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  • General flation models for count data
    Metrika (IF 0.679) Pub Date : 2020-07-10
    Dankmar Böhning, Helen E. Ogden

    The paper discusses very general extensions to existing inflation models for discrete random variables, allowing an arbitrary set of points in the sample space to be either inflated or deflated relative to a baseline distribution. The term flation is introduced to cover either inflation or deflation of counts. Examples include one-inflated count models where the baseline distribution is zero-truncated

    更新日期:2020-07-10
  • Maximin distance designs based on densest packings
    Metrika (IF 0.679) Pub Date : 2020-07-09
    Liuqing Yang, Yongdao Zhou, Min-Qian Liu

    Computer experiments play a crucial role when physical experiments are expensive or difficult to be carried out. As a kind of designs for computer experiments, maximin distance designs have been widely studied. Many existing methods for obtaining maximin distance designs are based on stochastic algorithms, and these methods will be infeasible when the run size or number of factors is large. In this

    更新日期:2020-07-10
  • On the properties of hermite series based distribution function estimators
    Metrika (IF 0.679) Pub Date : 2020-07-09
    Michael Stephanou, Melvin Varughese

    Hermite series based distribution function estimators have recently been applied in the context of sequential quantile estimation. These distribution function estimators are particularly useful because they allow the online (sequential) estimation of the full cumulative distribution function. This is in contrast to the empirical distribution function estimator and smooth kernel distribution function

    更新日期:2020-07-09
  • Least squares moment identification of binary regression mixture models
    Metrika (IF 0.679) Pub Date : 2020-07-08
    Benjamin Auder, Elisabeth Gassiat, Mor Absa Loum

    We consider finite mixtures of generalized linear models with binary output. We prove that cross moments (between the output and the regression variables) up to order three are sufficient to identify all parameters of the model. We propose a least-squares estimation method based on those moments and we prove the consistency and the Gaussian asymptotic behavior of the estimator. We provide simulation

    更新日期:2020-07-09
  • Asymptotic properties of mildly explosive processes with locally stationary disturbance
    Metrika (IF 0.679) Pub Date : 2020-07-06
    Junichi Hirukawa, Sangyeol Lee

    In this study, we derive the limiting distribution of the least squares estimator (LSE) and the localized LSE for mildly explosive autoregressive models with locally stationary disturbance and verify that it is Cauchy as in the iid case. We also investigate the limiting distribution of two types of Dickey–Fuller unit root tests, designed for detecting a bubble period in economic time series data, and

    更新日期:2020-07-07
  • Parallel inference for big data with the group Bayesian method
    Metrika (IF 0.679) Pub Date : 2020-06-25
    Guangbao Guo, Guoqi Qian, Lu Lin, Wei Shao

    In recent years, big datasets are often split into several subsets due to the storage requirements. We propose a parallel group Bayesian method for statistical inference in sparse big data. This method improves the existing methods in two aspects: the total datasets are also split into a data subset sequence and the parameter vector is divided into several sub-vectors. Besides, we add a weight sequence

    更新日期:2020-06-26
  • On the ARCH model with stationary liquidity
    Metrika (IF 0.679) Pub Date : 2020-06-24
    Marko Voutilainen, Pauliina Ilmonen, Soledad Torres, Ciprian Tudor, Lauri Viitasaari

    The classical ARCH model together with its extensions have been widely applied in the modeling of financial time series. We study a variant of the ARCH model that takes account of liquidity given by a positive stationary process. We provide minimal assumptions that ensure the existence and uniqueness of the stationary solution for this model. Moreover, we give necessary and sufficient conditions for

    更新日期:2020-06-25
  • Random discretization of stationary continuous time processes
    Metrika (IF 0.679) Pub Date : 2020-06-23
    Anne Philippe, Caroline Robet, Marie-Claude Viano

    This paper investigates second order properties of a stationary continuous time process after random sampling. While a short memory process always gives rise to a short memory one, we prove that long-memory can disappear when the sampling law has very heavy tails. Despite the fact that the normality of the process is not maintained by random sampling, the normalized partial sum process converges to

    更新日期:2020-06-23
  • A shrinkage approach to joint estimation of multiple covariance matrices
    Metrika (IF 0.679) Pub Date : 2020-06-19
    Zongliang Hu, Zhishui Hu, Kai Dong, Tiejun Tong, Yuedong Wang

    In this paper, we propose a shrinkage framework for jointly estimating multiple covariance matrices by shrinking the sample covariance matrices towards the pooled sample covariance matrix. This framework allows us to borrow information across different groups. We derive the optimal shrinkage parameters under the Stein and quadratic loss functions, and prove that our derived estimators are asymptotically

    更新日期:2020-06-19
  • Efficient Estimation for Varying-Coefficient Mixed Effects Models with Functional Response Data
    Metrika (IF 0.679) Pub Date : 2020-06-09
    Xiong Cai, Liugen Xue, Xiaolong Pu, Xingyu Yan

    In this article, we focus on the estimation of varying-coefficient mixed effects models for longitudinal and sparse functional response data, by using the generalized least squares method coupling a modified local kernel smoothing technique. This approach provides a useful framework that simultaneously takes into account the within-subject covariance and all observation information in the estimation

    更新日期:2020-06-09
  • Statistical inference based on a new weighted likelihood approach
    Metrika (IF 0.679) Pub Date : 2020-06-06
    Suman Majumder, Adhidev Biswas, Tania Roy, Subir Kumar Bhandari, Ayanendranath Basu

    We discuss a new weighted likelihood method for robust parametric estimation. The method is motivated by the need for generating a simple estimation strategy which provides a robust solution that is simultaneously fully efficient when the model is correctly specified. This is achieved by appropriately weighting the score function at each observation in the maximum likelihood score equation. The weight

    更新日期:2020-06-06
  • An empirical likelihood method for quantile regression models with censored data
    Metrika (IF 0.679) Pub Date : 2020-06-05
    Qibing Gao, Xiuqing Zhou, Yanqin Feng, Xiuli Du, XiaoXiao Liu

    An estimation for censored quantile regression models, which is based on an inverse-censoring-probability weighting method, is studied in this paper, and asymptotic distribution of the parameter vector estimator is obtained. Based on the parameter estimation and asymptotic distribution of the estimator, an empirical likelihood inference method is proposed for censored quantile regression models and

    更新日期:2020-06-05
  • Efficient crossover designs for non-regular settings
    Metrika (IF 0.679) Pub Date : 2020-06-05
    Rakhi Singh, Joachim Kunert

    Crossover designs are called for in situations when several subjects undergo a sequence of treatments. Though, usually, the model contains the direct effects of treatments as well as the carryover effects, the primary interest lies in the estimation of direct effects of the treatment. Most results in the literature on crossover designs are available for the situations where either the number of periods

    更新日期:2020-06-05
  • On the equivalence between the LRT and F-test for testing variance components in a class of linear mixed models
    Metrika (IF 0.679) Pub Date : 2020-05-28
    Fares Qeadan, Ronald Christensen

    For the special case of balanced one-way random effects ANOVA, it has been established that the generalized likelihood ratio test (LRT) and Wald’s test are largely equivalent in testing the variance component. We extend these results to explore the relationships between Wald’s F test, and the LRT for a much broader class of linear mixed models; the generalized split-plot models. In particular, we explore

    更新日期:2020-05-28
  • Conditional maximum Lq-likelihood estimation for regression model with autoregressive error terms
    Metrika (IF 0.679) Pub Date : 2020-05-20
    Yeşim Güney, Y. Tuaç, Ş. Özdemir, O. Arslan

    In this article, we consider the parameter estimation of regression model with pth-order autoregressive (AR(p)) error term. We use the maximum Lq-likelihood (MLq) estimation method proposed by Ferrari and Yang (Ann Stat 38(2):753–783, 2010), as a robust alternative to the classical maximum likelihood (ML) estimation method to handle the outliers in the data. After exploring the MLq estimators for the

    更新日期:2020-05-20
  • A general multivariate new better than used (MNBU) distribution and its properties
    Metrika (IF 0.679) Pub Date : 2020-04-30
    Hyunju Lee, Ji Hwan Cha

    In this paper, we develop a general multivariate new better than used (MNBU) distribution based on a multivariate common shock model. Assuming that the external shock process follows the generalized Pólya process and a shock can destroy each component with some given probability, the multivariate survival distribution is obtained. The dependence structure of the multivariate distribution is analyzed

    更新日期:2020-04-30
  • A unified approach to constructing correlation coefficients between random variables
    Metrika (IF 0.679) Pub Date : 2020-01-01
    Majid Asadi, Somayeh Zarezadeh

    Measuring the correlation between two random variables is an important goal in various statistical applications. The standardized covariance is a widely used criterion for measuring the linear association. In this paper, first, we propose a covariance-based unified measure of variability for a continuous random variable X and show that several measures of variability and uncertainty, such as variance

    更新日期:2020-01-01
  • Convergence rate of kernel regression estimation for time series data when both response and covariate are functional
    Metrika (IF 0.679) Pub Date : 2019-12-18
    Nengxiang Ling, Lingyu Wang, Philippe Vieu

    We investigate kernel estimates in the functional nonparametric regression model when both the response and the explanatory variable (the covariate) are functional. The rates of almost complete and uniform almost complete convergence of the estimator are obtained under some mild \(\alpha \)-mixing functional sample. Finally, a simulation study is carried out to illustrate the finite sample performance

    更新日期:2019-12-18
  • Increasing concave orderings of linear combinations of order statistics with applications to social welfare
    Metrika (IF 0.679) Pub Date : 2019-12-04
    Antonia Castaño-Martínez, Gema Pigueiras, Georgios Psarrakos, Miguel A. Sordo

    We provide in this paper sufficient conditions for comparing, in terms of the increasing concave order, some income random variables based on linear combinations of order statistics that are relevant in the framework of social welfare. The random variables under study are weighted average incomes of the poorest and, for some particular weights, their expectations are welfare measures whose integral

    更新日期:2019-12-04
  • Reliability of a coherent system equipped with two cold standby components
    Metrika (IF 0.679) Pub Date : 2019-11-05
    Achintya Roy, Nitin Gupta

    In this paper, we focus on a particular type of coherent system which may fail either on the failure of its first component or on the failure of its second component. We investigate the renewal of such a coherent system using two cold standby components. We obtain the reliability function of the considered coherent system which is equipped with two cold standby components. We study the problem to optimize

    更新日期:2019-11-05
  • Classes of Multiple Decision Functions Strongly Controlling FWER and FDR.
    Metrika (IF 0.679) Pub Date : 2015-07-15
    Edsel A Peña,Joshua D Habiger,Wensong Wu

    Two general classes of multiple decision functions, where each member of the first class strongly controls the family-wise error rate (FWER), while each member of the second class strongly controls the false discovery rate (FDR), are described. These classes offer the possibility that optimal multiple decision functions with respect to a pre-specified Type II error criterion, such as the missed discovery

    更新日期:2019-11-01
  • Optimal Hypothesis Testing: From Semi to Fully Bayes Factors.
    Metrika (IF 0.679) Pub Date : 2010-03-04
    Albert Vexler,Chengqing Wu,Kai Fun Yu

    We propose and examine statistical test-strategies that are somewhat between the maximum likelihood ratio and Bayes factor methods that are well addressed in the literature. The paper shows an optimality of the proposed tests of hypothesis. We demonstrate that our approach can be easily applied to practical studies, because execution of the tests does not require deriving of asymptotical analytical

    更新日期:2019-11-01
  • Quadratic semiparametric Von Mises calculus.
    Metrika (IF 0.679) Pub Date : 2009-03-01
    James Robins,Lingling Li,Eric Tchetgen,Aad W van der Vaart

    We discuss a new method of estimation of parameters in semiparametric and nonparametric models. The method is based on U-statistics constructed from quadratic influence functions. The latter extend ordinary linear influence functions of the parameter of interest as defined in semiparametric theory, and represent second order derivatives of this parameter. For parameters for which the matching cannot

    更新日期:2019-11-01
  • Consistency for the negative binomial regression with fixed covariate
    Metrika (IF 0.679) Pub Date : 2019-10-24
    Rafael Weißbach, Lucas Radloff

    We model an overdispersed count as a dependent measurement, by means of the Negative Binomial distribution. We consider a quantitative covariate that is fixed by design. The expectation of the dependent variable is assumed to be a known function of a linear combination involving the possibly multidimensional covariate and its coefficients. In the NB1-parametrization of the Negative Binomial distribution

    更新日期:2019-10-24
  • Discriminant analysis based on binary time series
    Metrika (IF 0.679) Pub Date : 2019-10-12
    Yuichi Goto, Masanobu Taniguchi

    Binary time series can be derived from an underlying latent process. In this paper, we consider an ellipsoidal alpha mixing strictly stationary process and discuss the discriminant analysis and propose a classification method based on binary time series. Assume that the observations are generated by time series which belongs to one of two categories described by different spectra. We propose a method

    更新日期:2019-10-12
  • Inequalities for Gaussian random variables under Archimedean copula dependence
    Metrika (IF 0.679) Pub Date : 2019-10-09
    Longxiang Fang, Wenyu Huang

    In this paper, we investigate two inequalities based on majorization for two random vectors with different Gaussian marginals and the same underlying Archimedean copulas. The established inequalities generalize well-known results by Slepian.

    更新日期:2019-10-09
  • Minimax estimation of a bivariate cumulative distribution function
    Metrika (IF 0.679) Pub Date : 2019-09-24
    Rafał Połoczański, Maciej Wilczyński

    The problem of estimating a bivariate cumulative distribution function F under the weighted squared error loss and the weighted Cramer–von Mises loss is considered. No restrictions are imposed on the unknown function F. Estimators, which are minimax among procedures being affine transformation of the bivariate empirical distribution function, are found. Then it is proved that these procedures are minimax

    更新日期:2019-09-24
  • Locally D -optimal designs for heteroscedastic polynomial measurement error models
    Metrika (IF 0.679) Pub Date : 2019-09-09
    Min-Jue Zhang, Rong-Xian Yue

    This paper considers constructions of optimal designs for heteroscedastic polynomial measurement error models. Corresponding approximate design theory is developed by using corrected score function approach, which leads to non-concave optimisation problems. For the weighted polynomial measurement error model of degree p with some commonly used heteroscedastic structures, the upper bounds for the number

    更新日期:2019-09-09
  • Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses
    Metrika (IF 0.679) Pub Date : 2019-09-07
    Xianwen Ding, Jiandong Chen, Xueping Chen

    The paper concerns the regularized quantile regression for ultrahigh-dimensional data with responses missing not at random. The propensity score is specified by the semiparametric exponential tilting model. We use the Pearson Chi-square type test statistic for identification of the important features in the sparse propensity score model, and employ the adjusted empirical likelihood method for estimation

    更新日期:2019-09-07
  • Quadrupling: construction of uniform designs with large run sizes
    Metrika (IF 0.679) Pub Date : 2019-08-27
    Hongyi Li, Hong Qin

    Fractional factorial designs are widely used because of their various merits. Foldover or level permutation are usually used to construct optimal fractional factorial designs. In this paper, a novel method via foldover and level permutation, called quadrupling, is proposed to construct uniform four-level designs with large run sizes. The relationship of uniformity between the initial design and the

    更新日期:2019-08-27
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