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  • Almost sure limit behaviour of Pfeifer record values
    Statistics (IF 0.645) Pub Date : 2020-08-05
    Jacob Peter Schmidt; Udo Kamps

    Pfeifer record values are based on a double sequence of independent, non-identically distributed random variables. For Pfeifer records with a common baseline distribution function and depending on a sequence of positive parameters, almost sure convergence to a non-degenerate random variable is examined, and sufficient conditions for almost sure relative stability as well as related results are studied

    更新日期:2020-08-05
  • On numbers of observations in random regions determined by records
    Statistics (IF 0.645) Pub Date : 2020-06-30
    Anna Dembińska; Masoumeh Akbari; Jafar Ahmadi

    Near-record observations are the ones that occur between successive record times and within a fixed distance of the current record value. In this paper, the concept of near-record observations is generalized to the notion of observations that fall into a random region determined by a given record and a Borel set. Description of the distribution of the number of such observations is provided, and asymptotic

    更新日期:2020-06-30
  • Efficient estimation for the non-mixture cure model with current status data
    Statistics (IF 0.645) Pub Date : 2020-06-22
    Xiaoguang Wang; Bo Han

    Medical advances including the neoadjuvant anti-PD-1 immunotherapy play a role in promoting clinical outcomes such as improved overall and progression-free survival probabilities. This paper considers the regression analysis of current status data with a cured subgroup in the population using a semiparametric non-mixture cure model. We propose a sieve maximum likelihood estimation for the model with

    更新日期:2020-06-22
  • Exponentiality tests based on Basu characterization
    Statistics (IF 0.645) Pub Date : 2020-06-04
    M. D. Jiménez-Gamero; Bojana Milošević; Marko Obradović

    This paper proposes and studies two new classes of tests for exponentiality. Both of them are based on Basu's characterization of the exponential distribution. The null distributions of the test statistics are parameter free. Moreover, conveniently normalized, they are asymptotically normally distributed. The large sample behaviour of the proposed tests is studied for fixed alternatives, and for local

    更新日期:2020-06-04
  • Testing Kendall's τ for a large class of dependent sequences
    Statistics (IF 0.645) Pub Date : 2020-06-02
    Sinda Ammous

    Let (Xi,Yi)i∈Z be a stationary sequence of R2-valued random variables. To test if X1 and Y1 are correlated in the sense of Kendall, we propose a robust correction of the usual Kendall test, valid for a large class of dependent sequences. We also show that the condition on the dependency coefficients is quasi-optimal in a certain sense, and we illustrate our results trough different sets of simulation

    更新日期:2020-06-02
  • A jackknifed ridge estimator in probit regression model
    Statistics (IF 0.645) Pub Date : 2020-06-01
    Yasin Asar; Kadriye Kılınç

    In this study, the effects of multicollinearity on the maximum likelihood estimator are analyzed in the probit regression model. It is known that the near-linear dependencies in the design matrix affect the maximum likelihood estimation negatively, namely, the standard errors become so large so that the estimations are said to be inconsistent. Therefore, a new jackknifed ridge estimator is introduced

    更新日期:2020-06-01
  • Stochastic comparisons on sample extremes from independent or dependent gamma samples
    Statistics (IF 0.645) Pub Date : 2020-06-01
    Rui Fang; Boyang Wang

    This study deals with random variables from gamma samples. For independent samples, it is found that when all the shape parameters are not smaller than 1, a less weakly supermajorized shape parameter vector implies a larger sample minimum in the sense of the hazard rate order. When the shape parameters are the same, a less weakly submajorized scale parameter vector also leads to a larger sample minimum

    更新日期:2020-06-01
  • Limit theorems for univariate and bivariate order statistics with variable ranks
    Statistics (IF 0.645) Pub Date : 2020-05-28
    H. M. Barakat; E. M. Nigm; M. H. Harpy

    In this work, we investigate the asymptotic behaviour of the intermediate and central order statistics of a bivariate data by using the Reduced Ordering Principle (R-ordering). When, the sup-norm is used, we reveal the interrelation between the R-ordering principle and Marginal-Ordering Principle (M-ordering). The asymptotic behaviour of the intermediate and central bivariate order statistics based

    更新日期:2020-05-28
  • Quantile regression for doubly truncated data
    Statistics (IF 0.645) Pub Date : 2020-05-28
    Pao-sheng Shen

    Doubly truncated data arise when the event time of interest T is observed only if it falls within a subject-specific, possibly random, interval [U,V]. In this article, we study the problem of fitting a quantile regression model with doubly truncated data. Based on the non-parametric maximum likelihood estimator of K(t)=P(U

    更新日期:2020-05-28
  • On the rates of convergence of parallelized averaged stochastic gradient algorithms
    Statistics (IF 0.645) Pub Date : 2020-05-18
    Antoine Godichon-Baggioni; Sofiane Saadane

    The growing interest for high-dimensional and functional data analysis led in the last decade to important research developing a consequent amount of techniques. Parallelized algorithms, which consist of distributing and treat the data into different machines, for example, are a good answer to deal with large samples taking values in high-dimensional spaces. We introduce here a parallelized averaged

    更新日期:2020-05-18
  • Uncertain stochastic ridge estimation in partially linear regression models with elliptically distributed errors
    Statistics (IF 0.645) Pub Date : 2020-05-14
    Mahdi Roozbeh; Nor Aishah Hamzah

    In fitting a regression model to survey data, using additional information or prior knowledge, stochastic uncertainty occurs in specifying linear programming due to economic and financial studies. These stochastic constraints, definitely cause some changes in the classic estimators and their efficiencies. In this paper, stochastic shrinkage estimators and their positive parts are defined in the partially

    更新日期:2020-05-14
  • A bimodal gamma distribution: properties, regression model and applications
    Statistics (IF 0.645) Pub Date : 2020-05-13
    Roberto Vila; Letícia Ferreira; Helton Saulo; Fábio Prataviera; Edwin Ortega

    In this paper, we propose a bimodal gamma distribution using a quadratic transformation based on the alpha-skew-normal model. We discuss several properties of this distribution such as mean, variance, moments, hazard rate and entropy measures. Further, we propose a new regression model with censored data based on the bimodal gamma distribution. This regression model can be very useful to the analysis

    更新日期:2020-05-13
  • Exact likelihood-ratio tests for joint type-II censored exponential data
    Statistics (IF 0.645) Pub Date : 2020-05-13
    Xiaojun Zhu; N. Balakrishnan; Chengzhu Feng; Jiacheng Ni; Nanxi Yu; Weilin Zhou

    Several results based on the likelihood-ratio test have been discussed in the literature for testing the scale parameters of two exponential distributions under complete and different censored forms of data, many of which are asymptotic in nature. However, exact likelihood-ratio tests have been derived only for the case of complete and independent Type-II censored samples. In this paper, we first develop

    更新日期:2020-05-13
  • A bimodal gamma distribution: properties, regression model and applications
    Statistics (IF 0.645) Pub Date : 2020-05-13
    Roberto Vila; Letícia Ferreira; Helton Saulo; Fábio Prataviera; Edwin Ortega

    In this paper, we propose a bimodal gamma distribution using a quadratic transformation based on the alpha-skew-normal model. We discuss several properties of this distribution such as mean, variance, moments, hazard rate and entropy measures. Further, we propose a new regression model with censored data based on the bimodal gamma distribution. This regression model can be very useful to the analysis

    更新日期:2020-05-13
  • Exact likelihood-ratio tests for joint type-II censored exponential data
    Statistics (IF 0.645) Pub Date : 2020-05-13
    Xiaojun Zhu; N. Balakrishnan; Chengzhu Feng; Jiacheng Ni; Nanxi Yu; Weilin Zhou

    Several results based on the likelihood-ratio test have been discussed in the literature for testing the scale parameters of two exponential distributions under complete and different censored forms of data, many of which are asymptotic in nature. However, exact likelihood-ratio tests have been derived only for the case of complete and independent Type-II censored samples. In this paper, we first develop

    更新日期:2020-05-13
  • Ordering results of extreme order statistics from heterogeneous Gompertz–Makeham random variables
    Statistics (IF 0.645) Pub Date : 2020-04-07
    Priyanka Majumder; Shyamal Ghosh; Murari Mitra

    Gompertz–Makeham distribution, which is not a member of the location-scale family, has been widely used for describing human mortality, determining policies in insurance, establishing actuarial tables and growth models. In this paper, we study stochastic comparisons for extreme order statistics from independent heterogeneous Gompertz–Makeham samples. The comparisons are carried out in the sense of

    更新日期:2020-04-07
  • Projection-based classification for functional data
    Statistics (IF 0.645) Pub Date : 2020-04-07
    Nadiyeh Darabi; S. Mohammad E. Hosseini-Nasab

    The classification of functional data has many applications in a variety of problems. A popular method for functional data classification is based on distances of the observations to the class centroid. In this paper, we introduce a weighted version of the centroid classifier that is based on projection functions and can lead to asymptotically perfect classification. We select the projection functions

    更新日期:2020-04-07
  • Identifiability of parameters in longitudinal correlated Poisson and inflated beta regression model with non-ignorable missing mechanism
    Statistics (IF 0.645) Pub Date : 2020-04-03
    Elham Tabrizi; Ehsan Bahrami Samani; Mojtaba Ganjali

    The identifiability of a statistical model is an essential and necessary property. When a model is not identifiable, even an infinite number of observations cannot determine the true parameter. Non-identifiablity problem in generalized linear models with and without random effects is very common. Also it can occur in such models when the response variable has non-ignorably missing. Since the structure

    更新日期:2020-04-03
  • Copula versions of distance multivariance and dHSIC via the distributional transform – a general approach to construct invariant dependence measures
    Statistics (IF 0.645) Pub Date : 2020-04-01
    Björn Böttcher

    The multivariate Hilbert-Schmidt Independence Criterion (dHSIC) and distance multivariance allow to measure and test independence of an arbitrary number of random vectors with arbitrary dimensions. Here, we define versions which only depend on an underlying copula. The approach is based on the distributional transform, yielding dependence measures which always feature a natural invariance with respect

    更新日期:2020-04-01
  • Ensuring balance through optimal allocation of experimental units with known categorical covariates into two treatments
    Statistics (IF 0.645) Pub Date : 2020-03-24
    Samrat Hore; Anup Dewanji; Aditya Chatterjee

    The balanced allocation of experimental units with regard to various known covariates among several treatment groups, before the physical experiment takes place, is often considered to be the most reasonable allocation scheme in all intervention studies and clinical trials. It is well-known that covariate mean balance over various treatment groups ensures widely used D- and A-optimality. However, it

    更新日期:2020-03-24
  • Semiparametric regression using empirical likelihood with shape information
    Statistics (IF 0.645) Pub Date : 2020-03-13
    Weibin Zhong; Kepher H. Makambi; Ao Yuan

    The empirical likelihood is a popular tool in statistics and many other fields, including regression analysis. It has the advantage of robustness against model specification and can incorporate side information to improve the estimation accuracy. There is vast literature on empirical likelihood incorporating various side information, mostly in the form of moment constraint(s). Here we study this method

    更新日期:2020-03-13
  • Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models.
    Statistics (IF 0.645) Pub Date : 2018-09-04
    E Bura,S Duarte,L Forzani,E Smucler,M Sued

    Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In contrast, few theoretical results are available for reduced-rank multivariate generalized linear models. We develop M-estimation theory for concave criterion functions that are maximized

    更新日期:2019-11-01
  • Exact confidence limits for the probability of response in two-stage designs.
    Statistics (IF 0.645) Pub Date : 2018-01-01
    Guogen Shan

    In addition to point estimate for the probability of response in a two-stage design (e.g., Simon's two-stage design for a Phase II clinical trial with binary endpoints), confidence limits should be Cute the confidence interval does not guarantee coverage probability in a two-stage setting. The existing exact approach to calculate one-sided limits is based on the overall number of responses to order

    更新日期:2019-11-01
  • Optimal designs for copula models.
    Statistics (IF 0.645) Pub Date : 2016-07-28
    E Perrone,W G Müller

    Copula modelling has in the past decade become a standard tool in many areas of applied statistics. However, a largely neglected aspect concerns the design of related experiments. Particularly the issue of whether the estimation of copula parameters can be enhanced by optimizing experimental conditions and how robust all the parameter estimates for the model are with respect to the type of copula employed

    更新日期:2019-11-01
  • Design-based random permutation models with auxiliary information¶
    Statistics (IF 0.645) Pub Date : 2012-01-01
    Wenjun Li,Edward J Stanek,Julio M Singer

    We extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification

    更新日期:2019-11-01
  • Probabilities for separating sets of order statistics.
    Statistics (IF 0.645) Pub Date : 2011-01-19
    D H Glueck,A Karimpour-Fard,J Mandel,K E Muller

    Consider a set of order statistics that arise from sorting samples from two different populations, each with their own, possibly different distribution functions. The probability that these order statistics fall in disjoint, ordered intervals and that of the smallest statistics, a certain number come from the first populations is given in terms of the two distribution functions. The result is applied

    更新日期:2019-11-01
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