study in the Chinese industry. Simulation results show that the agent's behaviour in the development process simulation is close to the real working process of designers. In the comparative case study, the scheduling behaviour considering both task urgency and task importance can significantly shorten project lead time compared with considering task urgency only. It can also save time for the whole project if the agent assigns collaborative tasks high priority. The developed model and simulation approach can help organisations to identify efficient and effective scheduling behaviours in collaborative and complex product development processes. [ABSTRACT FROM PUBLISHER]

Surrogate-based modeling is an effective search method for global design optimization over well-defined areas using complex and computationally intensive analysis and simulation tools. However, indentifying the appreciate surrogate models and their suitable areas remains a challenge that requires extensive human intervention. In this work, a new global optimization algorithm, namely Mixed Surrogate and Space Elimination (MSSE) method, is introduced. Representative surrogate models, including Quadratic Response Surface, Radial Basis function, and Kriging, are mixed with different weight ratios to form an adaptive metamodel with best tested performance. The approach divides the field of interest into several unimodal regions; identifies and ranks the regions that likely contain the global minimum; fits the weighted surrogate models over each promising region using additional design experiment data points from Latin Hypercube Designs and adjusts the weights according to the performance of each model; identifies its minimum and removes the processed region; and moves to the next most promising region until all regions are processed and the global optimum is identified. The proposed algorithm was tested using several benchmark problems for global optimization and compared with several widely used space exploration global optimization algorithms, showing reduced computation efforts, robust performance and comparable search accuracy, making the proposed method an excellent tool for computationally intensive global design optimization problems. [ABSTRACT FROM AUTHOR]

This study develops more computationally efficient versions of the TwO-Moment Aerosol Sectional (TOMAS) microphysics algorithms, collectively called “Fast TOMAS.” Several methods for speeding up the algorithm were attempted, but only reducing the number of size sections was adopted. Fast TOMAS models, coupled to the GISS GCM II-prime, require a new coagulation algorithm with less restrictive size resolution assumptions but only minor changes in other processes. Fast TOMAS models have been evaluated in a box model against analytical solutions of coagulation and condensation and in a 3-D model against the original TOMAS (TOMAS-30) model. Condensation and coagulation in the Fast TOMAS models agree well with the analytical solution but show slightly more bias than the TOMAS-30 box model. In the 3-D model, errors resulting from decreased size resolution in each process (i.e., emissions, cloud processing/wet deposition, microphysics) are quantified in a series of model sensitivity simulations. Errors resulting from lower size resolution in condensation and coagulation, defined as the microphysics error, affect number and mass concentrations by only a few percent. The microphysics error in CN70/CN100 (number concentrations of particles larger than 70/100 nm diameter), proxies for cloud condensation nuclei, range from –5% to 5% in most regions. The largest errors are associated with decreasing the size resolution in the cloud processing/wet deposition calculations, defined as cloud-processing error, and range from –20% to 15% in most regions for CN70/CN100 concentrations. Overall, the Fast TOMAS models increase the computational speed by 2 to 3 times with only small numerical errors stemming from condensation and coagulation calculations when compared to TOMAS-30. The faster versions of the TOMAS model allow for the longer, multi-year simulations required to assess aerosol effects on cloud lifetime and precipitation. Copyright 2012 American Association for Aerosol Research [ABSTRACT FROM PUBLISHER]

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method. [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]

An explicit one-dimensional transient numerical model has been built for a single-borehole ground heat exchanger, and two computing algorithms are given. The outlet temperature of the U-tube and the soil temperature can be predicted by using arbitrary time-varying load or inlet temperatures as inputs. This numerical model has been compared with analytical models and validated by using test data of three boreholes. Conclusions are that this numerical model is considerably accurate and efficient; the computational time of a one-year-period simulation is about 52 s; recommended is the discrete scheme with 60-s step time and spatial increment of 0.033-0.067 m. [ABSTRACT FROM AUTHOR]

A procedure for estimating a time series model with structural change is proposed. Nonparametric bootstrap (block bootstrap or AR sieve) is applied to a series of estimates obtained through a modified forward search (FS) algorithm. The FS algorithm is implemented with overlapping and independent blocks of time points. The procedure can mitigate the difficulty in estimating when there is a temporary structural change. The simulation study indicated robustness of estimates from the estimation method when temporary structural change is introduced into the model provided that the time series is fairly long. As the effect of structural change persists in a longer period, the robustness of the bootstrap methods is further emphasized. We also provided a procedure for detecting the structural change and the subsequent adjustment of the overall model if indeed, there is a structural change. [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]

We analyse the convergence properties of the Longstaff-Schwartz algorithm for approximately solving optimal stopping problems that arise in the pricing of American (Bermudan) financial options. Based on a new approximate dynamic programming principle error propagation inequality, we prove sample complexity error estimates for this algorithm for the case in which the corresponding approximation spaces may not necessarily possess any linear structure at all and may actually be any arbitrary sets of functions, each of which is uniformly bounded and possesses finite VC-dimension, but is not required to satisfy any further material conditions. In particular, we do not require that the approximation spaces be convex or closed, and we thus significantly generalize the results of Egloff, Clement et al., and others. Using our error estimation theorems, we also prove convergence, up to any desired probability, of the algorithm for approximating sets defined using L2 orthonormal bases, within a framework depending subexponentially on the number of time steps. In addition, we prove estimates on the overall convergence rate of the algorithm for approximation spaces defined by polynomials. [ABSTRACT FROM AUTHOR]

LASSO is a useful method for achieving both shrinkage and variable selection simultaneously. The main idea of LASSO is to use the L1 constraint in the regularization step which has been applied to various models such as wavelets, kernel machines, smoothing splines, and multiclass logistic models. We call such models with the L1 constraint generalized LASSO models. In this article, we propose a new algorithm called the gradient LASSO algorithm for generalized LASSO. The gradient LASSO algorithm is computationally more stable than QP-based algorithms because it does not require matrix inversions, and thus it can be more easily applied to high-dimensional data. Simulation results show that the proposed algorithm is fast enough for practical purposes and provides reliable results. To illustrate its computing power with high-dimensional data, we analyze multiclass microarray data using the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and traditionally used methods of item selection is also presented. It is shown that the ACO algorithm outperforms the largest factor loadings and maximum test information item selection methods. The results demonstrate the capabilities of using ACO for creating short-form scales. [ABSTRACT FROM AUTHOR]

The objective of this research is to study the relationship between terrain complexity and terrain analysis results from grid‐based digital elevation models (DEMs). The impact of terrain complexity represented by terrain steepness and orientation on derived parameters such as slope and aspect has been analysed. Experiments have been conducted to quantify the uncertainties created by digital terrain analysis algorithms. The test results show that (a) the RMSE of derived slope and aspect is negatively correlated with slope steepness; (b) the RMSE of derived aspect is more sensitive to terrain complexity than that of derived slope; and (c) the uncertainties in derived slope and aspect tend to be found in flatter areas, and decrease with increasing terrain complexity. The study shows that although primary surface parameters can be well defined mathematically, the implementation of those mathematical models in a GIS environment may generate considerable uncertainties related to terrain complexity. In general, when terrain is rugged with steep slopes, the uncertainty of derived parameters is quite minimal. While in flatter areas, the DEM‐based derivatives, particularly the aspect, may contain a great amount of uncertainty, causing significant limitation in applying the analytical results. [ABSTRACT FROM AUTHOR]

A new nonlinear robust control scheme of permanent magnet linear synchronous motors (PMLSM) is proposed in this article. A quasi-linearized and decoupled model with uncertainties is derived by the mathematical model of PMLSM according to its characteristic. A fixed-boundary sliding mode controller using the m sat function is designed to guarantee the robustness and remove the chattering that usually exists in normal sliding mode control. Design of a force observer is given to estimate the load force unknown in the new model. The validity of the proposed algorithm compared with the conventional PID control scheme is proved by MATLAB simulation results and DSP-based experimental results. [ABSTRACT FROM AUTHOR]

Markov chain Monte Carlo (MCMC) routines have become a fundamental means for generating random variates from distributions otherwise difficult to sample. The Hastings sampler, which includes the Gibbs and Metropolis samplers as special cases, is the most popular MCMC method. A number of implementations are available for running these MCMC routines varying in the order through which the components or blocks of the random vector of interest X are cycled or visited. The two most common implementations are the deterministic sweep strategy, whereby the components or blocks of X are updated successively and in a fixed order, and the random sweep strategy, whereby the coordinates or blocks of X are updated in a randomly determined order. In this article, we present a general representation for MCMC updating schemes showing that the deterministic scan is a special case of the random scan. We also discuss decision criteria for choosing a sweep strategy. [ABSTRACT FROM AUTHOR]