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  • A block bootstrap for quasi-likelihood in sparse functional data
    Statistics (IF 0.645) Pub Date : 2020-10-16
    Guangbao Guo

    This article utilizes bootstrap quasi-likelihood (QL) to model sparse functional data. The proposed method combines parallel block bootstrap and QL to fit the functional data. The parameter space is considered as a finite-dimensional space through a certain optimization rule. Statistical errors of the proposed method are discussed. Some asymptotic properties of the method are established under several

    更新日期:2020-10-17
  • On strong consistency and asymptotic normality of one-step Gauss-Newton estimators in ARMA time series models
    Statistics (IF 0.645) Pub Date : 2020-10-12
    Pierre Duchesne; Pierre Lafaye de Micheaux; Joseph François Tagne Tatsinkou

    ABSTRACT One-step Gauss-Newton estimators for causal and invertible autoregressive moving-average (ARMA) time series models with an unknown mean are considered. A proof of the strong consistency and asymptotic normality of these estimators is provided. In a simulation study, their empirical properties are illustrated in ARMA models and compared to a other estimators: the estimators based on the innovations

    更新日期:2020-10-12
  • Adaptive elastic-net selection in a quantile model with diverging number of variable groups
    Statistics (IF 0.645) Pub Date : 2020-10-08
    Gabriela Ciuperca

    In real applications of the linear model, the explanatory variables are very often naturally grouped, the most common example being the multivariate variance analysis. In the present paper, a quantile model with group structure is considered, where the number of groups can diverge with the sample size. We introduce and study the adaptive elastic-net group quantile estimator, for improving the parameter

    更新日期:2020-10-08
  • Best prediction regions for future exponential record intervals
    Statistics (IF 0.645) Pub Date : 2020-10-02
    Elham Basiri; Arturo J. Fernández; Akbar Asgharzadeh; Seyed Fazel Bagheri

    A class of prediction regions for a future upper record interval ( R s , R l ) based on a type-II censored sample from the exponential distribution is presented in this paper. The best prediction region for ( R s , R l ) is then determined by solving a constrained nonlinear optimization problem. The objective function is the area of the prediction region and the constraints are related to the desired

    更新日期:2020-10-02
  • On jackknifing the symmetrized Tyler matrix
    Statistics (IF 0.645) Pub Date : 2020-09-29
    František Rublík

    The consistency of the jackknife estimator of the asymptotic covariance matrix of a differentiable function of the symmetrized Tyler matrix is proved for sampling from continuous distribution without any constraint on this sampled continuous distribution. It is shown that under mild regularity conditions the rank of the asymptotic covariance matrix attains maximum possible value. The results are applied

    更新日期:2020-09-29
  • The sparse variance contamination model
    Statistics (IF 0.645) Pub Date : 2020-09-22
    Ery Arias-Castro; Rong Huang

    We consider a Gaussian contamination (i.e., mixture) model where the contamination manifests itself as a change in variance. We study this model in various asymptotic regimes, in parallel with the work of Ingster YI. [Some problems of hypothesis testing leading to infinitely divisible distributions. Math Methods Stat. 1997;6(1):47–69] and Donoho D, Jin J. [Higher criticism for detecting sparse heterogeneous

    更新日期:2020-09-23
  • Optimal equivariant prediction regions based on multiply type-II censored generalized order statistics from exponential distributions
    Statistics (IF 0.645) Pub Date : 2020-09-02
    Grigoriy Volovskiy; Udo Kamps

    Interval prediction of future generalized order statistics from exponential distributions based on a multiply Type-II censored sample is considered. On invoking the principle of reduction by equivariance, prediction regions of minimal size are derived and shown to be of interval form. An alternative derivation based on the use of a prediction sufficient statistic is pointed out.

    更新日期:2020-09-03
  • Trend-transformed independent vectors: construction and stochastic comparison results
    Statistics (IF 0.645) Pub Date : 2020-08-14
    F. G. Badía; Sophie Mercier; C. Sangüesa

    A new class of random vectors is introduced, where the components are obtained through trend-transforms of independent random variables. Such a vector is called trend-transformed independent vector. The new class includes many models from the previous literature, such as sequential and intermediate order statistics, or the vectors formed by the first arrival times in classical counting processes such

    更新日期:2020-09-01
  • 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:2020-09-01
  • 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:2020-09-01
  • Ordered maximum ranked set sampling with unequal sample
    Statistics (IF 0.645) Pub Date : 2020-08-19
    Mehdi Basikhasteh; Fazlollah Lak; Saeid Tahmasebi

    In this paper, a new sampling method is introduced by ordering the maxima of ranked set samples with unequal sizes. In this new modification of ranked set sampling which is called OMRSSU, the pdf, cdf and joint pdf are obtained. Also, a new distribution-free prediction interval for future order statistics is derived. In addition, the moments of OMRSSU for exponential distribution are computed and then

    更新日期:2020-09-01
  • The limiting distribution of combining the t and Wilcoxon rank sum tests
    Statistics (IF 0.645) Pub Date : 2020-08-24
    Masato Kitani; Hidetoshi Murakami

    In two-sample testing problems, Student's t-test and the Wilcoxon rank-sum test are often used to test the location parameter, and have been discussed by many authors over the years. The Wilcoxon rank-sum test is more powerful than the t-test for non-normal data. Previous studies consider combining these two tests within a maximum test and show that it controls the type I error and has good statistical

    更新日期:2020-09-01
  • Recruitment and survival time under mean residual lifetime bias sampling
    Statistics (IF 0.645) Pub Date : 2020-08-12
    Polychronis Economou; Georgios Psarrakos; Abdolsaeed Toomaj

    There are several situations in which we can assume that an individual with larger mean residual lifetime is more likely to participate in a sample than another with smaller mean residual lifetime; for example, employment agencies are more likely to hire individuals that are not close to their retirement. In this paper, a new bivariate model is proposed to describe such situations by modelling the

    更新日期:2020-09-01
  • A self-consistent estimator for interval-valued data
    Statistics (IF 0.645) Pub Date : 2020-08-28
    Hyejeong Choi; Johan Lim; Xinlei Wang; Minjung Kwak

    In interval-valued data, the variable of interest is provided in the form of an interval with lower and upper bounds, not a single value. An univariate representation for the interval is not unique by its nature, in particular when interval-valued data are of the min-max (MM) type. Researchers focus on the marginal histogram distribution which is well suited to the measurement error (ME) type interval

    更新日期:2020-08-28
  • Uniform convergence rate of the nonparametric maximum likelihood estimator for current status data with competing risks
    Statistics (IF 0.645) Pub Date : 2020-08-28
    Sergey V. Malov

    We study the uniform convergence rate of the nonparametric maximum likelihood estimator (MLE) for the sub-distribution functions of failure times in the current status data with competing risks model. It is known that the MLE have L 2 -norm convergence rate O P ( n − 1 / 3 ) in the absolutely continuous case, but no agreement has emerged on a uniform convergence rate. We specify conditions under which

    更新日期:2020-08-28
  • Testing in the growth curve model with intraclass correlation structure
    Statistics (IF 0.645) Pub Date : 2020-08-25
    Veronika Jurková; Ivan Žežula; Daniel Klein

    This paper describes two tests in the growth curve model under multivariate normality. The first one is revisited test for intraclass structure of the covariance matrix, and the second one is the simultaneous test for intraclass covariance structure together with a specific mean value of the model. Both tests are likelihood ratio tests.

    更新日期:2020-08-25
  • 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 ( X i , Y i ) i ∈ Z be a stationary sequence of R 2 -valued random variables. To test if X 1 and Y 1 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

    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 < t < V ) , we proposed a weighted quantile regression estimator

    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
  • Constructing optimal projection designs
    Statistics (IF 0.645) Pub Date : 2019-11-12
    A. M. Elsawah, Yu Tang, Kai-Tai Fang

    The early stages of many real-life experiments involve a large number of factors among which only a few factors are active. Unfortunately, the optimal full-dimensional designs of those early stages may have bad low-dimensional projections and the experimenters do not know which factors turn out to be important before conducting the experiment. Therefore, designs with good projections are desirable

    更新日期:2019-11-12
  • Exact inference on multiple exponential populations under a joint type-II progressive censoring scheme
    Statistics (IF 0.645) Pub Date : 2019-11-05
    Shuvashree Mondal, Debasis Kundu

    Recently Mondal and Kundu [Mondal S, Kundu D. A new two sample type-II progressive censoring scheme. Commun Stat Theory Methods. 2018. doi:10.1080/03610926.2018.1472781] introduced a Type-II progressive censoring scheme for two populations. In this article, we extend the above scheme for more than two populations. The aim of this paper is to study the statistical inference under the multi-sample Type-II

    更新日期:2019-11-05
  • On an asymptotic relative efficiency concept based on expected volumes of confidence regions
    Statistics (IF 0.645) Pub Date : 2019-11-05
    Ludwig Baringhaus, Daniel Gaigall

    The paper deals with an asymptotic relative efficiency concept for confidence regions of multidimensional parameters that is based on the expected volumes of the confidence regions. Under standard conditions the asymptotic relative efficiencies of confidence regions are seen to be certain powers of the ratio of the limits of the expected volumes. These limits are explicitly derived for confidence regions

    更新日期:2019-11-05
  • Testing the homogeneity of risk differences with sparse count data
    Statistics (IF 0.645) Pub Date : 2019-10-10
    Junyong Park, Iris Ivy Gauran

    In this paper, we consider testing the homogeneity of risk differences in independent binomial distributions especially when data are sparse. We point out some drawback of existing tests in either controlling a nominal size or obtaining powers through theoretical and numerical studies. The proposed test is designed to avoid the drawbacks of existing tests. We present the asymptotic null distribution

    更新日期:2019-10-10
  • A pivot function and its limiting distribution: applications in goodness of fit and testing hypothesis
    Statistics (IF 0.645) Pub Date : 2019-10-01
    A. R. Soltani

    In this paper, a pivot function which is in terms of the sample and the underlying population distribution is introduced. It is assumed that the population distribution is continuous and strictly increasing on its support. Then, the martingale central limit theorem is applied to prove that limiting distribution of the pivot function is the standard normal. Interestingly, this result provides a unified

    更新日期:2019-10-01
  • Principal varying coefficient estimator for high-dimensional models
    Statistics (IF 0.645) Pub Date : 2019-09-16
    Weihua Zhao, Fode Zhang, Xuejun Wang, Rui Li, Heng Lian

    We consider principal varying coefficient models in the high-dimensional setting, combined with variable selection, to reduce the effective number of parameters in semiparametric modelling. The estimation is based on B-splines approach. For the unpenalized estimator, we establish non-asymptotic bounds of the estimator and then establish the (asymptotic) local oracle property of the penalized estimator

    更新日期:2019-09-16
  • Simultaneous confidence bands for growth incidence curves in weighted sup-norm metrics
    Statistics (IF 0.645) Pub Date : 2019-09-04
    Biyi Shen, Fabian Dunker, Chixiang Chen

    The so-called growth incidence curve (GIC) is a popular way to evaluate the distributional pattern of economic growth and pro-poorness of growth in development economics. The log-transformation of the the GIC is related to the sum of empirical quantile processes which allows for constructions of simultaneous confidence bands for the GIC. However, standard constructions of these bands tend to be too

    更新日期:2019-09-04
  • Large-scale multiple hypothesis testing with the normal-beta prime prior
    Statistics (IF 0.645) Pub Date : 2019-09-02
    Ray Bai, Malay Ghosh

    We revisit the problem of simultaneously testing the means of n independent normal observations under sparsity. We take a Bayesian approach to this problem by studying a scale-mixture prior known as the normal-beta prime (NBP) prior. To detect signals, we propose a hypothesis test based on thresholding the posterior shrinkage weight under the NBP prior. Taking the loss function to be the expected number

    更新日期:2019-09-02
  • Model fusion and multiple testing in the likelihood paradigm: shrinkage and evidence supporting a point null hypothesis
    Statistics (IF 0.645) Pub Date : 2019-08-30
    David R. Bickel, Abbas Rahal

    According to the general law of likelihood, the strength of statistical evidence for a hypothesis as opposed to its alternative is the ratio of their likelihoods, each maximized over the parameter of interest. Consider the problem of assessing the weight of evidence for each of several hypotheses. Under a realistic model with a free parameter for each alternative hypothesis, this leads to weighing

    更新日期:2019-08-30
  • Strong consistency of kernel estimator in a semiparametric regression model
    Statistics (IF 0.645) Pub Date : 2019-08-26
    Emmanuel de Dieu Nkou, Guy Martial Nkiet

    Estimating the effective dimension reduction (EDR) space, related to the semiparametric regression model introduced by Li [Sliced inverse regression for dimension reduction. J Amer Statist Assoc. 1991;86:316–327], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λˆn of Λ based on kernel method was introduced

    更新日期:2019-08-26
  • Plug-in L2-upper error bounds in deconvolution, for a mixing density estimate in Rd and for its derivatives, via the L1-error for the mixture
    Statistics (IF 0.645) Pub Date : 2019-07-30
    Yannis G. Yatracos

    In deconvolution in Rd, d≥1, with mixing density p(∈P) and kernel h, the mixture density fp(∈Fp) is estimated with MDE fpˆn, having upper L1-error rate, an, in probability or in risk; pˆn∈P. In one application, P consists of L1-separable densities in R with differences changing sign at most J times and h(x−y) Totally Positive. When h is known and p is q~-smooth, vanishing outside a compact in Rd, plug-in

    更新日期:2019-07-30
  • Using adaptively weighted large margin classifiers for robust sufficient dimension reduction
    Statistics (IF 0.645) Pub Date : 2019-07-04
    Andreas Artemiou

    In this paper, we combine adaptively weighted large margin classifiers with Support Vector Machine (SVM)-based dimension reduction methods to create dimension reduction methods robust to the presence of extreme outliers. We discuss estimation and asymptotic properties of the algorithm. The good performance of the new algorithm is demonstrated through simulations and real data analysis.

    更新日期:2019-07-04
  • Semiparametric estimation of plane similarities: application to fast computation of aeronautic loads
    Statistics (IF 0.645) Pub Date : 2019-06-25
    Edouard Fournier, Stéphane Grihon, Thierry Klein

    In the big data era, it is often needed to resolve the problem of parsimonious data representation. In this paper, the data under study are curves and the sparse representation is based on a semiparametric model. Indeed, we propose an original registration model for noisy curves. The model is built transforming an unknown function by plane similarities. We develop a statistical method that allows to

    更新日期:2019-06-25
  • The Cox-Aalen model for left-truncated and mixed interval-censored data
    Statistics (IF 0.645) Pub Date : 2019-06-24
    Pao-sheng Shen, Li Ning Weng

    Scheike and Zhang [An additive-multiplicative Cox-Aalen regression model. Scand J Stat. 2002;29:75–88] proposed a flexible additive-multiplicative hazard model, called the Cox-Aalen model, by replacing the baseline hazard function in the well-known Cox model with a covariate-dependent Aalen model, which allows for both fixed and dynamic covariate effects. In this paper, based on left-truncated and

    更新日期:2019-06-24
  • On second-order improved estimation of a gamma scale parameter
    Statistics (IF 0.645) Pub Date : 2019-06-13
    Hidekazu Tanaka, Nabendu Pal, Wooi K. Lim

    This paper concludes our comprehensive study on point estimation of model parameters of a gamma distribution from a second-order decision theoretic point of view. It should be noted that efficient estimation of gamma model parameters for samples ‘not large’ is a challenging task since the exact sampling distributions of the maximum likelihood estimators and its variants are not known. Estimation of

    更新日期:2019-06-13
  • Constructing quantile confidence intervals using extended simple random sample in finite populations
    Statistics (IF 0.645) Pub Date : 2019-06-10
    Omer Ozturk, Narayanaswamy Balakrishnan

    This paper constructs quantile confidence intervals based on extended simple random sample (SRS) from a finite population, where ranks of population units are all known. Extended simple random sample borrows additional information from unmeasured observations in the population by conditioning on the population ranks of the measured units in SRS. The confidence intervals are improved using Rao-Blackwell

    更新日期:2019-06-10
  • Berry-Esseen type bounds of the estimators in a semiparametric model under linear process errors with α-mixing dependent innovations
    Statistics (IF 0.645) Pub Date : 2019-06-10
    Yi Wu, Xuejun Wang, Yongming Li, Shuhe Hu

    In this paper, we mainly study the Berry-Esseen type bounds of estimators in a semiparametric model yi=xiβ+g(ti)+Vi, 1≤i≤n, where β is an unknown parameter, (xi,ti) are nonrandom design points, yi are the response variables, g(⋅) is an unknown function designed on the closed interval [0,1], and the errors Vi are formed by Vi=∑j=−∞∞ψjei−j with ∑j=−∞∞|ψj|<∞ and ei are α-mixing random variables. Under

    更新日期:2019-06-10
  • On LSE in regression model for long-range dependent random fields on spheres
    Statistics (IF 0.645) Pub Date : 2019-06-10
    Vo Anh, Andriy Olenko, Volodymyr Vaskovych

    We study the asymptotic behaviour of least squares estimators (LSE) in regression models for long-range dependent random fields observed on spheres. The LSE can be given as a weighted functional of long-range dependent random fields. It is known that in this scenario the limits can be non-Gaussian. We derive the limit distribution and the corresponding rate of convergence for the estimators. The results

    更新日期:2019-06-10
  • Bayesian analysis of semiparametric Bernstein polynomial regression models for data with sample selection
    Statistics (IF 0.645) Pub Date : 2019-06-05
    Hea-Jung Kim, Taeyoung Roh, Taeryon Choi

    In regression analysis, a sample selection scheme often applies to the response variable, which results in missing not at random observations on the variable. In this case, a regression analysis using only the selected cases would lead to biased results. This paper proposes a Bayesian methodology to correct this bias based on a semiparametric Bernstein polynomial regression model that incorporates

    更新日期:2019-06-05
  • Unified multivariate hypergeometric interpoint distances
    Statistics (IF 0.645) Pub Date : 2019-05-27
    Yu Song, Reza Modarres

    We establish (a) the probability mass function (p.m.f.) of the interpoint distance (IPD) between random vectors drawn from the unified multivariate hypergeometric (UMHG) family of distributions; (b) obtain the distribution of the IPD within one sample and across two samples from this family; (c) determine the distribution of the UMHG Euclidean norm and distance from fixed point in Zd; and (d) provide

    更新日期:2019-05-27
  • A new diagnostic tool for VARMA(p,q) models
    Statistics (IF 0.645) Pub Date : 2019-05-23
    Santiago Velilla, Huong Nguyen

    A new diagnostic method for VARMA(p,q) time series models is introduced. The procedure is based on a statistic that generalizes to a multivariate setting the properties of the usual univariate ARMA(p,q) residual correlations. A multiple version of the cumulative periodogram statistic is also suggested. Simulation studies and one real data application are presented.

    更新日期:2019-05-23
  • Local asymptotic normality for Student-Lévy processes under high-frequency sampling
    Statistics (IF 0.645) Pub Date : 2019-05-21
    Till Massing

    There is considerable interest in parameter estimation in Lévy models. The maximum likelihood estimator is widely used because under certain conditions it enjoys asymptotic efficiency properties. The toolkit for Lévy processes is the local asymptotic normality which guarantees these conditions. Although the likelihood function is not known explicitly, we prove local asymptotic normality for the location

    更新日期:2019-05-21
  • Estimation in zero-inflated binomial regression with missing covariates
    Statistics (IF 0.645) Pub Date : 2019-05-21
    Alpha Oumar Diallo, Aliou Diop, Jean-François Dupuy

    We investigate inverse-probability-weighted (IPW) maximum likelihood estimation in zero-inflated binomial regression with missing-at-random covariates. Large sample properties (consistency, asymptotic normality) of the IPW estimator are established. Finite sample properties are assessed via simulations. The methodology is illustrated on a real data set.

    更新日期:2019-05-21
  • Modelling failures times with dependent renewal type models via exchangeability
    Statistics (IF 0.645) Pub Date : 2019-05-21
    Arrigo Coen, Luis Gutiérrez, Ramsés H. Mena

    Failure times of a machinery cannot always be assumed independent and identically distributed, eg, if after reparations the machinery is not restored to a same-as-new condition. Framed within the renewal processes approach, a generalization that considers exchangeable inter-arrival times is presented. The resulting model provides a more realistic approach to capture the dependence among events occurring

    更新日期:2019-05-21
  • Efficient nonparametric estimation and inference for the volatility function
    Statistics (IF 0.645) Pub Date : 2019-05-20
    Francesco Giordano, Maria Lucia Parrella

    In this paper we focus on nonparametric analysis of the volatility function for mixing processes. Our approach is based on local polynomial smoothing and supplies several tools which can be used to test a specific parametric model: nonparametric function estimation, nonparametric confidence intervals, and nonparametric test for symmetry. At the same time, it faces the main drawbacks of the nonparametric

    更新日期:2019-05-20
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