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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]

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]