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  • 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

    更新日期:2020-08-08
  • 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

    更新日期:2020-08-05
  • 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

    更新日期:2020-07-24
  • 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

    更新日期:2020-07-13
  • 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

    更新日期:2020-07-10
  • 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

    更新日期:2020-07-09
  • 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

    更新日期:2020-07-03
  • 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

    更新日期:2020-06-30
  • 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

    更新日期:2020-06-29
  • 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

    更新日期:2020-06-28
  • 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

    更新日期:2020-06-23
  • 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

    更新日期:2020-06-12
  • 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]

    更新日期:2020-06-10
  • 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

    更新日期:2020-06-10
  • 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

    更新日期:2020-06-10
  • 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

    更新日期:2020-06-05
  • 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

    更新日期:2020-06-05
  • 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

    更新日期:2020-06-03
  • 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

    更新日期:2020-06-03
  • 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

    更新日期:2020-04-28
  • 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.

    更新日期:2020-04-24
  • 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 \(L^2\)

    更新日期:2020-04-23
  • 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)

    更新日期:2020-04-23
  • Nonparametric kernel estimation of CVaR under $$\alpha $$α -mixing sequences
    Stat. Pap. (IF 1.433) Pub Date : 2017-10-03
    Zhongde Luo

    Conditional Value-at-Risk (CVaR) is an increasingly popular coherent risk measure in financial risk management. In this paper, a new nonparametric kernel estimator of CVaR is established, and a Bahadur type expansion of the estimator is also given under \(\alpha \)-mixing sequences. Furthermore, the mean, variance, mean square error (MSE) and uniformly asymptotic normality of the new estimator are

    更新日期:2020-04-23
  • Mean targeting estimator for the integer-valued GARCH(1, 1) model
    Stat. Pap. (IF 1.433) Pub Date : 2017-10-10
    Qi Li, Fukang Zhu

    The integer-valued GARCH model is commonly used in modeling time series of counts. Maximum likelihood estimation (MLE) is used to estimate unknown parameters, but numerical results for MLE are sensitive to the choice of initial values, which also occurs in estimating the GARCH model. To alleviate this numerical difficulty, we propose an alternative to MLE and name it as mean targeting estimation (MTE)

    更新日期:2020-04-23
  • Empirical likelihood-based weighted rank regression with missing covariates
    Stat. Pap. (IF 1.433) Pub Date : 2017-10-24
    Tianqing Liu, Xiaohui Yuan

    This paper proposes an empirical likelihood-based weighted (ELW) rank regression approach for estimating linear regression models when some covariates are missing at random. The proposed ELW estimator of regression parameters is computationally simple and achieves better efficiency than the inverse probability weighted (IPW) estimator if the probability of missingness is correctly specified. The covariances

    更新日期:2020-04-23
  • Mixtures of multivariate contaminated normal regression models
    Stat. Pap. (IF 1.433) Pub Date : 2017-11-13
    Angelo Mazza, Antonio Punzo

    Mixtures of regression models (MRMs) are widely used to investigate the relationship between variables coming from several unknown latent homogeneous groups. Usually, the conditional distribution of the response in each mixture component is assumed to be (multivariate) normal (MN-MRM). To robustify the approach with respect to possible elliptical heavy-tailed departures from normality, due to the presence

    更新日期:2020-04-23
  • On estimation of $$P\left( X > Y \right) $$PX>Y based on judgement post stratification
    Stat. Pap. (IF 1.433) Pub Date : 2017-11-01
    Ali Dastbaravarde, Ehsan Zamanzade

    We propose an unbiased estimator for \(P\left( X>Y\right) \) and obtain an exact expression for its variance, based on judgement post stratification (JPS) sampling scheme. We then prove that the introduced estimator is consistent and establish its asymptotic normality. We show that the proposed estimator is at least as efficient asymptotically as its counterpart in simple random sampling (SRS), regardless

    更新日期:2020-04-23
  • A matching prior based on the modified profile likelihood for the common mean in multiple log-normal distributions
    Stat. Pap. (IF 1.433) Pub Date : 2017-09-20
    Yongku Kim, Woo Dong Lee, Sang Gil Kang

    In this paper, we develop a matching prior for the common mean in several log-normal distributions. For this problem, assigning priors appropriately for the common log-normal mean is challenging owing to the presence of nuisance parameters. Matching priors, which are priors that match the posterior probabilities of certain regions within their frequentist coverage probabilities, are commonly used in

    更新日期:2020-04-23
  • Bahadur intercept with applications to one-sided testing
    Stat. Pap. (IF 1.433) Pub Date : 2017-10-09
    Zeng-Hua Lu

    In this paper we propose Bahadur intercept for measuring the Bahadur deficiency of two test procedures that have the same Bahadur slope. We illustrate our results in an example of testing a one-sided hypothesis in which the comparison of Bahadur intercepts of one-sided and two-sided tests reveals an important explanation for the test deficiency. We also show that the Bahadur deficiency coincides with

    更新日期:2020-04-23
  • Component-wise outlier detection methods for robustifying multivariate functional samples
    Stat. Pap. (IF 1.433) Pub Date : 2017-09-21
    Francesca Ieva, Anna Maria Paganoni

    We propose a new method for detecting outliers in multivariate functional data. We exploit the joint use of two different depth measures, and generalize the outliergram to the multivariate functional framework, aiming at detecting and discarding both shape and magnitude outliers. The main application consists in robustifying the reference samples of data, composed by G different known groups to be

    更新日期:2020-04-23
  • Construction of Latin hypercube designs with nested and sliced structures
    Stat. Pap. (IF 1.433) Pub Date : 2017-10-24
    Bing Guo, Xue-Ping Chen, Min-Qian Liu

    Recently, the construction of nested or sliced Latin hypercube designs (LHDs) has received notable interest for planning computer experiments with special combinational structures. In this paper, we propose an approach to constructing nested and/or sliced LHDs by using small LHDs and structural vectors/matrices. This method is easy to implement, and can generate nested and sliced LHDs through a unified

    更新日期:2020-04-23
  • Correlated endpoints: simulation, modeling, and extreme correlations
    Stat. Pap. (IF 1.433) Pub Date : 2017-10-25
    Sergei Leonov, Bahjat Qaqish

    Modeling and simulation of correlated random variables are important for evaluating operating characteristics of experimental designs in various applications, of which clinical trials with multiple endpoints provide an important example. There exist efficient algorithms to address the problem of generating multivariate distributions with given marginals and correlation structure. For model fitting

    更新日期:2020-04-23
  • On the consistency of the P–C estimator in a nonparametric regression model
    Stat. Pap. (IF 1.433) Pub Date : 2017-11-24
    Yi Wu, Xuejun Wang, Narayanaswamy Balakrishnan

    In this paper, we investigate the nonparametric regression model based on extended negatively dependent errors. Some consistency results for the estimator of the regression function g(x) are presented, including the rates of strong consistency and complete consistency, and the mean convergence. The results obtained in this paper improve and extend the corresponding ones of Yang and Wang (Acta Math

    更新日期:2020-04-23
  • Estimation of a symmetric distribution function in multistage ranked set sampling
    Stat. Pap. (IF 1.433) Pub Date : 2017-11-20
    M. Mahdizadeh, Ehsan Zamanzade

    This article concerns estimation of a symmetric distribution function under multistage ranked set sampling. A nonparametric estimator is developed and its theoretical properties are explored. Performance of the suggested estimator is further evaluated using numerical studies.

    更新日期:2020-04-23
  • Partial sufficient dimension reduction on additive rates model for recurrent event data with high-dimensional covariates
    Stat. Pap. (IF 1.433) Pub Date : 2017-09-19
    Xiaobing Zhao, Xian Zhou

    Recurrent event data with an additive marginal rates function have been extensively studied in the literature. The existing statistical inference, however, faces the difficulty with high-dimensional covariates due to “curse of dimensionality”. Examples include gene expression and single nucleotide polymorphism data which have revolutionized our understanding of disease such as cancer recurrence. In

    更新日期:2020-04-23
  • Inference on q-Weibull parameters
    Stat. Pap. (IF 1.433) Pub Date : 2017-09-21
    Xiang Jia, Saralees Nadarajah, Bo Guo

    The q-Weibull distribution is a generalization of the Weibull distribution and could describe complex systems. We firstly point out how to derive the maximum likelihood estimates (MLEs) and least-squares estimates (LSEs) of the q-Weibull parameters. Next, three confidence intervals (CIs) for the q-Weibull parameters are constructed based on bootstrap methods and asymptotic normality of the MLEs. Explicit

    更新日期:2020-04-23
  • The distribution of unobserved heterogeneity in competing risks models
    Stat. Pap. (IF 1.433) Pub Date : 2017-10-17
    Yang Lu

    We show that in a large class of proportional hazard competing risks models, the distribution of the bivariate frailty among survivors converges to a limiting distribution. This generalizes the result of Abbring and van den Berg (Biometrika 94(1):87–99, 2007), who show that in a single spell duration model, the frailty distribution converges to the gamma distribution. The resulting limiting distribution

    更新日期:2020-04-23
  • 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

    更新日期:2020-03-21
  • 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

    更新日期:2020-03-20
  • 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

    更新日期:2020-01-29
  • 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

    更新日期:2020-01-27
  • A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose.
    Stat. Pap. (IF 1.433) Pub Date : 2019-04-02
    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

    更新日期:2019-11-01
  • Incoherent dose-escalation in phase I trials using the escalation with overdose control approach.
    Stat. Pap. (IF 1.433) Pub Date : 2018-06-08
    Graham M Wheeler

    A desirable property of any dose-escalation strategy for phase I oncology trials is coherence: if the previous patient experienced a toxicity, a higher dose is not recommended for the next patient; similarly, if the previous patient did not experience a toxicity, a lower dose is not recommended for the next patient. The escalation with overdose control (EWOC) approach is a model-based design that has

    更新日期:2019-11-01
  • Inference on the marginal distribution of clustered data with informative cluster size.
    Stat. Pap. (IF 1.433) Pub Date : 2014-02-01
    Jaakko Nevalainen,Somnath Datta,Hannu Oja

    In spite of recent contributions to the literature, informative cluster size settings are not well known and understood. In this paper, we give a formal definition of the problem and describe it from different viewpoints. Data generating mechanisms, parametric and nonparametric models are considered in light of examples. Our emphasis is on nonparametric and robust approaches to the inference on the

    更新日期:2019-11-01
  • On the performance of weighted bootstrapped kernel deconvolution density estimators
    Stat. Pap. (IF 1.433) Pub Date : 2018-05-02
    Ali Al-Sharadqah, Majid Mojirsheibani, William Pouliot

    We propose a weighted bootstrap approach that can improve on current methods to approximate the finite sample distribution of normalized maximal deviations of kernel deconvolution density estimators in the case of ordinary smooth errors. Using results from the approximation theory for weighted bootstrap empirical processes, we establish an unconditional weak limit theorem for the corresponding weighted

    更新日期:2018-05-02
  • New lower bound for Lee discrepancy of asymmetrical factorials
    Stat. Pap. (IF 1.433) Pub Date : 2018-04-25
    Liuping Hu, Kashinath Chatterjee, Jiaqi Liu, Zujun Ou

    Lee discrepancy has wide applications in design of experiments, which can be used to measure the uniformity of fractional factorials. An improved lower bound of Lee discrepancy for asymmetrical factorials with mixed two-, three- and four-level is presented. The new lower bound is more accurate for a lot of designs than other existing lower bound, which is a useful complement to the lower bounds of

    更新日期:2018-04-25
  • Random coefficient minification processes
    Stat. Pap. (IF 1.433) Pub Date : 2018-04-23
    Lengyi Han, W. John Braun, Jason Loeppky

    A common way to model nonnegative time series is to apply a log transformation and then use classical ARMA techniques. We demonstrate using Canadian Fire Weather Index (FWI) data that simulating from such models can lead to unrealistic data scenarios. Minification models provide another approach to nonnegative time series, but they can be too restrictive. We propose a random coefficient version of

    更新日期:2018-04-23
  • Second-order matching prior family parametrized by sample size and matching probability
    Stat. Pap. (IF 1.433) Pub Date : 2018-04-18
    Toyoto Tanaka, Yoshihiro Hirose, Fumiyasu Komaki

    We propose a family of priors that satisfies the second-order probability matching property. The posterior quantile of a probability matching prior is exactly or approximately equal to the frequentist one. Most models lack an exact matching prior. If all quantiles of a prior’s posterior converge to the frequentist ones up to \(o(n^{-1/2})\) or \(o(n^{-1})\) as the sample size n increases, the prior

    更新日期:2018-04-18
  • Cramér’s type results for some bootstrapped U -statistics
    Stat. Pap. (IF 1.433) Pub Date : 2018-04-09
    Sergio Alvarez-Andrade, Salim Bouzebda

    In the present paper, we are mainly interested in Cramér-type results for the weighted bootstrap of the U-statistics. The method of proof is based on the Hoeffding decomposition according to the bootstrapped Cramér transform together with the contraction technique. Finally, we investigate the U-statistics indexed by a one dimensional symmetric random walk.

    更新日期:2018-04-09
  • Some convergence properties for partial sums of widely orthant dependent random variables and their statistical applications
    Stat. Pap. (IF 1.433) Pub Date : 2018-03-29
    Mengmei Xi, Rui Wang, Zhaoyang Cheng, Xuejun Wang

    In this paper, we present the \(L_p\) convergence for partial sums \(S_n=\sum _{k=1}^nX_k\) under the Cesàro uniform integrability condition and the complete convergence for the maximum of \(S_n\) for sequences of widely orthant dependent random variables \(\{X_n,n\ge 1\}.\) Some of the results extend the corresponding ones in reference. As applications, we get the complete consistency and the strong

    更新日期:2018-03-29
  • Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images
    Stat. Pap. (IF 1.433) Pub Date : 2018-03-27
    Massimo Aria, Antonio D’Ambrosio, Carmela Iorio, Roberta Siciliano, Valentina Cozza

    In this paper, multivalued data or multiple values variables are defined. They are typical when there is some intrinsic uncertainty in data production, as the result of imprecise measuring instruments, such as in image recognition, in human judgments and so on. So far, contributions in symbolic data analysis literature provide data preprocessing criteria allowing for the use of standard methods such

    更新日期:2018-03-27
  • Optimal principal points estimators of multivariate distributions of location-scale and location-scale-rotation families
    Stat. Pap. (IF 1.433) Pub Date : 2018-03-20
    Shun Matsuura, Thaddeus Tarpey

    A set of k points that optimally summarize a distribution is called a set of k-principal points, which is a generalization of the mean from one point to multiple points and is useful especially for multivariate distributions. This paper discusses the estimation of principal points of multivariate distributions. First, an optimal estimator of principal points is derived for multivariate distributions

    更新日期:2018-03-20
  • An exponential inequality and its application to M estimators in multiple linear models
    Stat. Pap. (IF 1.433) Pub Date : 2018-03-17
    Xin Deng, Xuejun Wang

    In the paper, an exponential inequality for widely orthant dependent random variables is established without bounded condition. By using the inequality, we further investigate the strong linear representation for the M estimator of the regression parameter vector in linear regression models with widely orthant dependent random errors under some general conditions. In addition, we have conducted comprehensive

    更新日期:2018-03-17
  • Conditional SIRS for nonparametric and semiparametric models by marginal empirical likelihood
    Stat. Pap. (IF 1.433) Pub Date : 2018-03-15
    Yi Chu, Lu Lin

    Dimension reduction is a crucial issue for high-dimensional data analysis. When the correlation among the variables is strong, the original SIRS (Zhu et al. in J Am Stat Assoc 106(496):1464–1475, 2011) may lose efficiency. Under high-dimensional setting, eliminating the bad influence caused by the correlation has become an important issue. Aiming at this issue, we propose a feature screening approach

    更新日期:2018-03-15
  • Construction of simultaneous confidence bands for a percentile hyper-plane with predictor variables constrained in an ellipsoidal region
    Stat. Pap. (IF 1.433) Pub Date : 2018-03-15
    Sanyu Zhou, Defa Wang, Jingjing Zhu

    This paper provides the method of constructing the simultaneous confidence band for a percentile hyper-plane with predictor variables constrained in an ellipsoidal region. The band is compared with that on a rectangular region. It is shown that the band on the ellipsoidal region has a big advantage. The proposed method allows to construct the Type I band and the Type II band, and also allows to constrain

    更新日期:2018-03-15
  • Markov switching asymmetric GARCH model: stability and forecasting
    Stat. Pap. (IF 1.433) Pub Date : 2018-03-10
    N. Alemohammad, S. Rezakhah, S. H. Alizadeh

    A new Markov switching asymmetric GARCH model is proposed where each state follows the smooth transition GARCH model, represented by Lubrano (Recherches Economiques de Louvain 67:257–287, 2001), that follows a logistic smooth transition structure between effects of positive and negative shocks. This consideration provides better forecasts than GARCH, Markov switching GARCH and smooth transition GARCH

    更新日期:2018-03-10
  • Stochastic restricted estimation in partially linear additive errors-in-variables models
    Stat. Pap. (IF 1.433) Pub Date : 2018-02-24
    Chuanhua Wei, Jin Yang

    As a generalization of additive model and partially linear model, partially linear additive model has been paid considerably attention in recent years. This paper considers estimation of the parametric component of the semiparametric model when the covariates in the linear part are measured with additive error and some additional stochastic linear restrictions on the parametric component are available

    更新日期:2018-02-24
  • Quantile regression for nonlinear mixed effects models: a likelihood based perspective
    Stat. Pap. (IF 1.433) Pub Date : 2018-02-24
    Christian E. Galarza, Luis M. Castro, Francisco Louzada, Victor H. Lachos

    Longitudinal data are frequently analyzed using normal mixed effects models. Moreover, the traditional estimation methods are based on mean regression, which leads to non-robust parameter estimation under non-normal error distribution. However, at least in principle, quantile regression (QR) is more robust in the presence of outliers/influential observations and misspecification of the error distributions

    更新日期:2018-02-24
  • Variable selection in high-dimensional sparse multiresponse linear regression models
    Stat. Pap. (IF 1.433) Pub Date : 2018-02-23
    Shan Luo

    We consider variable selection in high-dimensional sparse multiresponse linear regression models, in which a q-dimensional response vector has a linear relationship with a p-dimensional covariate vector through a sparse coefficient matrix \(B\in R^{p\times q}\). We propose a consistent procedure for the purpose of identifying the nonzeros in B. The procedure consists of two major steps, where the first

    更新日期:2018-02-23
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