A test for the presence of pure feedback in multivariate dynamic stochastic systems

This paper describes a procedure for testing the presence of a pure feedback loop in a transfer function model for a multivariate discrete dynamic stochastic system. A modification of the portmanteau statistic based on sample cross-covariance matrices of the prewhitened series is proposed. The statistic is shown to be asymptotically distributed according to a % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiiYdd9qrFfea0dXdf9vqai-hEir8Ve% ea0de9qq-hbrpepeea0db9q8as0-LqLs-Jirpepeea0-as0Fb9pgea% 0lrP0xe9Fve9Fve9qapdbaqaaeGacaGaaiaabeqaamaabaabcaGcba% Gaeq4Xdm2aaWbaaSqabeaacaaIYaaaaaaa!3E0C!\[\chi ^2 \]-distribution with certain degrees of freedom under some pure feedback assumptions. Some numerical results are given to show the behavior of the proposed method.     

A note on convex ordering for stable stochastic integrals

We establish a convex ordering between stochastic integrals driven by strictly α-stable processes with index α ∈ (1,2). Our approach is based on the forward–backward stochastic calculus for martingales together with a suitable decomposition of stable stochastic integrals.     

Inference of constant-stress accelerated life test for a truncated distribution under progressive censoring

Accelerated life test (ALT) provides a feasible and efficient way to obtain information quickly on lifetime of products by testing them at higher-than-use operating conditions. In this paper, the lifetime of products is assumed to follow a lower truncated family of distributions, when both resilience and threshold parameters are nonconstant and affected by operating stress, inference is discussed for simple constant-stress ALT under progressive Type-II censoring. Point estimates for unknown parameters are presented based on maximum likelihood and pivotal quantities based estimation methods. Meanwhile, generalized, asymptotic and bootstrap confidence intervals for the parameters of interest are constructed as well. Simulation studies and illustrative examples are carried out to investigate the performance of the proposed methods.     

A concept of negative dependence using stochastic ordering

A concept of negative dependence called negative dependence by stochastic ordering is introduced. This concept satisfies various closure properties. It is shown that three models for negetive dependence satisfy it and that it implies the basic negative orthant inequalities. This concept is also satisfied by the multinomial, multivariate hypergeometric. Dirichlet and Dirichlet compound multinomial distributions. Furthermore, the joint distribution of ranks of a sample and the multivariate normal with nonpositive pairwise correlations also satisfy this condition. The positive dependence analog of this condition is also studied.     

A Berry-Esseen Bound for k-sample Symmetric Statistics

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A numerical comparison of the normal and some saddlepoint approximations to a distribution-free test for stochastic ordering in the competing risks model

Calculating the exact critical value of the test statistic is important in nonparametric statistics. However, to evaluate the exact critical value is difficult when the sample sizes are moderate to large. Under these circumstances, to consider more accurate approximation for the distribution function of a test statistic is extremely important. A distribution-free test for stochastic ordering in the competing risks model has been proposed by Bagai et al. (1989). Herein, we performed a saddlepoint approximation in the upper tails for the Bagai statistic under finite sample sizes. We then compared the saddlepoint approximations with the Bagai approximation and investigate the accuracy of the approximations. Additionally, the orders of errors of the saddlepoint approximations were derived.     

Optimal ordering rule for a stochastic sequencing model

In this note, necessary and sufficient conditions are derived for the optimality of a sequencing rule for a class of stochastic sequential models. The optimal sequential rule generalizes the deterministic results, given in Refs. 1–2, for situations when some of the parameters of the problem are random variables. Two cases are given to demonstrate the usefulness of the results.The author would like to acknowledge the support provided by King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, during his sabbatical leave. Also, the Industrial and Operations Engineering Department, University of Michigan, is acknowledged for hosting the author.     

Rank tests for regression and k-sample rank tests under random censorship

The uncensored observations and the censored ones are ranked separately. A test statistic is obtained by suitably combining the rank statistics corresponding to these two groups. This approach leads to simple tests that closely resemble those for the uncensored situation.     

Optimum life testing plans in presence of hybrid censoring: A cost function approach

Hybrid censoring scheme is a combination of Type‐I and Type‐II censoring schemes. Determination of optimum hybrid censoring scheme is an important practical issue in designing life testing experiments to enhance the information on reliability of the product. In this work, we consider determination of optimum life testing plans under hybrid censoring scheme by minimizing the total cost associated with the experiment. It is shown that the proposed cost function is scale invariant for some selected distributions. Optimum solution cannot be obtained analytically. We propose a method for obtaining the optimum solution and consider Weibull distribution for illustration. We also studied the sensitivity of the optimal solution to the misspecification of parameter values and cost components through a well‐designed sensitivity analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Two-stage stochastic optimization problems with stochastic ordering constraints on the recourse

We consider two-stage risk-averse stochastic optimization problems with a stochastic ordering constraint on the recourse function. Two new characterizations of the increasing convex order relation are provided. They are based on conditional expectations and on integrated quantile functions: a counterpart of the Lorenz function. We propose two decomposition methods to solve the problems and prove their convergence. Our methods exploit the decomposition structure of the risk-neutral two-stage problems and construct successive approximations of the stochastic ordering constraints. Numerical results confirm the efficiency of the methods.     

Beran-based approach for single-index models under censoring

In this paper we propose a new method for estimating parameters in a single-index model under censoring based on the Beran estimator for the conditional distribution function. This, likelihood-based method is also a useful and simple tool used for bandwidth selection. Additionally, we perform an extensive simulation study comparing this new Beran-based approach with other existing method based on Kaplan–Meier integrals. Finally, we apply both methods to a primary biliary cirrhosis data set and propose a bootstrap test for the parameters.     

Assumptions regarding right censoring in the presence of left truncation

Clinical studies using complex sampling often involve both truncation and censoring, where there are options for the assumptions of independence of censoring and event and for the relationship between censoring and truncation. In this paper, we clarify these choices, show certain equivalences, and provide examples.     

Nonparametric tests for multiple regression under progressive censoring

For continuous observations from time-sequential studies, suitable Cramér-von Mises and Kolmogorov-Smirnov types of (nonparametric) statistics (based on linear rank statistics) for testing hypotheses on some multiple-regression models are proposed and studied. The asymptotic theory of these tests is provided for both the null and (local) alternative hypotheses situations and is based on the weak convergence of suitable rank order processes (on the D[0, 1] space) to certain functions of Brownian motions. Bahadur efficiency results are also presented. Empirical values of the percentile points of the null distributions of the proposed test statistics, obtained through simulation studies, are also provided.     

Confidence sets for the two-parameter Rayleigh distribution under progressive censoring

Exact confidence intervals and regions are proposed for the location and scale parameters of the Rayleigh distribution. These sets are valid for complete data, and also for standard and progressive failure censoring. Constrained optimization problems are solved to find the minimum-size confidence sets for the Rayleigh parameters with the required confidence level. The smallest-area confidence region is derived by simultaneously solving four nonlinear equations. Three numerical examples regarding remission times of leukemia patients, strength data and the titanium content in an aircraft-grade alloy, as well as a simulation study, are included for illustrative purposes. Further applications in hypothesis testing and the construction of pointwise and simultaneous confidence bands are also pointed out.     

Wavelet estimations for mixed densities under multiplicative censoring

Using compactly supported wavelets, Chaubey et al consider L²-risk estimation for mixed density under multiplicative censoring (Chaubey YP, Chesneau C, Doosti H. Adaptive wavelet estimation of a density from mixtures under multiplicative censoring. Statistics, 2015, 49: 638-659). In this paper, we try to discuss L^p-risk (1 ≤ p<∞) estimation for that statistical model of the linear and nonlinear wavelet estimators respectively. Our results can be seen as an extension of the work of Chaubey et al.     

On the quantile process under progressive censoring

This work deals with an asymptotic almost-sure representation of the quantile process under type-II progressive censoring. A convergence rate of the law-of-the-iterated-logarithm type is obtained for this representation. To cite this article: S. Alvarez-Andrade, C. R. Acad. Sci. Paris, Ser. I 347 (2009).     

Exponential mixing for stochastic PDEs: the non-additive case

We establish a general criterion which ensures exponential mixing of parabolic stochastic partial differential equations (SPDE) driven by a non additive noise which is white in time and smooth in space. We apply this criterion on two representative examples: 2D Navier–Stokes (NS) equations and Complex Ginzburg–Landau (CGL) equation with a locally Lipschitz noise. Due to the possible degeneracy of the noise, Doob theorem cannot be applied. Hence, a coupling method is used in the spirit of Kuksin and Shirikyan (J. Math. Pures Appl. 1:567–602, 2002) and Mattingly (Commun. Math. Phys. 230:421–462, 2002). Previous results require assumptions on the covariance of the noise which might seem restrictive and artificial. For instance, for NS and CGL, the covariance operator is supposed to be diagonal in the eigenbasis of the Laplacian and not depending on the high modes of the solutions. The method developed in the present paper gets rid of such assumptions and only requires that the range of the covariance operator contains the low modes.