Comparing exponential location parameters with several controls under heteroscedasticity

Suppose that random samples are taken from \(k\) treatment groups and \(l\) control groups, where the observations in each group have a two-parameter exponential distribution. We consider the problem of constructing simultaneous confidence intervals for the differences between location parameters of the treatment groups and the control groups when the scale parameters may be unequal. Using the parametric bootstrap approach, we develop a new multiple comparisons procedure when the scale parameters and sample sizes are possibly unequal. We then present a simulation study in which we compare the performance of our proposed procedure with two other procedures. The results of our simulations indicate that our proposed procedure performs better than other procedures. The usefulness of our proposed procedure is illustrated with an example.     

Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression

Semiparametric partially linear varying coefficient models (SPLVCM) are frequently used in statistical modeling. With high-dimensional covariates both in parametric and nonparametric part for SPLVCM, sparse modeling is often considered in practice. In this paper, we propose a new estimation and variable selection procedure based on modal regression, where the nonparametric functions are approximated by $B$ -spline basis. The outstanding merit of the proposed variable selection procedure is that it can achieve both robustness and efficiency by introducing an additional tuning parameter (i.e., bandwidth $h$ ). Its oracle property is also established for both the parametric and nonparametric part. Moreover, we give the data-driven bandwidth selection method and propose an EM-type algorithm for the proposed method. Monte Carlo simulation study and real data example are conducted to examine the finite sample performance of the proposed method. Both the simulation results and real data analysis confirm that the newly proposed method works very well.     

Robust estimation for varying index coefficient models

Varying index coefficient models (VICMs) proposed by Ma and Song (J Am Stat Assoc, 2014. doi: 10.1080/01621459.2014.903185) are a new class of semiparametric models, which encompass most of the existing semiparametric models. So far, only the profile least squares method and local linear fitting were developed for the VICM, which are very sensitive to the outliers and will lose efficiency for the heavy tailed error distributions. In this paper, we propose an efficient and robust estimation procedure for the VICM based on modal regression which depends on a bandwidth. We establish the consistency and asymptotic normality of proposed estimators for index coefficients by utilizing profile spline modal regression method. The oracle property of estimators for the nonparametric functions is also established by utilizing a two-step spline backfitted local linear modal regression approach. In addition, we discuss the bandwidth selection for achieving better robustness and efficiency and propose a modified expectation–maximization-type algorithm for the proposed estimation procedure. Finally, simulation studies and a real data analysis are carried out to assess the finite sample performance of the proposed method.     

Multidimensional linearizable system of <Emphasis Type="Italic">n</Emphasis>-wave-type equations

We propose a linearizable version of a multidimensional system of n-wave-type nonlinear partial differential equations (PDEs). We derive this system using the spectral representation of its solution via a procedure similar to the dressing method for nonlinear PDEs integrable by the inverse scattering transform method. We show that the proposed system is completely integrable and construct a particular solution.     

An exact method for minimizing the total treatment time in intensity-modulated radiotherapy

In this paper, we solve a combinatorial optimization problem that arises from the treatment planning of a type of radiotherapy where intensity is modulated by multileaf collimators (MLC) in a step-and-shoot manner. In Ernst et al [INFORMS Journal on Computing 21 (4) (2009): 562–574], we proposed an exact method for minimizing the number of MLC apertures needed for a treatment. Our method outperformed the fastest algorithms available at the time. We refer to our method as the CPI method. We now attempt to minimize the total treatment time by modifying our CPI method. This modification involves non-trivial work, as some of the search space elimination schemes used in the CPI method cannot be applied in here. In our numerical experiments, we again compare our new method with the fastest algorithms currently available. There has been significant recent research in this area; hence we compare our method with those published in Wake et al [Computers and Operations Research 36 (2009): 795–810], Ta?kin et al [Operations Research 58 (3) (2010): 674–690] and Cambazard et al [CPAIRO (2010): 1–16]. The numerical comparisons indicate that our method generally outperformed the first two, while being competitive with the third.     

A variant of the IDR(s) method with the quasi-minimal residual strategy

The IDR($s$) method proposed by Sonneveld and van Gijzen is an effective method for solving nonsymmetric linear systems, but usually with irregular convergence behavior. In this paper, we reformulate the relations of residuals and their auxiliary vectors generated by the IDR($s$) method in matrix form. Then, using this new formulation and motivated by other QMR-type methods, we propose a variant of the IDR($s$) method, called QMRIDR($s$), for overcoming the disadvantage of its irregular convergence behavior. Both fast and smooth convergence behaviors of the QMRIDR($s$) method can be shown. Numerical experiments are reported to show the efficiency of our proposed method.     

A New Iterative Method for the Computation of the Solutions of Nonlinear Equations

Recently, by Costabile, Gualtieri and Serra (1999), an iterative method was presented for the computation of zeros of C ¹ functions. This method combines the assured convergence of the bisection-like algorithms with a superlinear convergence speed which characterizes Newton-like methods. The order of the method and the cost per iteration is exactly equivalent to the Newton method. In this paper we present a new iterative method for the computation of the zeros of C ¹ functions with the same properties of convergence as the method proposed by Costabile, Gualtieri and Serra (1999) but with order 1+ $\sqrt 2 $ ?2.41 for C ³ functions. Compared with the methods of order 1+ $\sqrt 2 $ presented by Traub (1964), our methods ensure global convergence. Then we consider a generalization of this procedure which gives a class of methods of order (n+ $\sqrt {n^2 + 4} $ )/2, where n is the degree of the approximating polynomial, with one-point iteration functions with memory. Finally a number of numerical tests are performed. The numerical results seem to show that, at least on a set of problems, the new methods work better than the methods proposed, and, therefore, than both the Newton and Alefeld and Potra (1992) methods.     

Simultaneous confidence intervals for ordered pairwise differences of exponential location parameters under heteroscedasticity

This article deals with the extension of the two-stage and one-stage procedures for successive differences of location parameters of k$k$ independent two-parameter exponential distributions proposed by Maurya et al. (2011). While maintaining the advantages of the Maurya et al. (2011) procedure, our proposed procedure has more power for rejecting the null hypothesis of homogeneity of several location parameters in favour of the alternative. Finally, a numerical example is given to illustrate the advantages of the proposed procedure.     

A class of nonseparable dynamic programming problems

A solution procedure is proposed for a class of deterministic sequential decision problems whose objective functions are of the form f _n(x _n)+(g _n(x _n)) where is differentiable and either concave or convex. The procedure calls for the collaboration between dynamic programming and c-programming, and is demonstrated in our treatment of a minimum variance type problem.     

Estimating the number of zero-one multi-way tables via sequential importance sampling

In 2005, Chen et al. introduced a sequential importance sampling (SIS) procedure to analyze zero-one two-way tables with given fixed marginal sums (row and column sums) via the conditional Poisson (CP) distribution. They showed that compared with Monte Carlo Markov chain (MCMC)-based approaches, their importance sampling method is more efficient in terms of running time and also provides an easy and accurate estimate of the total number of contingency tables with fixed marginal sums. In this paper, we extend their result to zero-one multi-way ( $d$ -way, $d \ge 2$ ) contingency tables under the no $d$ -way interaction model, i.e., with fixed $d-1$ marginal sums. Also, we show by simulations that the SIS procedure with CP distribution to estimate the number of zero-one three-way tables under the no three-way interaction model given marginal sums works very well even with some rejections. We also applied our method to Samson’s monks data set.     

Hybridization of accelerated gradient descent method

We present a gradient descent algorithm with a line search procedure for solving unconstrained optimization problems which is defined as a result of applying Picard-Mann hybrid iterative process on accelerated gradient descent S M method described in Stanimirovi? and Miladinovi? (Numer. Algor. 54, 503–520, 2010). Using merged features of both analyzed models, we show that new accelerated gradient descent model converges linearly and faster then the starting S M method which is confirmed trough displayed numerical test results. Three main properties are tested: number of iterations, CPU time and number of function evaluations. The efficiency of the proposed iteration is examined for the several values of the correction parameter introduced in Khan (2013).     

A finite volume scheme with preconditioned Lanczos method for two-dimensional space-fractional reaction–diffusion equations

Fractional differential equations have been increasingly used as a powerful tool to model the non-locality and spatial heterogeneity inherent in many real-world problems. However, a constant challenge faced by researchers in this area is the high computational expense of obtaining numerical solutions of these fractional models, owing to the non-local nature of fractional derivatives. In this paper, we introduce a finite volume scheme with preconditioned Lanczos method as an attractive and high-efficiency approach for solving two-dimensional space-fractional reaction–diffusion equations. The computational heart of this approach is the efficient computation of a matrix-function-vector product $f(\mathbf{A})\mathbf{b}$, where $\mathbf{A}$ is the matrix representation of the Laplacian obtained from the finite volume method and is non-symmetric. A key aspect of our proposed approach is that the popular Lanczos method for symmetric matrices is applied to this non-symmetric problem, after a suitable transformation. Furthermore, the convergence of the Lanczos method is greatly improved by incorporating a preconditioner. Our approach is show-cased by solving the fractional Fisher equation including a validation of the solution and an analysis of the behaviour of the model.     

Error analysis of splitting methods for semilinear evolution equations

We consider a Strang-type splitting method for an abstract semilinear evolution equation

$${\partial _t}u = Au + F\left( u \right).$$

Roughly speaking, the splitting method is a time-discretization approximation based on the decomposition of the operators A and F. Particularly, the Strang method is a popular splitting method and is known to be convergent at a second order rate for some particular ODEs and PDEs. Moreover, such estimates usually address the case of splitting the operator into two parts. In this paper, we consider the splitting method which is split into three parts and prove that our proposed method is convergent at a second order rate.

     

Due-date assignment with asymmetric earliness–tardiness cost

The problem of minimizing the maximal weighted absolute lateness (MWAL) is known to be NP-hard. The due-date assignment part of MWAL for a given sequence has been shown in the literature to be solved on a single machine in O(n²) time. In this paper, we study a more general version of the problem with asymmetric cost (nonidentical earliness and tardiness weights). We introduce a linear-programming-based O(n) solution for this case. We also extend our proposed solution procedure to other machine settings such as flow-shop and parallel machines.     

Local convergence of generalized Mann iteration

The local convergence of generalized Mann iteration is investigated in the setting of a real Hilbert space. As application, we obtain an algorithm for estimating the local radius of convergence for some known iterative methods. Numerical experiments are presented showing the performances of the proposed algorithm. For a particular case of the Ezquerro-Hernandez method (Ezquerro and Hernandez, J. Complex., 25:343–361: 2009), the proposed procedure gives radii which are very close to or even identical with the best possible ones.     

Approximate KKT points and a proximity measure for termination

Karush–Kuhn–Tucker (KKT) optimality conditions are often checked for investigating whether a solution obtained by an optimization algorithm is a likely candidate for the optimum. In this study, we report that although the KKT conditions must all be satisfied at the optimal point, the extent of violation of KKT conditions at points arbitrarily close to the KKT point is not smooth, thereby making the KKT conditions difficult to use directly to evaluate the performance of an optimization algorithm. This happens due to the requirement of complimentary slackness condition associated with KKT optimality conditions. To overcome this difficulty, we define modified ${\epsilon}$ -KKT points by relaxing the complimentary slackness and equilibrium equations of KKT conditions and suggest a KKT-proximity measure, that is shown to reduce sequentially to zero as the iterates approach the KKT point. Besides the theoretical development defining the modified ${\epsilon}$ -KKT point, we present extensive computer simulations of the proposed methodology on a set of iterates obtained through an evolutionary optimization algorithm to illustrate the working of our proposed procedure on smooth and non-smooth problems. The results indicate that the proposed KKT-proximity measure can be used as a termination condition to optimization algorithms. As a by-product, the method helps to find Lagrange multipliers correspond to near-optimal solutions which can be of importance to practitioners. We also provide a comparison of our KKT-proximity measure with the stopping criterion used in popular commercial softwares.     

Double acceptance sampling plans for Burr type XII distribution percentiles under the truncated life test

Lio et al (2010a,?2010b) introduced two single acceptance sampling plans (SASPs) for the percentiles of Birnbaum-Saunders and Burr type XII distribution with a truncated censoring scheme. They assured that the acceptance sampling plans for percentiles significantly improve the traditional ones for mean life. In this paper, a double-sampling procedure is developed for Burr type XII distribution percentiles to save sample resource with a truncated censoring scheme. Minimum sample sizes for implementing the proposed double-sampling method are determined under the satisfied levels of consumer's risk and producer's risk. Illustrative examples are used to demonstrate the applications of the proposed method. Compared with the SASP, the proposed double-sampling acceptance plan uses less sample resource in average for truncated life testing.     

An indefinite variant of LOBPCG for definite matrix pencils

In this paper, we propose a novel preconditioned solver for generalized Hermitian eigenvalue problems. More specifically, we address the case of a definite matrix pencil \(A-\lambda B\) , that is, A, B are Hermitian and there is a shift \(\lambda _{0}\) such that \(A-\lambda _{0} B\) is definite. Our new method can be seen as a variant of the popular LOBPCG method operating in an indefinite inner product. It also turns out to be a generalization of the recently proposed LOBP4DCG method by Bai and Li for solving product eigenvalue problems. Several numerical experiments demonstrate the effectiveness of our method for addressing certain product and quadratic eigenvalue problems.