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  • Model averaging marginal regression for high dimensional conditional quantile prediction
    Stat. Pap. (IF 1.433) Pub Date : 2020-10-16
    Jingwen Tu, Hu Yang, Chaohui Guo, Jing Lv

    In this article, we propose a high dimensional semiparametric model average approach to predict the conditional quantile of the response variable. Firstly, we approximate the multivariate conditional quantile function by an affine combination of one-dimensional marginal conditional quantile functions which can be estimated by the local linear regression. Secondly, based on the estimated marginal quantile

  • Model pursuit and variable selection in the additive accelerated failure time model
    Stat. Pap. (IF 1.433) Pub Date : 2020-10-12
    Li Liu, Hao Wang, Yanyan Liu, Jian Huang

    In this paper, we propose a new semiparametric method to simultaneously select important variables, identify the model structure and estimate covariate effects in the additive AFT model, for which the dimension of covariates is allowed to increase with sample size. Instead of directly approximating the non-parametric effects as in most existing studies, we take a linear effect out to weak the condition

  • Characterization of continuous symmetric distributions using information measures of records
    Stat. Pap. (IF 1.433) Pub Date : 2020-10-02
    Jafar Ahmadi

    In this paper, several characterizations of continuous symmetric distributions are provided. The results are based on the properties of some information measures of k-records. These include cumulative residual (past) entropy, Shannon entropy, Rényi entropy, Tsallis entropy, also some common Kerridge inaccuracy measures. It is proved that the equality of information in upper and lower k-records is a

  • Global statistical inference for the difference between two regression mean curves with covariates possibly partially missing
    Stat. Pap. (IF 1.433) Pub Date : 2020-10-01
    Li Cai, Suojin Wang

    In two sample problems it is of interest to examine the difference between the two regression curves or to detect whether certain functions are adequate to describe the overall trend of the difference. In this paper, we propose a simultaneous confidence band (SCB) as a global inference method with asymptotically correct coverage probabilities for the difference curve based on the weighted local linear

  • Limit theorems for locally stationary processes
    Stat. Pap. (IF 1.433) Pub Date : 2020-10-01
    Rafael Kawka

    We present limit theorems for locally stationary processes that have a one sided time-varying moving average representation. In particular, we prove a central limit theorem (CLT), a weak and a strong law of large numbers (WLLN, SLLN) and a law of the iterated logarithm (LIL) under mild assumptions using a time-varying Beveridge–Nelson decomposition.

  • Estimation and clustering for partially heterogeneous single index model
    Stat. Pap. (IF 1.433) Pub Date : 2020-09-06
    Fangfang Wang, Lu Lin, Lei Liu, Kangning Wang

    In this paper, our goal is to estimate the homogeneous parameter and cluster the heterogeneous parameters in a partially heterogeneous single index model (PHSIM). To achieve the goal, the minimization criterion for such a single index model is first transformed into a least-squares optimization problem in the population form. Based on the least-squares objective function, we introduce an empirical

  • On properties of Toeplitz-type covariance matrices in models with nested random effects
    Stat. Pap. (IF 1.433) Pub Date : 2020-09-01
    Yuli Liang, Dietrich von Rosen, Tatjana von Rosen

    Models that capture symmetries present in the data have been widely used in different applications, with early examples from psychometric and medical research. The aim of this article is to study a random effects model focusing on the covariance structure that is block circular symmetric. Useful results are obtained for the spectra of these structured matrices.

  • Testing symmetry around a subspace
    Stat. Pap. (IF 1.433) Pub Date : 2020-08-07
    Šárka Hudecová, Miroslav Šiman

    The article shows how some common measures of association between two random vectors may be used to test multivariate symmetry around a subspace (possibly up to a shift), which also permits testing exchangeability, axial symmetry, halfspace symmetry, and certain goodness-of-fit and equality-of-scale hypotheses. The resulting (parametric, nonparametric, permutation, and asymptotic) tests of the symmetry

  • Population empirical likelihood estimation in dual frame surveys
    Stat. Pap. (IF 1.433) Pub Date : 2020-08-05
    Maria del Mar Rueda, Maria Giovanna Ranalli, Antonio Arcos, David Molina

    Dual frame surveys are a device to reduce the costs derived from data collection in surveys and improve coverage for the whole target population. Since their introduction, in the 1960‘s, dual frame surveys have gained much attention and several estimators have been formulated based on a number of different approaches. In this work, we propose new dual frame estimators based on the population empirical

  • Multiple change point detection and validation in autoregressive time series data
    Stat. Pap. (IF 1.433) Pub Date : 2020-07-13
    Lijing Ma; Andrew J. Grant; Georgy Sofronov

    It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is of interest. In this paper, time series data is modelled assuming each segment is an autoregressive time series with possibly different autoregressive parameters. This is achieved using two main steps. The first step

  • Bootstrap Methods for Judgment Post Stratification
    Stat. Pap. (IF 1.433) Pub Date : 2020-07-09
    Mozhgan Alirezaei Dizicheh, Nasrollah Iranpanah, Ehsan Zamanzade

    It has been shown in the literature that judgment post stratification (JPS) sampling design often leads to more efficient statistical inference than what is possible to obtain in simple random sampling (SRS) design of comparable size. Since the JPS is a cost-efficient sampling design, a large enough sample size may not be available to use normal theory of the estimators. In this paper, we describe

  • Change point detection for nonparametric regression under strongly mixing process
    Stat. Pap. (IF 1.433) Pub Date : 2020-07-09
    Qing Yang; Yu-Ning Li; Yi Zhang

    In this article, we consider the estimation of the structural change point in the nonparametric model with dependent observations. We introduce a maximum-CUSUM-estimation procedure, where the CUSUM statistic is constructed based on the sum-of-squares aggregation of the difference of the two Nadaraya-Watson estimates using the observations before and after a specific time point. Under some mild conditions

  • A new foldover strategy and optimal foldover plans for three-level design
    Stat. Pap. (IF 1.433) Pub Date : 2020-07-02
    Zujun Ou, Hongyi Li

    The foldover is a useful technique in construction of factorial designs. It is also a standard follow-up strategy discussed in many textbooks by adding a second fraction called a foldover design. In this paper uniformity criterion measured by the wrap-around \(L_2\)-discrepancy is used to further distinguish the optimal foldover plan for three-level designs. For three-level fractional factorials as

  • Comparing six shrinkage estimators with large sample theory and asymptotically optimal prediction intervals
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-30
    Lasanthi C. R. Pelawa Watagoda, David J. Olive

    Consider the multiple linear regression model \(Y = \beta _1 + \beta _2 x_2 + \cdots + \beta _p x_p + e = {\varvec{x}}^T \varvec{\beta }+ e\) with sample size n. This paper compares the six shrinkage estimators: forward selection, lasso, partial least squares, principal components regression, lasso variable selection, and ridge regression, with large sample theory and two new prediction intervals that

  • Degrees of freedom for regularized regression with Huber loss and linear constraints
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-29
    Yongxin Liu, Peng Zeng, Lu Lin

    The ordinary least squares estimate for linear regression is sensitive to errors with large variance. It is not robust to heavy-tailed errors or outliers, which are commonly encountered in applications. In this paper, we propose to use a Huber loss function with a generalized penalty to achieve robustness in estimation and variable selection. The performance of estimation and variable selection can

  • Selection and integration of generalized instrumental variables for estimating total effects
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-28
    Ryusei Shingaki, Hiroshi Kanda, Manabu Kuroki

    We consider a situation where cause–effect relationships between variables can be described as a directed acyclic graph (DAG) and the corresponding linear structural equation model (linear SEM). When several pairs of instrumental variables (IVs) and covariates (IV-pairs; Pearl in: Proceedings of the 20th conference on uncertainty in artificial intelligence, AUAI Press, Arlington, Virginia, United States

  • A self-normalization break test for correlation matrix
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-21
    Ji-Eun Choi, Dong Wan Shin

    We construct a new test for correlation matrix break based on the self-normalization method. The self-normalization test has practical advantage over the existing test: easy and stable implementation; not having the singularity issue and the bandwidth selection issue of the existing test; remedying size distortion problem of the existing test under (near) singularity, serial dependence, conditional

  • Testing skew-symmetry based on extreme ranked set sampling
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-12
    Parisa Hasanalipour, Mostafa Razmkhah

    The problem of testing skew-symmetry of a distribution is studied in a general model of skew distributions. Toward this end, an order statistic-based test is first introduced to test the null hypotheses of symmetry against the alternative of skew-symmetry of a distribution. Some properties of this test are also studied. Then, using the idea of ranked set sampling, some appropriate sampling schemes

  • Optimal robust estimators for families of distributions on the integers
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-10
    Ricardo A. Maronna, Victor J. Yohai

    Let \(F_{\theta }\) be a family of distributions with support on the set of nonnegative integers \(Z_{0}\). In this paper we derive the M-estimators with smallest gross error sensitivity (GES). We start by defining the uniform median of a distribution F with support on \(Z_{0}\) (umed(F)) as the median of \(x+u,\) where x and u are independent variables with distributions F and uniform in [-0.5,0.5]

  • On nonparametric tests of multivariate meta-ellipticity
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-10
    Jean-François Quessy

    A statistical procedure to determine if the dependence structure of a multivariate random vector belongs or not to the general class of elliptical copulas has been proposed by Jaser et al. (Depend Model 5:330–353, 2017). Their test exploits the fact that when the copula of a multivariate population is elliptical, the theoretical Kendall and Blomqvist dependence measures of each pair are the same. Under

  • Bayesian prediction of spatial data with non-ignorable missingness
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-10
    Samira Zahmatkesh, Mohsen Mohammadzadeh

    In spatial data, especially in geostatistics data where measurements are often provided by satellite scanning, some parts of data may get missed. Due to spatial dependence in the data, these missing values probably are caused by some latent spatial random fields. In this case, ignoring missingness is not logical and may lead to invalid inferences. Thus incorporating the missingness process model into

  • Variables acceptance reliability sampling plan based on degradation test
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-05
    Ji Hwan Cha, F. G. Badía

    Until now, various acceptance reliability sampling plans have been developed based on different life tests. In most of the reliability sampling plans, the decision procedures are based on the lifetimes of the items observed on tests, or the number of failures observed during a pre-specified testing time. However, frequently, the items are subject to degradation phenomena and, in these cases, the observed

  • Robust estimation of single index models with responses missing at random
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-05
    Ash Abebe, Huybrechts F. Bindele, Masego Otlaadisa, Boikanyo Makubate

    A single-index regression model is considered, where some responses in the model are assumed to be missing at random. Local linear rank-based estimators of the single-index direction and the unknown link function are proposed. Asymptotic properties of the estimators are established under mild regularity conditions. Monte Carlo simulation experiments show that the proposed estimators are more efficient

  • On the problems of sequential statistical inference for Wiener processes with delayed observations
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-03
    Pavel V. Gapeev

    We study the sequential hypothesis testing and quickest change-point (or disorder) detection problems with linear delay penalty costs for observable Wiener processes under (constantly) delayed detection times. The method of proof consists of the reduction of the associated delayed optimal stopping problems for one-dimensional diffusion processes to the equivalent free-boundary problems and solution

  • Strong convergence properties for weighted sums of m -asymptotic negatively associated random variables and statistical applications
    Stat. Pap. (IF 1.433) Pub Date : 2020-06-03
    Yi Wu, Xuejun Wang, Aiting Shen

    In this paper, we establish a general result on complete moment convergence and the Marcinkiewicz–Zygmund-type strong law of large numbers for weighted sums of m-asymptotic negatively associated random variables, which improve and extend some existing ones. As applications of our main results, we present a result on complete consistency for the weighted estimator in a nonparametric regression model

  • Changepoint in dependent and non-stationary panels
    Stat. Pap. (IF 1.433) Pub Date : 2020-05-24
    Matúš Maciak; Michal Pešta; Barbora Peštová

    Detection procedures for a change in means of panel data are proposed. Unlike classical inference tools used for the changepoint analysis in the panel data framework, we allow for mutually dependent and generally non-stationary panels with an extremely short follow-up period. Two competitive self-normalized test statistics are employed and their asymptotic properties are derived for a large number

  • Change-point methods for multivariate time-series: paired vectorial observations
    Stat. Pap. (IF 1.433) Pub Date : 2020-04-28
    Zdeněk Hlávka; Marie Hušková; Simos G. Meintanis

    We consider paired and two-sample break-detection procedures for vectorial observations and multivariate time series. The new methods involve L2-type criteria based on empirical characteristic functions and are easy to compute regardless of dimension. We obtain asymptotic results that allow for application of the methods to a wide range of settings involving on-line as well as retrospective circumstances

  • Optimal $$2^K$$2K paired comparison designs for third-order interactions
    Stat. Pap. (IF 1.433) Pub Date : 2020-04-24
    Eric Nyarko

    In psychological research often paired comparisons are used in which either full or partial profiles of the alternatives described by a common set of two-level attributes are presented. For this situation the problem of finding optimal designs is considered in the presence of third-order interactions.

  • On mean derivative estimation of longitudinal and functional data: from sparse to dense
    Stat. Pap. (IF 1.433) Pub Date : 2020-04-07
    Hassan Sharghi Ghale-Joogh, S. Mohammad E. Hosseini-Nasab

    Derivative estimation of the mean of longitudinal and functional data is useful, because it provides a quantitative measure of changes in the mean function that can be used for modeling of the data. We propose a general method for estimation of the derivative of the mean function that allows us to make inference about both longitudinal and functional data regardless of the sparsity of data. The \(L^2\)

  • Uniform projection nested Latin hypercube designs
    Stat. Pap. (IF 1.433) Pub Date : 2020-04-06
    Hao Chen, Yan Zhang, Xue Yang

    Computer experiments usually involve many factors, but only a few of them are active. In such a case, it is desirable to construct designs with good projection properties. Maximum projection designs and uniform projection designs have been developed for common experimental situations, however, there has been little study on constructing projection designs for high-accuracy computer experiments (HEs)

  • Rationalization of detection of the multiple disorders
    Stat. Pap. (IF 1.433) Pub Date : 2020-03-21
    Krzysztof J. Szajowski

    We will analyze the importance of elements of a complex structure on the availability of the system. The basis for the element assessment are the importance measures for multi-state systems introduced by Birnbaum (in: Krishaiah, Econometrics, principal components, reliability, and applications, Academic Press, New York, 1969) and Barlow and Proshan (Stoch Process Appl 3:153–173, 1975). The availability

  • Order patterns, their variation and change points in financial time series and Brownian motion
    Stat. Pap. (IF 1.433) Pub Date : 2020-03-20
    Christoph Bandt

    Order patterns and permutation entropy have become useful tools for studying biomedical, geophysical or climate time series. Here we study day-to-day market data, and Brownian motion which is a good model for their order patterns. A crucial point is that for small lags (1 up to 6 days), pattern frequencies in financial data remain essentially constant. The two most important order parameters of a time

  • Convergence of U -processes in Hölder spaces with application to robust detection of a changed segment
    Stat. Pap. (IF 1.433) Pub Date : 2020-01-29
    Alfredas Račkauskas; Martin Wendler

    To detect a changed segment (so called epidemic changes) in a time series, variants of the CUSUM statistic are frequently used. However, they are sensitive to outliers in the data and do not perform well for heavy tailed data, especially when short segments get a high weight in the test statistic. We will present a robust test statistic for epidemic changes based on the Wilcoxon statistic. To study

  • Estimating change points in nonparametric time series regression models
    Stat. Pap. (IF 1.433) Pub Date : 2020-01-27
    Maria Mohr; Leonie Selk

    In this paper we consider a regression model that allows for time series covariates as well as heteroscedasticity with a regression function that is modelled nonparametrically. We assume that the regression function changes at some unknown time \(\lfloor ns_0\rfloor \), \(s_0\in (0,1)\), and our aim is to estimate the (rescaled) change point \(s_0\). The considered estimator is based on a Kolmogorov-Smirnov

  • Asymptotic properties of maximum likelihood estimators with sample size recalculation
    Stat. Pap. (IF 1.433) Pub Date : 2019-02-28
    Sergey Tarima; Nancy Flournoy

    Consider an experiment in which the primary objective is to determine the significance of a treatment effect at a predetermined type I error and statistical power. Assume that the sample size required to maintain these type I error and power will be re-estimated at an interim analysis. A secondary objective is to estimate the treatment effect. Our main finding is that the asymptotic distributions of

  • Multi-part balanced incomplete-block designs
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-23
    R. A. Bailey; Peter J. Cameron

    We consider designs for cancer trials which allow each medical centre to treat only a limited number of cancer types with only a limited number of drugs. We specify desirable properties of these designs, and prove some consequences. Then we give several different constructions. Finally we generalize this to three or more factors, such as biomarkers.

  • Bregman divergences based on optimal design criteria and simplicial measures of dispersion
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-16
    Luc Pronzato; Henry P. Wynn; Anatoly Zhigljavsky

    In previous work the authors defined the k-th order simplicial distance between probability distributions which arises naturally from a measure of dispersion based on the squared volume of random simplices of dimension k. This theory is embedded in the wider theory of divergences and distances between distributions which includes Kullback–Leibler, Jensen–Shannon, Jeffreys–Bregman divergence and Bhattacharyya

  • Distribution of the multivariate nonlinear LS estimator under an uncertain input
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-10
    Andrej Pázman

    The aim of the paper is to develop further the approach presented in Pázman (Nonlinear Stat Model, Kluwer, Dordrecht, 1993a) for the computation of the probability density of a least squares estimator for moderate size samples in nonlinear regression. We consider here cases when the variance matrix of observations is not known, hence, it can not be used for the definition of the parameter estimator

  • Locally D -optimal designs for a wider class of non-linear models on the k -dimensional ball
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-07
    Martin Radloff; Rainer Schwabe

    In this paper we extend the results of Radloff and Schwabe (arXiv:1806.00275, 2018), which could be applied for example to Poisson regression, negative binomial regression and proportional hazard models with censoring, to a wider class of non-linear multiple regression models. This includes the binary response models with logit and probit link besides others. For this class of models we derive (locally)

  • Optimality of block designs under the model with the first-order circular autoregression
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-02
    Katarzyna Filipiak; Razieh Khodsiani; Augustyn Markiewicz

    In this paper optimal properties of some circular balanced block designs under the model with circular autoregression of order one are studied. Universal optimality of some balanced block designs with equal block sizes is proven and E-optimality of complete balanced block designs with the number of blocks equal to the number of treatments or the number of treatments reduced by two is shown.

  • Adaptive designs for drug combination informed by longitudinal model for the response
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-02
    Tobias Mielke; Vladimir Dragalin

    Objectives in Phase II drug combination studies are to estimate the efficacy response surface for the combination of doses of different drugs and to select the most efficient combination for the final Phase III clinical trial. One problem is to find an optimal design that allocates subjects to the dose-combinations which will maximize the information obtained in the trial. Adaptive designs help in

  • Optimal design of inspection times for interval censoring
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-02
    Nadja Malevich; Christine H. Müller

    We treat optimal equidistant and optimal non-equidistant inspection times for interval censoring of exponential distributions. We provide in particular a new approach for determining the optimal non-equidistant inspection times. The resulting recursive formula is related to a formula for optimal spacing of quantiles for asymptotically best linear estimates based on order statistics and to a formula

  • Optimal designs for minimax-criteria in random coefficient regression models
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-01
    Maryna Prus

    We consider minimax-optimal designs for the prediction of individual parameters in random coefficient regression models. We focus on the minimax-criterion, which minimizes the “worst case” for the basic criterion with respect to the covariance matrix of random effects. We discuss particular models: linear and quadratic regression, in detail.

  • On the aberrations of mixed level orthogonal arrays with removed runs
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-01
    Roberto Fontana; Fabio Rapallo

    Given an orthogonal array we analyze the aberrations of the sub-fractions which are obtained by the deletion of some of its points. We provide formulae to compute the Generalized Word-Length Pattern of any sub-fraction. In the case of the deletion of one single point, we provide a simple methodology to find which the best sub-fractions are according to the Generalized Minimum Aberration criterion.

  • Randomization-based inference and the choice of randomization procedures
    Stat. Pap. (IF 1.433) Pub Date : 2019-01-01
    Yanying Wang; William F. Rosenberger; Diane Uschner

    In testing the significance of treatment effects in randomized clinical trials (RCTs), randomization-based inference is distinguished from population-based parametric and nonparametric inference, such as the t-test or permutation tests, taking into account three properties: preservation of type I error rate, relation of power to the randomization procedure, and flexibility in choosing the test statistic

  • Optimal subsampling for softmax regression
    Stat. Pap. (IF 1.433) Pub Date : 2018-12-18
    Yaqiong Yao; HaiYing Wang

    To meet the challenge of massive data, Wang et al. (J Am Stat Assoc 113(522):829–844, 2018b) developed an optimal subsampling method for logistic regression. The purpose of this paper is to extend their method to softmax regression, which is also called multinomial logistic regression and is commonly used to model data with multiple categorical responses. We first derive the asymptotic distribution

  • Optimal designs for K -factor two-level models with first-order interactions on a symmetrically restricted design region
    Stat. Pap. (IF 1.433) Pub Date : 2018-12-17
    Fritjof Freise; Rainer Schwabe

    We develop D-optimal designs for linear models with first-order interactions on a subset of the \(2^K\) full factorial design region, when both the number of factors set to the higher level and the number of factors set to the lower level are simultaneously bounded by the same threshold. It turns out that in the case of narrow margins the optimal design is concentrated only on those design points,

  • An unexpected connection between Bayes A -optimal designs and the group lasso
    Stat. Pap. (IF 1.433) Pub Date : 2018-12-13
    Guillaume Sagnol; Edouard Pauwels

    We show that the A-optimal design optimization problem over m design points in \({\mathbb {R}}^n\) is equivalent to minimizing a quadratic function plus a group lasso sparsity inducing term over \(n\times m\) real matrices. This observation allows to describe several new algorithms for A-optimal design based on splitting and block coordinate decomposition. These techniques are well known and proved

  • Shrinkage for covariance estimation: asymptotics, confidence intervals, bounds and applications in sensor monitoring and finance
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-17
    Ansgar Steland

    When shrinking a covariance matrix towards (a multiple) of the identity matrix, the trace of the covariance matrix arises naturally as the optimal scaling factor for the identity target. The trace also appears in other context, for example when measuring the size of a matrix or the amount of uncertainty. Of particular interest is the case when the dimension of the covariance matrix is large. Then the

  • When and when not to use optimal model averaging
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-14
    Michael Schomaker, Christian Heumann

    Traditionally model averaging has been viewed as an alternative to model selection with the ultimate goal to incorporate the uncertainty associated with the model selection process in standard errors and confidence intervals by using a weighted combination of candidate models. In recent years, a new class of model averaging estimators has emerged in the literature, suggesting to combine models such

  • Nonparametric estimators of probability characteristics using unbiased prior conditions
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-12
    Yury G. Dmitriev; Gennady M. Koshkin

    A class of nonparametric estimators of the main functional of distribution constructed by making use auxiliary information is proposed. It is shown that the knowledge usage of other distribution functionals in estimation of the main functional can often provide the mean squared error (MSE) smaller than that of estimators constructed without such auxiliary information. In the paper, the adaptive estimators

  • Analysis of loss systems with overlapping resource requirements
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-11
    Konstantin Samouylov; Yuliya Gaidamaka

    In this paper, a queueing system with multicast service, random resource requirements and general probability distributions of busy and idle periods is considered. Motivated by challenges from practical applications in the area of performance analysis of telecommunication systems, the model is built on the basis of well-known resource queueing systems theory. Two distinctive features of the model related

  • On Markovian modelling of arrival processes
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-10
    Gely Basharin; Valeriy Naumov; Konstantin Samouylov

    Markovian arrival process (MAP) is a popular tool for modeling arrival processes of stochastic systems such as queueing systems, reliability systems and telecommunications networks. In this paper we show how properties of Markovian Arrival Processes can be derived from the general theory of Markov processes with a homogeneous second component. We also present a series of results on queueing systems

  • Testing for serial independence in vector autoregressive models
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-07
    Simos G. Meintanis; Joseph Ngatchou-Wandji; James Allison

    We consider tests for serial independence of arbitrary finite order for the innovations in vector autoregressive models. The tests are expressed as L2-type criteria involving the difference of the joint empirical characteristic function and the product of corresponding marginals. Asymptotic as well as Monte-Carlo results are presented.

  • Block tensor train decomposition for missing data estimation
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-06
    Namgil Lee; Jong-Min Kim

    We propose a method for imputation of missing values in large scale matrix data based on a low-rank tensor approximation technique called the block tensor train (BTT) decomposition. Given sparsely observed data points, the proposed method iteratively computes the singular value decomposition (SVD) of the underlying data matrix with missing values. The SVD of the matrices is performed based on a low-rank

  • Effective coefficients in the electromagnetic logging problem with log-normal distribution, multiscale conductivity and permittivity
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-05
    Olga N. Soboleva; Mikhail I. Epov; Ekaterina P. Kurochkina

    The effective coefficients for Maxwell’s equations in the frequency domain for a multiscale isotropic medium by using a subgrid modeling approach are calculated. The correlated fields of conductivity and permittivity are approximated by the Kolmogorov multiplicative continuous cascades with a lognormal probability distribution. The wavelength is assumed to be large when compared with the scale of heterogeneities

  • A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose.
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-05
    Víctor Casero-Alonso,Andrey Pepelyshev,Weng K Wong

    Hormesis has been widely observed and debated in a variety of context in biomedicine and toxicological sciences. Detecting its presence can be an important problem with wide ranging implications. However, there is little work on constructing an efficient experiment to detect its existence or estimate the threshold dose. We use optimal design theory to develop a variety of locally optimal designs to

  • On steady state probabilities of renewable system with Marshal–Olkin failure model
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-04
    V. Rykov

    A heterogeneous double redundant hot-standby renewable system with Marshal–Olkin failure model is considered. Steady-state characteristics for such system are calculated. The problem of sensitivity of these characteristics to the shape of repair time distributions for this model is studied.

  • Stochastic models of atmospheric clouds structure
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-04
    Vasily A. Ogorodnikov; Evgeniya G. Kablukova; Sergei M. Prigarin

    This paper deals with numerical simulation of random fields corresponding to a stochastic structure of atmospheric clouds. We construct numerical models to simulate the optical thickness of stratus clouds as well as indicator fields of broken clouds. To simulate the random fields we use spectral and autoregressive models, as well as a method of nonlinear transformations of Gaussian functions.

  • On the evaluation of spatial–angular distributions of polarization characteristics of scattered radiation
    Stat. Pap. (IF 1.433) Pub Date : 2018-09-01
    Natalya Tracheva; Sergey Ukhinov

    This paper is focused on a Monte Carlo based projective algorithm for the estimation of bidirectional angular characteristics of scattered polarized radiation in the context of different sets of basic functions, normalized with certain weights. We consider hemispherical harmonics designed on the basis of associated shifted Jacobi polynomials in comparison with those designed as a factorization of modified

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