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Zero-inflated regression models are generally accepted for modeling data with inflated amounts of zeros. The principle used in constructing the distribution for these models has been extended to modeling certain types of data with inflation at the upper bound of the support. An excellent example of this is the zero and κ-inflated truncated Poisson regression model which utilizes the Poisson distribution. However, the equi-dispersion nature of the Poisson distribution makes the model less suitable for data that are under-dispersed and over-dispersed. Models based on exponentiated exponential geometric distribution (EEGD) are presented as alternatives for modeling any count data with an upper bound, irrespective of the nature of dispersion. The models are obtained by truncating the EEGD at the top and inflating at either or both zero and κ. A simulation study is conducted to examine the performances of the models under certain conditions. The result shows that the closer the dispersion parameter c is from the equi-dispersion line, the better the estimates for over-dispersed data, while the farther the dispersion parameter from the equi-dispersed line, the better the estimates for an under-dispersed model. The application of the models is illustrated using data from the Behavioral Risk Factor Surveillance System 2011. [ABSTRACT FROM AUTHOR]

The operation of machine tools often demands a highly accurate knowledge of the tool center point's (TCP) position. The displacement of the TCP over time can be inferred from coupled thermal models, which comprise a set of geometrically coupled heat equations. Each of these equations represents the temperature in part of the machine, and they are often formulated on complicated geometries. The accuracy of the TCP prediction depends highly on the accuracy of the model parameters, such as heat exchange parameters, and the initial temperature. Thus it is of utmost interest to determine the influence of these parameters on the TCP displacement prediction. In turn, the accuracy of the parameter estimate is essentially determined by the measurement accuracy and the sensor placement. Determining the accuracy of a given sensor configuration is a key prerequisite of optimal sensor placement. In this paper, we develop a thermal model for the discretization of realistic machine tool. On top of this model we propose two numerical algorithms to evaluate any given thermal sensor configuration with respect to its accuracy. We compute the posterior variances from the posterior covariance matrix with respect to an uncertain initial temperature field. The full matrix is dense and potentially very large, depending on the size of the discretized model. We first present a way to compute this approximation, which relies on the computation of the model sensitivities with respect to the initial values. Additionally, we present a low-rank tensor method which exploits the underlying system structure. We compare the efficiency of both algorithms with respect to runtime and memory requirements and discuss their respective advantages with regard to optimal sensor placement problems. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for the accurate estimation of the derivative (aka. sensitivity) of expectations of functions involving an indicator function by modifying a (stochastic) algorithmic differentiation, replacing the derivative of the indicator function with a suitable operator. We show that we can split this operator into a conditional expectation operator and the density. This allows using different or improved numerical approximation methods for these operators, e.g. regression. The method is an improvement of the approach presented in C.P. Fries [Automatic backward differentiation for American Monte-Carlo algorithms (conditional expectation). Risk, April, 2018; Stochastic automatic differentiation: Automatic differentiation for Monte-Carlo simulations, Quant. Finance 19(6) (2019), pp. 1043–1059]. The finite difference approximation of a partial derivative of a Monte-Carlo integral of a discontinuous function is known to exhibit a high Monte-Carlo error. The issue is evident since the Monte-Carlo approximation of a discontinuous function is just a finite sum of discontinuous functions and as such, not even differentiable. The algorithmic differentiation of a discontinuous function is problematic. A natural approach is to replace the discontinuity by continuous functions. This is equivalent to replacing a path-wise automatic differentiation by a (local) finite difference approximation. The decoupling of the integration of the Dirac delta and the remaining conditional expectation introduced here results in an improvement in terms of variance reduction and implementation design. [ABSTRACT FROM AUTHOR]

For solving the inverse Cauchy problem of a three-dimensional (3D) non-homogeneous sideways heat equation in a cuboid, we first transform it into a partial homogeneous boundary value problem, using a time-dependent two-dimensional (2D) homogenization technique. After a time step-by-step approach of the inverse Cauchy problem, it becomes an inverse Cauchy problem of the 3D modified Helmholtz equation. A sequence of 2D sine functions and a set of second-order ordinary differential equations are derived to determine the expansion coefficients, which can be solved in closed-form. In order to stably solve the inverse Cauchy problem, we simply introduce a spring-damping regularization technique to modify the coefficients, which results in a regularized 2D Fourier sine series solution of the unknown Dirichlet data on the top of the cuboid. The novel method can find the solution in the whole domain, which is quite accurate with small errors, even a large noise up to 20 % is imposed on the given data. The present method together with the analyses of regularization parameter and the stability of solution is novel and shows promise in the 3D sideways problems of the heat equation. [ABSTRACT FROM AUTHOR]

We give an explicit representation for the transition law of a tempered stable Ornstein–Uhlenbeck process and use it to develop a rejection sampling algorithm for exact simulation of increments from this process. Our results apply to general classes of both univariate and multivariate tempered stable distributions and contain a number of previously studied results as special cases. [ABSTRACT FROM AUTHOR]

In the progressive Type-II censoring with random removal, choosing a distribution for removing the number of unfailed units which is independent from the lifetime distribution, may be a serious challenge and lead to an unreliable inference. Two approaches based on the time distance between consecutive failure times are proposed to determine the removal numbers. Then, the joint probability mass functions of random removals are derived where the underlying distribution of the lifetime data is assumed to be an exponential. Furthermore, The expected test time is investigated under these two approaches and is subsequently considered as an optimality criterion for identifying progressive Type-II censoring schemes. Some statistical inference results are also obtained by using Monte Carlo simulations. The empirical results indicate that the proposed methods work perfectly well in the sense that, reduce the expected total time on the test. Finally, a numerical example, based on a real data set, is presented and analysed to illustrate the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper presents an improved method for applying smoothed particle hydrodynamics within a nested Lagrangian domain of fluid particles. In our previous implementation, ghost particles, generated by Poisson-disk sampling to enclose the region occupied by fluid particles, transferred necessary physical quantities from the outer domain to the fluid. Using this technique, the local fluid motion agreed with results simulated by SPH over the entire domain. However, the cost of generating ghost particles was burdensome. We propose herein a much less expensive, patch-based sampling method to generate ghost particles. In this new approach, the ghost particles are generated locally around each fluid particle and corresponding physical quantities are determined using the local ghost particles. Furthermore, we introduce a new formulation that determines the physical quantities of ghost particles from the outer domain, and transfers them to the local fluid particles. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by α-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilize computer simulation algorithm in MATLAB to visualize the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modeling structure of stochastic differential equations. [ABSTRACT FROM AUTHOR]

In the present paper, we discuss algorithms of record generation when records are taken from a normal population. We propose three new generation algorithms, compare their efficiency and find the most efficient algorithm (Algorithm 2.1). We then compare these algorithms with known generation algorithms presented in the work of Balakrishnan, So, and Zhu (2016). [ABSTRACT FROM AUTHOR]

In the present paper, we discuss algorithms of generation of weak records. These generation algorithms are based on two different methods. In the case, when the inverse function for the underlying distribution function can be obtained explicitly, the corresponding generation algorithms are built on the inverse-transform method. In the case, when the inverse function cannot be obtained explicitly, the algorithms are based on the rejection method. Generation algorithms of our paper are supplied with illustrative examples. [ABSTRACT FROM AUTHOR]

In this article, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy-tailed alternative to the normal distribution.We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution. [ABSTRACT FROM AUTHOR]

This paper deals with the numerical solution of Volterra-Fredholm integral equations. In this work, we approximate the unknown functions based on the shifted orthonormal Bernstein polynomials, in conjunction with the least-squares approximation method. The method is using a simple computational manner to obtain a quite acceptable approximate solution. The merits of this method lie in the fact that, on the one hand, the problem will be reduced to a system of algebraic equations. On the other hand, the efficiency and accuracy of the method for solving these equations are high. The convergence analysis of proposed method have been discussed through some theorems. Moreover, we will obtain an estimation of error bound for this algorithm. Finally, some examples are given to show the capability of presented method in comparison with four well-known algorithms in the literature namely the Legendre collocation method, Taylor collocation method, Taylor polynomial method and Lagrange collocation method. [ABSTRACT FROM AUTHOR]

We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectation-maximization variable selection (EMVS) with a deterministic annealing variant as the platform for our method, due to its proven flexibility and efficiency. We propose a variance adjustment of the priors for the coefficients of qualitative covariates, which controls false-positive rates, and a flexible parameterization for interaction terms, which accommodates user-specified heredity constraints. This method can handle all pairwise interaction terms as well as a subset of specific interactions. Using simulation, we show that this method selects associated covariates better than the grouped LASSO and the LASSO with heredity constraints in various exploratory research scenarios encountered in epidemiological studies. We apply our method to identify genetic and non-genetic risk factors associated with smoking experimentation in a cohort of Mexican-heritage adolescents. [ABSTRACT FROM AUTHOR]

In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributions. We give the expectation–maximization (EM) algorithm to obtain the maximum likelihood (ML) estimators for the parameters of interest. We also analyze the mixture regression model based on the SLN distribution and provide the ML estimators of the parameters using the EM algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples. [ABSTRACT FROM PUBLISHER]

In this study, for two-dimensional Maxwell's equations, an efficient preconditioned generalized minimum residual method on the graphics processing unit (GPUPGMRES) is proposed to obtain numerical solutions of the equations that are discretized by a multisymplectic Preissmann scheme. In our proposed GPUPGMRES, a novel sparse matrix–vector multiplication (SpMV) kernel is suggested while keeping the compressed sparse row (CSR) intact. The proposed kernel dynamically assigns different number of rows to each thread block, and accesses the CSR arrays in a fully coalesced manner. This greatly alleviates the bottleneck of many existing CSR-based algorithms. Furthermore, the vector-operation and inner-product decision trees are automatically constructed. These kernels and their corresponding optimized compute unified device architecture parameter values can be automatically selected from the decision trees for vectors of any size. In addition, using the sparse approximate inverse technique, the preconditioner equation solving falls within the scope of SpMV. Numerical results show that our proposed kernels have high parallelism. GPUPGMRES outperforms a recently proposed preconditioned GMRES method, and a preconditioned GMRES implementation in the AmgX library. Moreover, GPUPGMRES is efficient in solving the two-dimensional Maxwell's equations. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to perform sensitivity analysis to the specification of prior distributions in a Bayesian analysis setting of STAR models. To achieve this aim, the joint posterior distribution of model order, coefficient, and implicit parameters in the logistic STAR model is first being presented. The conditional posterior distributions are then shown, followed by the design of a posterior simulator using a combination of Metropolis-Hastings, Gibbs Sampler, RJMCMC, and Multiple Try Metropolis algorithms, respectively. Following this, simulation studies and a case study on the prior sensitivity for the implicit parameters are being detailed at the end. [ABSTRACT FROM AUTHOR]

This article simulates the stationary M/M/1 retrial queues using Simple Random Sampling (SRS) and Refined Descriptive Sampling (RDS) to generate input variables. We design and realize a software under Linux using C language which establishes the performance measures of the M/M/1 retrial queues, computes their relative deviation and their variance reductions in order to compare both sampling methods. The simulation results demonstrate that RDS produces more accurate and efficient point estimates of the true parameters and can significantly improves performance sometimes by an important variance reduction factor in the M/M/1retrial queue compared to SRS. [ABSTRACT FROM AUTHOR]

In applications of spatial statistics, it is necessary to compute the product of some matrix W of spatial weights and a vector y of observations. The weighting matrix often needs to be adapted to the specific problems, such that the computation of Wy cannot necessarily be done with available R-packages. Hence, this article suggests one possibility treating such issues. The proposed technique avoids the computation of the matrix product by calculating each entry of Wy separately. Initially, a specific spatial autoregressive process is introduced. The performance of the proposed program is briefly compared to a basic program using the matrix multiplication. [ABSTRACT FROM AUTHOR]

This paper proposes non parametric adaptive refined descriptive sampling algorithm (NARDS) to estimate the survival function in lifetime models using Kaplan–Meier and Fleming–Harrington estimators. Simulations were performed using real data to generate inputs. The obtained results prove that NARDS works well on non parametric data. [ABSTRACT FROM AUTHOR]

This article analyzes and simulates the stationary FIFO M/G/1 queuing system with different service time distributions using Simple Random Sampling (SRS) and Refined Descriptive Sampling (RDS). Consequently, we design and realize a software under Linux using C language which establishes the performance measures of the M/G/1 queuing system, computes their variances and the variance reduction in order to compare both sampling methods. The simulation results demonstrate that RDS produces more accurate and efficient point estimates of the true parameters and can significantly improve performance sometimes by a huge variance reduction factor in the M/G/1 queue compared to SRS. [ABSTRACT FROM AUTHOR]

This article examines the behavior of refined descriptive sampling (RDS) on a civil engineering problem and focuses on the statistical comparison of random sampling (RS) and RDS. The problem considered is the aggregates quarry of Bejaia city. A discrete event simulation model is built using the activity cycle diagram and an application is then developed under the simulation system PSim, in Turbo Pascal programming language. The simulation of aggregates quarry is carried out to measure its performance using both sampling methods to generate input variables. Variance reduction and efficiency are deduced. The results prove the efficiency of RDS over RS. [ABSTRACT FROM AUTHOR]

In this paper, a three-dimensional (3D) multiscale model is proposed for the formation process of a primary thrombus. In the model, blood plasma is modelled by Navier–Stokes equations in macroscale because the blood plasma is seen as a continuous viscous fluid. The adhesion and aggregation of platelets are the main physiological processes of primary thrombus formation. As platelets and the primary thrombus are seen as rigid solids, these physiological processes are modelled in microscale according to the force related to the distance between the two solid bodies. We use level sets to represent the growth of the primary thrombus in 3D, and the multiscale model is applied to the 3D simulation of the primary thrombus formation. From numerical observations, the appearance of the formation process shows that it was affected by the change of blood-flow velocities. We can conclude that the appearance of the primary thrombus affects vascular blood flow. [ABSTRACT FROM PUBLISHER]

Probabilistic sensitivity analysis (SA) allows to incorporate background knowledge on the considered input variables more easily than many other existing SA techniques. Incorporation of such knowledge is performed by constructing a joint density function over the input domain. However, it rarely happens that available knowledge directly and uniquely translates into such a density function. A naturally arising question is then to what extent the choice of density function determines the values of the considered sensitivity measures. In this paper we perform simulation studies to address this question. Our empirical analysis suggests some guidelines, but also cautions to practitioners in the field of probabilistic SA. [ABSTRACT FROM AUTHOR]

Whenever a practitioner is not sure about the underlying process distribution, alternative monitoring schemes that may be used are called nonparametric charts. A nonparametric scheme mostly used to monitor the difference in the means of two samples is called the Wilcoxon rank-sum (WRS). In this paper, we propose nonparametric (or distribution-free) cumulative sum and exponentially weighted moving average charts based on the WRS using ranked set sampling. We thoroughly discuss the performance of the proposed control charts in terms of run-length properties through intensive simulations. Moreover, we conduct an overall performance comparison using the relative mean index and a variety of quality loss functions (for instance, the average extra quadratic loss, average ratio of the average run-length and performance comparison index). The newly proposed charts have very attractive run-length properties and they have better overall performance than their counterparts. An illustrative example is given, as well as an easy-to-use table with optimal design parameters to aid practical implementation. [ABSTRACT FROM AUTHOR]

In recent years, effective monitoring of data quality has increasingly attracted attention of researchers in the area of statistical process control. Among the relevant research on this topic, none used multivariate methods to control the multidimensional data quality process, but instead relied on multiple univariate control charts. Based on a novel one-sided multivariate exponentially weighted moving average (MEWMA) chart, we propose a conditional false discovery rate-adjusted scheme to on-line monitor the data quality of high-dimensional data streams. With thousands of input data streams, the average run length loses its usefulness because one will likely have out-of-control signals at each time period. Hence, we first control the percentage of signals that are false alarms. Then, we compare the power of the proposed MEWMA scheme with that of two alternative methods. Compared with two competitors, numerical results show that the proposed MEWMA scheme has higher average power. [ABSTRACT FROM PUBLISHER]

Financial resources are scarce, which is why corporate capital budgeting needs to employ efficient allocation mechanisms. This paper conceptually transforms the idea behind a hurdle rate-based coordination mechanism from an agency model into a computational model of a multi-divisional corporation with both heterogeneous departments and heterogeneous investment opportunities competing for the same source of funding. On the basis of our results, we reason that for heterogeneous investment opportunities a recurrent use of a hurdle rate-based coordination mechanism can work efficiently only if intra-organizational communication is assumed to be absent. We show that, if only a single investment opportunity can be carried into execution due to scarce financial resources, the heterogeneity of the competing investment opportunities positively affects the departments’ pay-offs, while the number of proposed investment projects negatively impacts departmental utilities derived from a residual income. The latter is why our results support the assumption that an emergence of cooperation is to be expected as soon as departments can establish interdepartmental communication, rendering a hurdle rate-based coordination mechanism inefficient. [ABSTRACT FROM AUTHOR]

Two algorithms for generating random variables with a rational probability-generating function are presented. One of them implements the recently developed general range reduction method, and the other is an extension of the alias method designed for generating discrete finite-valued random variables to the case where the generated random variable is infinite-valued. An example of a random variable which was efficiently generated by random number generators implementing the presented algorithms is given. Possible ways of improving the complexity of the presented algorithms are discussed. [ABSTRACT FROM PUBLISHER]

Firefly algorithm is a bio-inspired optimization algorithm which has been empirically demonstrated to perform well on many optimization problems. However, it can easily get trapped in the local optima and causes low precision. Therefore, improvement of this disadvantage is the very important. In this paper, we propose a wise strategy for step setting, which considers the information of firefly's personal and the global best positions. The results show that the modified algorithm enhances the performance of the basic firefly algorithm. [ABSTRACT FROM PUBLISHER]

In modelling and simulation, model complexity increases with the complexity of real systems, often resulting in unfeasibly long simulation times. Variable-structure models, which can change their equation set during run-time, offer a solution. This article introduces an object-oriented approach that describes such models independent of a simulation environment. The problem of changing equations during run-time is addressed using a Python framework. Three tools are already integrated, namely, Dymola, OpenModelica and Matlab/Simulink. With this framework, existing models can be easily reused, and the advantages of different simulation tools can be leveraged. The framework is illustrated using a simple satellite launch example, along with instructions for how to use the framework. The main aim of the framework is to simulate complex models with a few mode changes and thus save simulation time. With two real-world examples and a scalability analysis, it is shown that the framework fulfils these requirements. [ABSTRACT FROM AUTHOR]

In the multi-focus image fusion problem, the source images are obtained from the same scene. They are fused to get an image that contains all well-focussed objects. Previously, individual machine-learning models are proposed for image fusion. The performance of individual models is limited to fuse the useful information extracted from the blurred images. To address this problem, we developed a novel ensemble scheme for multi-focus image fusion using support vector machines (SVMs). In the proposed scheme, first, SVM models are constructed using different kernel functions of linear, polynomial, radial basis, and sigmoid. The predictions of individual SVM models are then combined using majority voting. In this way, the combined decision space becomes more informative and discriminant. A comparative analysis of the proposed scheme is carried out with previous techniques. It is found that our scheme is more accurate for synthesized-blurred and real defocussed images. [ABSTRACT FROM AUTHOR]

Control charts are widely used in industries to monitor a process for quality improvement. Evaluation of the average run length (ARL) or average time to signal (ATS) plays an important role in the design of control charts and performance comparison. In this paper, we review several basic and popular procedures, including the Markov chain and integral equation methods for computing ARL, ATS and associated run length distributions for cumulative sum charts, exponentially weighted moving average charts and combined control charts, respectively. Some important references and key formulations are provided for practitioners. [ABSTRACT FROM PUBLISHER]

In this paper, the finite sample properties of the maximum likelihood and Bayesian estimators of the half-normal stochastic frontier production function are analyzed and compared through a Monte Carlo study. The results show that the Bayesian estimator should be used in preference to the maximum likelihood owing to the fact that the mean square error performance is substantially better in the Bayesian framework. [ABSTRACT FROM PUBLISHER]

If we are observing very small objects in the microscope the information appears somehow distorted. This distortion is called point spread function (PSF) and it can be measured and simulated as we show in this article. Many experiments and simulations were done in the field of confocal or deconvolution microscopy, but we want to present 3D reconstruction and measurement of PSF in the bright field (or phase contrast) microscopy. Our goal is to use this knowledge about PSF to estimate objective focus. We want to use this method for automatic focusing of the microscope. We measured the PSF on our microscope in bright field using a 100×objective. We used 15 nm gold and 200 nm latex particles as point sources. We used two different simulations; first one was Airy disc simulation; the second one was Extended Nijboer–Zernike theory. Both approaches were used for description of PSF and finding the position of the focus. [ABSTRACT FROM PUBLISHER]

In this paper, a novel semi-fragile watermarking scheme for authenticating an audio signal based on dual-tree complex wavelet transform (DT-CWT) and discrete cosine transform (DCT) is proposed. Specifically, the watermark data are efficiently inserted into the coefficients of the low-frequency sub-band of DT-CWT taking advantages of both DCT and quantization index modulation (QIM). First, the original digital audio signal is segmented and then performed with DT-CWT. Second, based on the energy compression property, the low-frequency sub-band coefficients of the DT-CWT domain are performed with DCT, and the DC component is utilized to embed one distorted watermark bit by the QIM technique. Finally, inverse DCT and DT-CWT are orderly implemented on the watermarked coefficients of each audio segment to get a watermarked audio signal. Simulation results show that the hybrid embedding domain constructed by DT-CWT and DCT is effective, and the proposed watermarking scheme is not only inaudible, but also robust against content persistent non-malicious audio signal processing operations, such as MP3 compression, noise addition, re-sampling, re-quantization, etc. Furthermore, the proposed scheme can effectively authenticate the veracity and integrity of audio content and greatly expands the applicability of the audio watermarking scheme. [ABSTRACT FROM AUTHOR]

Frailty models are often used to model heterogeneity in survival analysis. The distribution of the frailty is generally assumed to be continuous. In some circumstances, it is appropriate to consider discrete frailty distributions. Having zero frailty can be interpreted as being immune, and population heterogeneity may be analysed using discrete frailty models. In this paper, survival functions are derived for the frailty models based on the discrete compound Poisson process. Maximum likelihood estimation procedures for the parameters are studied. We examine the fit of the models to earthquake and the traffic accidents’ data sets from Turkey. [ABSTRACT FROM AUTHOR]

In this article, we propose a new estimate algorithm for the parameters of a first-order Random Coefficient Autoregressive (RCA) Model. This algorithm turns out to be very reliable in estimating the true parameter values of a given model. It combines quasi-maximum likelihood method, the Kalman filter algorithm, and the Powell's method. Simulation results demonstrate that the algorithm is viable and promising. [ABSTRACT FROM AUTHOR]

Cross-validation has been widely used in the context of statistical linear models and multivariate data analysis. Recently, technological advancements give possibility of collecting new types of data that are in the form of curves. Statistical procedures for analysing these data, which are of infinite dimension, have been provided by functional data analysis. In functional linear regression, using statistical smoothing, estimation of slope and intercept parameters is generally based on functional principal components analysis (FPCA), that allows for finite-dimensional analysis of the problem. The estimators of the slope and intercept parameters in this context, proposed by Hall and Hosseini-Nasab [On properties of functional principal components analysis, J. R. Stat. Soc. Ser. B: Stat. Methodol. 68 (2006), pp. 109–126], are based on FPCA, and depend on a smoothing parameter that can be chosen by cross-validation. The cross-validation criterion, given there, is time-consuming and hard to compute. In this work, we approximate this cross-validation criterion by such another criterion so that we can turn to a multivariate data analysis tool in some sense. Then, we evaluate its performance numerically. We also treat a real dataset, consisting of two variables; temperature and the amount of precipitation, and estimate the regression coefficients for the former variable in a model predicting the latter one. [ABSTRACT FROM PUBLISHER]

In this article, a new distribution, namely, Weibull-Pareto distribution is defined and studied. Various properties of the Weibull-Pareto distribution are obtained. The distribution is found to be unimodal and the shape of the distribution can be skewed to the right or skewed to the left. Results for moments, limiting behavior, and Shannon's entropy are provided. The method of modified maximum likelihood estimation is proposed for estimating the model parameters. Several real data sets are used to illustrate the applications of Weibull-Pareto distribution. [ABSTRACT FROM AUTHOR]

In this paper, we introduce thep-generalized polar methods for the simulation of thep-generalized Gaussian distribution. On the basis of geometric measure representations, the well-known Box–Muller method and the Marsaglia–Bray rejecting polar method for the simulation of the Gaussian distribution are generalized to simulate thep-generalized Gaussian distribution, which fits much more flexibly to data than the Gaussian distribution and has already been applied in various fields of modern sciences. To prove the correctness of thep-generalized polar methods, we give stochastic representations, and to demonstrate their adequacy, we perform a comparison of six simulation techniques w.r.t. the goodness of fit and the complexity. The competing methods include adapted general methods and another special method. Furthermore, we prove stochastic representations for all the adapted methods. [ABSTRACT FROM AUTHOR]

The backfilling algorithm commonly used in estimating additive models is used to decompose the component shares explained by a set of predictors on a dependent variable in the presence of linear dependencies (multicollinearity) among the predictors. Simulated and actual data show that the backfitting methods are superior in terms of predictive ability as the degree of multicollinearity worsens. Furthermore, the additive smoothing splines are especially superior when the linear model yield inadequate fil to the data and the predictors exhibit extreme multicollinearity. [ABSTRACT FROM AUTHOR]

The backoff protocol is widely used for sharing a common channel among several stations in communication networks. The Binary Exponential Backoff (BEB) improves the system throughput but increases the capture effect, permitting a station to seize the channel for a long time. In this article, we introduce and analyze a new class of adaptive backoff protocols where a station changes its contention window after a successful transmission differently than BEB. The transitions between the contention window states are determined by a stochastic matrix which describes a finite, irreducible, aperiodic Markov chain. We derive a stationary distribution of an associated embedded Markov chain in an explicit form and then find the stationary distribution of the basic Markov chain explicitly. Preliminary simulation results show that our backoff protocol can reduce the capture effect in Ethernet and wireless networks significantly. [ABSTRACT FROM PUBLISHER]

In this paper, the sufficient conditions that guarantee the convergence of the variational iteration method when applied to solve a coupled system of nonlinear partial differential equations are presented. Especial attention is given to the error bound of the nth term of the resultant sequence. Numerical examples to show the efficiency of the method are presented. [ABSTRACT FROM AUTHOR]

This work is carried out within a research collaboration with the Astronomical Observatory of Arcetri (Florence, Italy) aimed at the development of a parallel version of the adaptive optics simulation software named CAOS (Code for Adaptive Optics System). CAOS is currently used at the Arcetri Observatory to simulate the behaviour of an adaptive optic system, from the atmospheric turbulence corruption to the subsequent adaptive correction made on the wavefronts coming from the astronomical objects onto the pupils of the ground-based telescopes. We describe the computational efforts towards the integration of the CAOS package into a high-performance computing architecture, implementing three different parallelization approaches of a fast fourier transform (FFT)-based CAOS module and discuss the performance using standard performance metrics. [ABSTRACT FROM AUTHOR]

In the context of finite population survey sampling, we propose a new model-based mean estimator, when the function that links the variables is discontinuous. The available estimators of the mean based on nonparametric regression are derived under the assumption that the regression function is continuous. We propose a new approach to adjust for the effect of discontinuity on regression estimation of the mean. The performance of the proposed estimator is analysed through a simulation study because the theoretic study of asymptotics is not possible. In the literature, the new estimator requires more computational cost than others, but the simulation experiments indicate that the proposed method has higher efficiency than other traditional parametric and nonparametric regression methods. [ABSTRACT FROM AUTHOR]

Many models that have been developed for cortical bone oversimplify much of the architectural and physical complexity. With SiNuPrOs model, a more complete approach is investigated: it is multiscale because it contains five structural levels and multi physic because it takes into account simultaneously structure (with various properties: elasticity, piezoelectricity, porous medium), fluid and mineralization process modelization. The multiscale aspect is modeled by using 18 structural parameters in a specific application of the mathematical theory of homogenization and 10 other physical parameters are necessary for the multi physic aspect. The modelization of collagen as a piezoelectric medium has needed the development of a new behaviour law allowing a better simulation of the effect of a medium considered as evolving during a mineralization process. Then the main interest of SiNuPrOs deals with the possibility to study, at each level of the cortical architecture, either the elastic properties or the fluid motion or the piezoelectric effects or both of them. All these possibilities constitute a very large work and all this mass of information (fluid aspects, even at the nanoscopic scale, piezoelectric phenomena and simulations) will be presented in several papers. This first one is only devoted to the presentation of this model with an application to the computation of elastic properties at the macroscopic scale. The computational methods have been packed into software also called SiNuPrOs and allowing a large number of predictive simulations corresponding to various different configurations. [ABSTRACT FROM AUTHOR]

Timed Petri nets can be used for the modelling and analysis of a wide range of concurrent discrete-event systems, e.g. production systems. The present paper describes how to do so while starting from the information about the structure of a production facility and about the products usually given in production-data management systems. We describe a method for using these data to algorithmically build a Petri-net model. The timed Petri-net simulator, which was built in Matlab®, is also described. This simulator makes it possible to introduce heuristics, and in this way production operations can be scheduled. To demonstrate the applicability of our approach, we applied it to a scheduling problem in a multi-product batch plant. [ABSTRACT FROM AUTHOR]

A wave forecasting system developed by Puertos del Estado (The Spanish holding of harbours) to predict waves at the coast is run in a twice a day cycle with a forecasting horizon of 72 h. This system is driven by wind fields supplied by the Spanish Meteorological Service from the HIRLAM model. Nested within this model a set of local forecasting systems, one for each harbour, covering an area of around 25 × 25 km, has been developed. The narrowness of the Spanish continental shelf requires very high resolution grids to be applied to localised regions near the coast. This fact involves the use of modelling techniques that makes this forecasting system different from other systems implemented in other regions. This article describes the wave forecasting system and the different techniques developed at Puertos del Estado to implement it. [ABSTRACT FROM AUTHOR]

We present a method for the simulation of two-phase flows which can be applied to problems characterised by the presence of up to several hundreds of gas bubbles. The bubble model is kept simple, requiring only six parameters to describe the shape of a single bubble. The model is coupled to a conventional time discrete finite-volume scheme for the solution of the Navier–Stokes equations by the density field which is calculated on basis of the information on the positions and the shapes of the bubbles before each time step. The motion of the bubbles is in turn calculated from an analysis of the computed flow field. Systematical errors due to simplifications are eliminated by the introduction of correction factors. For a selection of fluid dynamical problems, the results of simulations using the method are compared to experimental data. Good quantitative agreement could be found. [ABSTRACT FROM AUTHOR]