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  Subjects -> STATISTICS (Total: 130 journals)
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Journal Cover Metrika
  [SJR: 0.605]   [H-I: 30]   [2 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1435-926X - ISSN (Online) 0026-1335
   Published by Springer-Verlag Homepage  [2335 journals]
  • Conditional empirical likelihood for quantile regression models
    • Authors: Wu Wang; Zhongyi Zhu
      Pages: 1 - 16
      Abstract: In this paper, we propose a new Bayesian quantile regression estimator using conditional empirical likelihood as the working likelihood function. We show that the proposed estimator is asymptotically efficient and the confidence interval constructed is asymptotically valid. Our estimator has low computation cost since the posterior distribution function has explicit form. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies.
      PubDate: 2017-01-01
      DOI: 10.1007/s00184-016-0588-6
      Issue No: Vol. 80, No. 1 (2017)
  • Robust feature screening for varying coefficient models via quantile
           partial correlation
    • Authors: Xiang-Jie Li; Xue-Jun Ma; Jing-Xiao Zhang
      Pages: 17 - 49
      Abstract: This article is concerned with feature screening for varying coefficient models with ultrahigh-dimensional predictors. We propose a new sure independence screening method based on quantile partial correlation (QPC-SIS), which is quite robust against outliers and heavy-tailed distributions. Then we establish the sure screening property for the QPC-SIS, and conduct simulations to examine its finite sample performance. The results of simulation study indicate that the QPC-SIS performs better than other methods like sure independent screening (SIS), sure independent ranking and screening, distance correlation-sure independent screening, conditional correlation sure independence screening and nonparametric independent screening, which shows the validity and rationality of QPC-SIS.
      PubDate: 2017-01-01
      DOI: 10.1007/s00184-016-0589-5
      Issue No: Vol. 80, No. 1 (2017)
  • Shrinkage estimation of the linear model with spatial interaction
    • Authors: Yueqin Wu; Yan Sun
      Pages: 51 - 68
      Abstract: The linear model with spatial interaction has attracted huge attention in the past several decades. Different from most existing research which focuses on its estimation, we study its variable selection problem using the adaptive lasso. Our results show that the method can identify the true model consistently, and the resulting estimator can be efficient as the oracle estimator which is obtained when the zero coefficients in the model are known. Simulation studies show that the proposed methods perform very well.
      PubDate: 2017-01-01
      DOI: 10.1007/s00184-016-0590-z
      Issue No: Vol. 80, No. 1 (2017)
  • On the residual lifetime of coherent systems with heterogeneous components
    • Authors: P. Samadi; M. Rezaei; M. Chahkandi
      Pages: 69 - 82
      Abstract: The residual lifetime is of significant interest in reliability and survival analysis. In this article, we obtain a mixture representation for the reliability function of the residual lifetime of a coherent system with heterogeneous components in terms of the reliability functions of residual lifetimes of order statistics. Some stochastic comparisons are made on the residual lifetimes of the systems. Some examples are also given to illustrate the main results.
      PubDate: 2017-01-01
      DOI: 10.1007/s00184-016-0591-y
      Issue No: Vol. 80, No. 1 (2017)
  • Asymptotics of self-weighted M-estimators for autoregressive models
    • Authors: Xinghui Wang; Shuhe Hu
      Pages: 83 - 92
      Abstract: In this paper, we consider a stationary autoregressive AR(p) time series \(y_t=\phi _0+\phi _1y_{t-1}+\cdots +\phi _{p}y_{t-p}+u_t\) . A self-weighted M-estimator for the AR(p) model is proposed. The asymptotic normality of this estimator is established, which includes the asymptotic properties under the innovations with finite or infinite variance. The result generalizes and improves the known one in the literature.
      PubDate: 2017-01-01
      DOI: 10.1007/s00184-016-0592-x
      Issue No: Vol. 80, No. 1 (2017)
  • Fiducial inference in the classical errors-in-variables model
    • Authors: Liang Yan; Rui Wang; Xingzhong Xu
      Pages: 93 - 114
      Abstract: For the slope parameter of the classical errors-in-variables model, existing interval estimations with finite length will have confidence level equal to zero because of the Gleser–Hwang effect. Especially when the reliability ratio is low and the sample size is small, the Gleser–Hwang effect is so serious that it leads to the very liberal coverages and the unacceptable lengths of the existing confidence intervals. In this paper, we obtain two new fiducial intervals for the slope. One is based on a fiducial generalized pivotal quantity and we prove that this interval has the correct asymptotic coverage. The other fiducial interval is based on the method of the generalized fiducial distribution. We also construct these two fiducial intervals for the other parameters of interest of the classical errors-in-variables model and introduce these intervals to a hybrid model. Then, we compare these two fiducial intervals with the existing intervals in terms of empirical coverage and average length. Simulation results show that the two proposed fiducial intervals have better frequency performance. Finally, we provide a real data example to illustrate our approaches.
      PubDate: 2017-01-01
      DOI: 10.1007/s00184-016-0593-9
      Issue No: Vol. 80, No. 1 (2017)
  • Robust Dickey–Fuller tests based on ranks for time series with
           additive outliers
    • Authors: V. A. Reisen; C. Lévy-Leduc; M. Bourguignon; H. Boistard
      Pages: 115 - 131
      Abstract: In this paper the unit root tests proposed by Dickey and Fuller (DF) and their rank counterpart suggested by Breitung and Gouriéroux (J Econom 81(1): 7–27, 1997) (BG) are analytically investigated under the presence of additive outlier (AO) contaminations. The results show that the limiting distribution of the former test is outlier dependent, while the latter one is outlier free. The finite sample size properties of these tests are also investigated under different scenarios of testing contaminated unit root processes. In the empirical study, the alternative DF rank test suggested in Granger and Hallman (J Time Ser Anal 12(3): 207–224, 1991) (GH) is also considered. In Fotopoulos and Ahn (J Time Ser Anal 24(6): 647–662, 2003), these unit root rank tests were analytically and empirically investigated and compared to the DF test, but with outlier-free processes. Thus, the results provided in this paper complement the studies of the previous works, but in the context of time series with additive outliers. Equivalently to DF and Granger and Hallman (J Time Ser Anal 12(3): 207–224, 1991) unit root tests, the BG test shows to be sensitive to AO contaminations, but with less severity. In practical situations where there would be a suspicion of additive outlier, the general conclusion is that the DF and Granger and Hallman (J Time Ser Anal 12(3): 207–224, 1991) unit root tests should be avoided, however, the BG approach can still be used.
      PubDate: 2017-01-01
      DOI: 10.1007/s00184-016-0594-8
      Issue No: Vol. 80, No. 1 (2017)
  • Bayesian estimation based on ranked set sample from Morgenstern type
           bivariate exponential distribution when ranking is imperfect
    • Authors: Manoj Chacko
      Abstract: In this paper we consider Bayes estimation based on ranked set sample when ranking is imperfect, in which units are ranked based on measurements made on an easily and exactly measurable auxiliary variable X which is correlated with the study variable Y. Bayes estimators under squared error loss function and LINEX loss function for the mean of the study variate Y, when (X, Y) follows a Morgenstern type bivariate exponential distribution, are obtained based on both usual ranked set sample and extreme ranked set sample. Estimation procedures developed in this paper are illustrated using simulation studies and a real data.
      PubDate: 2016-12-08
      DOI: 10.1007/s00184-016-0607-7
  • An ergodic theorem for proportions of observations that fall into random
           sets determined by sample quantiles
    • Authors: Anna Dembińska
      Abstract: Assume that a sequence of observations \((X_n; n\ge 1)\) forms a strictly stationary process with an arbitrary univariate cumulative distribution function. We investigate almost sure asymptotic behavior of proportions of observations in the sample that fall into a random region determined by a given Borel set and a sample quantile. We provide sufficient conditions under which these proportions converge almost surly and describe the law of the limiting random variable.
      PubDate: 2016-12-07
      DOI: 10.1007/s00184-016-0606-8
  • On the shape of the cross-ratio function in bivariate survival models
           induced by truncated and folded normal frailty distributions
    • Authors: Steffen Unkel
      Abstract: In shared frailty models for bivariate survival data the frailty is identifiable through the cross-ratio function (CRF), which provides a convenient measure of association for correlated survival variables. The CRF may be used to compare patterns of dependence across models and data sets. We explore the shape of the CRF for the families of one-sided truncated normal and folded normal frailty distributions.
      PubDate: 2016-12-07
      DOI: 10.1007/s00184-016-0608-6
  • Acceleration of the stochastic search variable selection via componentwise
           Gibbs sampling
    • Authors: Hengzhen Huang; Shuangshuang Zhou; Min-Qian Liu; Zong-Feng Qi
      Abstract: The stochastic search variable selection proposed by George and McCulloch (J Am Stat Assoc 88:881–889, 1993) is one of the most popular variable selection methods for linear regression models. Many efforts have been proposed in the literature to improve its computational efficiency. However, most of these efforts change its original Bayesian formulation, thus the comparisons are not fair. This work focuses on how to improve the computational efficiency of the stochastic search variable selection, but remains its original Bayesian formulation unchanged. The improvement is achieved by developing a new Gibbs sampling scheme different from that of George and McCulloch (J Am Stat Assoc 88:881–889, 1993). A remarkable feature of the proposed Gibbs sampling scheme is that, it samples the regression coefficients from their posterior distributions in a componentwise manner, so that the expensive computation of the inverse of the information matrix, which is involved in the algorithm of George and McCulloch (J Am Stat Assoc 88:881–889, 1993), can be avoided. Moreover, since the original Bayesian formulation remains unchanged, the stochastic search variable selection using the proposed Gibbs sampling scheme shall be as efficient as that of George and McCulloch (J Am Stat Assoc 88:881–889, 1993) in terms of assigning large probabilities to those promising models. Some numerical results support these findings.
      PubDate: 2016-11-22
      DOI: 10.1007/s00184-016-0604-x
  • Efficient paired choice designs with fewer choice pairs
    • Authors: Aloke Dey; Rakhi Singh; Ashish Das
      Abstract: For paired choice experiments, two new construction methods of designs are proposed for the estimation of the main effects. In many cases, these designs require about 30–50% fewer choice pairs than the existing designs and at the same time have reasonably high D-efficiencies for the estimation of the main effects. Furthermore, as against the existing efficient designs, our designs have higher D-efficiencies for the same number of choice pairs.
      PubDate: 2016-11-17
      DOI: 10.1007/s00184-016-0605-9
  • Imputation based statistical inference for partially linear quantile
           regression models with missing responses
    • Authors: Peixin Zhao; Xinrong Tang
      Abstract: In this paper, we consider the confidence interval construction for partially linear quantile regression models with missing response at random. We propose an imputation based empirical likelihood method to construct confidence intervals for the parametric components and the nonparametric components, and show that the proposed empirical log-likelihood ratios are both asymptotically Chi-squared in theory. Then, the confidence region for the parametric component and the pointwise confidence interval for the nonparametric component are constructed. Some simulation studies and a real data application are carried out to assess the performance of the proposed estimation method, and simulation results indicate that the proposed method is workable.
      PubDate: 2016-11-01
      DOI: 10.1007/s00184-016-0586-8
  • Qualitative robustness of estimators on stochastic processes
    • Authors: Katharina Strohriegl; Robert Hable
      Abstract: A lot of statistical methods originally designed for independent and identically distributed (i.i.d.) data are also successfully used for dependent observations. Still most theoretical investigations on robustness assume i.i.d. pairs of random variables. We examine an important property of statistical estimators—the qualitative robustness in the case of observations which do not fulfill the i.i.d. assumption. In the i.i.d. case qualitative robustness of a sequence of estimators is, according to Hampel (Ann Math Stat 42:1887–1896, 1971), ensured by continuity of the corresponding statistical functional. A similar result for the non-i.i.d. case is shown in this article. Continuity of the corresponding statistical functional still ensures qualitative robustness of the estimator as long as the data generating process satisfies a certain convergence condition on its empirical measure. Examples for processes providing such a convergence condition, including certain Markov chains or mixing processes, are given as well as examples for qualitatively robust estimators in the non-i.i.d. case.
      PubDate: 2016-11-01
      DOI: 10.1007/s00184-016-0582-z
  • Tree based diagnostic procedures following a smooth test of
    • Authors: Gilles R. Ducharme; Walid Al Akhras
      Abstract: This paper introduces a statistical procedure, to be applied after a goodness-of-fit test has rejected a null model, that provides diagnostic information to help the user decide on a better model. The procedure goes through a list of departures, each being tested by a local smooth test. The list is organized into a hierarchy by seeking answers to the questions “Where is the problem?” and “What is the problem there?”. This hierarchy allows to focus on finer departures as the data becomes more abundant. The procedure controls the family-wise Type 1 error rate. Simulations show that the procedure can succeed in providing useful diagnostic information.
      PubDate: 2016-11-01
      DOI: 10.1007/s00184-016-0585-9
  • A test of linearity in partial functional linear regression
    • Authors: Ping Yu; Zhongzhan Zhang; Jiang Du
      Abstract: This paper investigates the hypothesis test of the parametric component in partial functional linear regression. We propose a test procedure based on the residual sums of squares under the null and alternative hypothesis, and establish the asymptotic properties of the resulting test. A simulation study shows that the proposed test procedure has good size and power with finite sample sizes. Finally, we present an illustration through fitting the Berkeley growth data with a partial functional linear regression model and testing the effect of gender on the height of kids.
      PubDate: 2016-11-01
      DOI: 10.1007/s00184-016-0584-x
  • AR(1) model with skew-normal innovations
    • Authors: M. Sharafi; A. R. Nematollahi
      Abstract: In this paper, we consider an autoregressive model of order one with skew-normal innovations. We propose several methods for estimating the parameters of the model and derive the limiting distributions of the estimators. Then, we study some statistical properties and the regression behavior of the proposed model. Finally, we provide a Monte Carlo simulation study for comparing performance of estimators and consider a real time series to illustrate the applicability of the proposed model.
      PubDate: 2016-11-01
      DOI: 10.1007/s00184-016-0587-7
  • Nonparametric estimation in a mixed-effect Ornstein–Uhlenbeck model
    • Authors: Charlotte Dion
      Abstract: Two adaptive nonparametric procedures are proposed to estimate the density of the random effects in a mixed-effect Ornstein–Uhlenbeck model. First a kernel estimator is introduced with a new bandwidth selection method developed recently by Goldenshluger and Lepski (Ann Stat 39:1608–1632, 2011). Then, we adapt an estimator from Comte et al. (Stoch Process Appl 7:2522–2551, 2013) in the framework of small time interval of observation. More precisely, we propose an estimator that uses deconvolution tools and depends on two tuning parameters to be chosen in a data-driven way. The selection of these two parameters is achieved through a two-dimensional penalized criterion. For both adaptive estimators, risk bounds are provided in terms of integrated \(\mathbb {L}^2\) -error. The estimators are evaluated on simulations and show good results. Finally, these nonparametric estimators are applied to neuronal data and are compared with previous parametric estimations.
      PubDate: 2016-11-01
      DOI: 10.1007/s00184-016-0583-y
  • A study on the conditional inactivity time of coherent systems
    • Authors: S. Goli; M. Asadi
      Abstract: The study on the inactivity times is useful in evaluating the aging and reliability properties of coherent systems in reliability engineering. In the present paper, we investigate the inactivity time of a coherent system consisting of n i.i.d. components. We drive some mixture representations for the reliability function of conditional inactivity times of coherent systems under two specific conditions on the status of the system components. Some ageing and stochastic properties of the proposed conditional inactivity times are also explored.
      PubDate: 2016-10-25
      DOI: 10.1007/s00184-016-0600-1
  • Reliability parameters estimation for parallel systems under imperfect
    • Authors: Soumaya Ghnimi; Soufiane Gasmi; Arwa Nasr
      Abstract: We consider in this paper a parallel system consisting of \(\eta \) identical components. Each component works independently of the others and has a Weibull distributed inter-failure time. When the system fails, we assume that the repair maintenance is imperfect according to the Arithmetic Reduction of Age models ( \(ARA_{m}\) ) proposed by Doyen and Gaudoin. The purpose of this paper is to generate a simulated failure data of the whole system in order to forecast the behavior of the failure process. Besides, we estimate the maintenance efficiency and the reliability parameters of an imperfect repair following \(ARA_{m}\) models using maximum likelihood estimation method. Our method is tested with several data sets available from related sources. The real data set corresponds to the time between failures of a compressor which is tested by Likelihood Ratio Test (LR). An analysis of the importance and the effect of the memory order of imperfect repair classes ( \(ARA_{m}\) ) will be discussed using LR test.
      PubDate: 2016-10-22
      DOI: 10.1007/s00184-016-0603-y
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