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Estimation for partially linear single-index spatial autoregressive model with covariate measurement errors Stat. Pap. (IF 1.3) Pub Date : 2024-04-23 Ke Wang, Dehui Wang
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A trigamma-free approach for computing information matrices related to trigamma function Stat. Pap. (IF 1.3) Pub Date : 2024-04-20 Zhou Yu, Niloufar Dousti Mousavi, Jie Yang
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On some stable linear functional regression estimators based on random projections Stat. Pap. (IF 1.3) Pub Date : 2024-04-17 Asma Ben Saber, Abderrazek Karoui
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Testing practical relevance of treatment effects Stat. Pap. (IF 1.3) Pub Date : 2024-04-17 Andrea Ongaro, Sonia Migliorati, Roberto Ascari, Enrico Ripamonti
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Supervised dimension reduction for functional time series Stat. Pap. (IF 1.3) Pub Date : 2024-04-16 Guochang Wang, Zengyao Wen, Shanming Jia, Shanshan Liang
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Statistical inferences for missing response problems based on modified empirical likelihood Stat. Pap. (IF 1.3) Pub Date : 2024-04-16 Sima Sharghi, Kevin Stoll, Wei Ning
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A high-dimensional single-index regression for interactions between treatment and covariates Stat. Pap. (IF 1.3) Pub Date : 2024-04-13 Hyung Park, Thaddeus Tarpey, Eva Petkova, R. Todd Ogden
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Flexible-dimensional L-statistic for mean estimation of symmetric distributions Stat. Pap. (IF 1.3) Pub Date : 2024-04-06 Juan Baz, Diego García-Zamora, Irene Díaz, Susana Montes, Luis Martínez
Estimating the mean of a population is a recurrent topic in statistics because of its multiple applications. If previous data is available, or the distribution of the deviation between the measurements and the mean is known, it is possible to perform such estimation by using L-statistics, whose optimal linear coefficients, typically referred to as weights, are derived from a minimization of the mean
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Matrix-variate generalized linear model with measurement error Stat. Pap. (IF 1.3) Pub Date : 2024-04-06 Tianqi Sun, Weiyu Li, Lu Lin
Matrix-variate generalized linear model (mvGLM) has been investigated successfully under the framework of tensor generalized linear model, because matrix-form data can be regarded as a specific tensor (2-dimension). But there are few works focusing on matrix-form data with measurement error (ME), since tensor in conjunction with ME is relatively complex in structure. In this paper we introduce a mvGLM
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Some practical and theoretical issues related to the quantile estimators Stat. Pap. (IF 1.3) Pub Date : 2024-04-05 Dagmara Dudek, Anna Kuczmaszewska
The paper contains the comparative analysis of the efficiency of different qunatile estimators for various distributions. Additionally, we show strong consistency of different quantile estimators and we study the Bahadur representation for each of the quantile estimators, when the sample is taken from NA, \(\varphi \), \(\rho ^*\), \(\rho \)-mixing population.
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A sequential feature selection approach to change point detection in mean-shift change point models Stat. Pap. (IF 1.3) Pub Date : 2024-04-03
Abstract Change point detection is an important area of scientific research and has applications in a wide range of fields. In this paper, we propose a sequential change point detection (SCPD) procedure for mean-shift change point models. Unlike classical feature selection based approaches, the SCPD method detects change points in the order of the conditional change sizes and makes full use of the
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The exponentiated exponentially weighted moving average control chart Stat. Pap. (IF 1.3) Pub Date : 2024-04-03 Vasileios Alevizakos, Arpita Chatterjee, Kashinath Chatterjee, Christos Koukouvinos
Memory-type control charts are widely used for monitoring small to moderate shifts in the process parameter(s). In the present article, we present an exponentiated exponentially weighted moving average (Exp-EWMA) control chart that weights the past observations of a process using an exponentiated function. We evaluated the run-length characteristics of the Exp-EWMA chart via Monte Carlo simulations
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Hypothesis testing for varying coefficient models in tail index regression Stat. Pap. (IF 1.3) Pub Date : 2024-04-02 Koki Momoki, Takuma Yoshida
This study examines the varying coefficient model in tail index regression. The varying coefficient model is an efficient semiparametric model that avoids the curse of dimensionality when including large covariates in the model. In fact, the varying coefficient model is useful in mean, quantile, and other regressions. The tail index regression is not an exception. However, the varying coefficient model
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The resampling method via representative points Stat. Pap. (IF 1.3) Pub Date : 2024-03-18 Long-Hao Xu, Yinan Li, Kai-Tai Fang
The bootstrap method relies on resampling from the empirical distribution to provide inferences about the population with a distribution F. The empirical distribution serves as an approximation to the population. It is possible, however, to resample from another approximating distribution of F to conduct simulation-based inferences. In this paper, we utilize representative points to form an alternative
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An heuristic scree plot criterion for the number of factors Stat. Pap. (IF 1.3) Pub Date : 2024-03-18
Abstract Cattel’s (Multivar Behav Res 1:245–276, 1966) heuristic determines the number of factors as the elbow point between ‘steep’ and ‘not steep’ in the scree plot. In contrast, an elbow is by definition absent in points on a hyberbole with corresponding equisized surfaces. We formalize this heuristic and propose a criterion to determine the number of factors by comparing surfaces under the scree
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A semi-orthogonal nonnegative matrix tri-factorization algorithm for overlapping community detection Stat. Pap. (IF 1.3) Pub Date : 2024-03-14 Zhaoyang Li, Yuehan Yang
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Statistical simulations with LR random fuzzy numbers Stat. Pap. (IF 1.3) Pub Date : 2024-03-08 Abbas Parchami, Przemyslaw Grzegorzewski, Maciej Romaniuk
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Minimax weight learning for absorbing MDPs Stat. Pap. (IF 1.3) Pub Date : 2024-03-06 Fengying Li, Yuqiang Li, Xianyi Wu
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Welch’s t test is more sensitive to real world violations of distributional assumptions than student’s t test but logistic regression is more robust than either Stat. Pap. (IF 1.3) Pub Date : 2024-03-04 David Curtis
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Homogeneity tests and interval estimations of risk differences for stratified bilateral and unilateral correlated data Stat. Pap. (IF 1.3) Pub Date : 2024-03-04 Shuyi Liang, Kai-Tai Fang, Xin-Wei Huang, Yijing Xin, Chang-Xing Ma
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A scale-invariant test for linear hypothesis of means in high dimensions Stat. Pap. (IF 1.3) Pub Date : 2024-02-29
Abstract In this paper, we propose a new scale-invariant test for linear hypothesis of mean vectors with heteroscedasticity in high-dimensional settings. Most existing tests impose strong conditions on covariance matrices so that null distributions of their tests are asymptotically normal, which restricts the application of test procedures. However, our proposed test has different null distributions
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A unified approach to goodness-of-fit testing for spherical and hyperspherical data Stat. Pap. (IF 1.3) Pub Date : 2024-02-26 Bruno Ebner, Norbert Henze, Simos Meintanis
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Is Fisher inference inferior to Neyman inference for policy analysis? Stat. Pap. (IF 1.3) Pub Date : 2024-02-20 Rauf Ahmad, Per Johansson, Mårten Schultzberg
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The effect of correlated errors on the performance of local linear estimation of regression function based on random functional design Stat. Pap. (IF 1.3) Pub Date : 2024-02-14 Karim Benhenni, Ali Hajj Hassan, Yingcai Su
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Strong consistency of tail value-at-risk estimator and corresponding general results under widely orthant dependent samples Stat. Pap. (IF 1.3) Pub Date : 2024-01-17 Jinyu Zhou, Jigao Yan, Dongya Cheng
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Subgroup analysis with concave pairwise fusion penalty for ordinal response Stat. Pap. (IF 1.3) Pub Date : 2024-01-13 Weirong Li, Wensheng Zhu
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Some additional remarks on statistical properties of Cohen’s d in the presence of covariates Stat. Pap. (IF 1.3) Pub Date : 2024-01-12 Jürgen Groß, Annette Möller
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Deficiency bounds for the multivariate inverse hypergeometric distribution Stat. Pap. (IF 1.3) Pub Date : 2024-01-09 Frédéric Ouimet
The multivariate inverse hypergeometric (MIH) distribution is an extension of the negative multinomial (NM) model that accounts for sampling without replacement in a finite population. Even though most studies on longitudinal count data with a specific number of ‘failures’ occur in a finite setting, the NM model is typically chosen over the more accurate MIH model. This raises the question: How much
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Improved Breitung and Roling estimator for mixed-frequency models with application to forecasting inflation rates Stat. Pap. (IF 1.3) Pub Date : 2024-01-04
Abstract Instead of applying the commonly used parametric Almon or Beta lag distribution of MIDAS, Breitung and Roling (J Forecast 34:588–603, 2015) suggested a nonparametric smoothed least-squares shrinkage estimator (henceforth \({SLS}_{1}\) ) for estimating mixed-frequency models. This \({SLS}_{1}\) approach ensures a flexible smooth trending lag distribution. However, even if the biasing parameter
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Adaptive slicing for functional slice inverse regression Stat. Pap. (IF 1.3) Pub Date : 2024-01-02
Abstract In the paper, we propose a functional dimension reduction method for functional predictors and a scalar response. In the past study, the most popular functional dimension reduction method is the functional sliced inverse regression (FSIR) and people usually use a fixed slicing scheme to implement the estimation of FSIR. However, in practical, there are two main questions for the fixed slicing
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Optimal dichotomization of bimodal Gaussian mixtures Stat. Pap. (IF 1.3) Pub Date : 2024-01-02 Yan-ni Jhan, Wan-cen Li, Shin-hui Ruan, Jia-jyun Sie, Iebin Lian
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Semiparametric estimation in generalized additive partial linear models with nonignorable nonresponse data Stat. Pap. (IF 1.3) Pub Date : 2023-12-30 Jierui Du, Xia Cui
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Implicit profiling estimation for semiparametric models with bundled parameters Stat. Pap. (IF 1.3) Pub Date : 2023-12-27
Abstract Solving semiparametric models can be computationally challenging because the dimension of parameter space may grow large with increasing sample size. Classical Newton’s method becomes quite slow and unstable with an intensive calculation of the large Hessian matrix and its inverse. Iterative methods separately updating parameters for the finite dimensional component and the infinite dimensional
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Locally optimal designs for comparing curves in generalized linear models Stat. Pap. (IF 1.3) Pub Date : 2023-12-22 Chang-Yu Liu, Xin Liu, Rong-Xian Yue
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Using the softplus function to construct alternative link functions in generalized linear models and beyond Stat. Pap. (IF 1.3) Pub Date : 2023-12-15
Abstract Response functions that link regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is usually not further scrutinized. For example, the exponential response function is often assumed for parameters restricted to be positive
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Estimating the entropy of a Rayleigh model under progressive first-failure censoring Stat. Pap. (IF 1.3) Pub Date : 2023-12-14 Mohammed S. Kotb, Huda M. Alomari
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Testing omitted variables in VARs Stat. Pap. (IF 1.3) Pub Date : 2023-12-12 Andrea Beccarini
A procedure is outlined aiming at testing the bias due to omitted variables in vector autoregressions. The procedure consists first of filtering a vector of omitted variables and then testing the bias. The test does not rely on the availability of the omitted variables, and is based on a comparison between maximum-likelihood with Kalman filter vector autoregression and linear vector autoregression
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Analysis of the positive response data with the varying coefficient partially nonlinear multiplicative model Stat. Pap. (IF 1.3) Pub Date : 2023-12-11 Huilan Liu, Xiawei Zhang, Huaiqing Hu, Junjie Ma
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Robust signal dimension estimation via SURE Stat. Pap. (IF 1.3) Pub Date : 2023-12-09 Joni Virta, Niko Lietzén, Henri Nyberg
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Active-set based block coordinate descent algorithm in group LASSO for self-exciting threshold autoregressive model Stat. Pap. (IF 1.3) Pub Date : 2023-12-09 Muhammad Jaffri Mohd Nasir, Ramzan Nazim Khan, Gopalan Nair, Darfiana Nur
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Fourier approach to goodness-of-fit tests for Gaussian random processes Stat. Pap. (IF 1.3) Pub Date : 2023-12-01 Petr Čoupek, Viktor Dolník, Zdeněk Hlávka, Daniel Hlubinka
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Regression analysis of clustered panel count data with additive mean models Stat. Pap. (IF 1.3) Pub Date : 2023-11-28 Weiwei Wang, Zhiyang Cui, Ruijie Chen, Yijun Wang, Xiaobing Zhao
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Adaptive parametric change point inference under covariance structure changes Stat. Pap. (IF 1.3) Pub Date : 2023-11-16 Stergios B. Fotopoulos, Abhishek Kaul, Vasileios Pavlopoulos, Venkata K. Jandhyala
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A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method Stat. Pap. (IF 1.3) Pub Date : 2023-11-15 Valdério Anselmo Reisen, Céline Lévy-Leduc, Edson Zambon Monte, Pascal Bondon
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Alleviating conditional independence assumption of naive Bayes Stat. Pap. (IF 1.3) Pub Date : 2023-11-14 Xu-Qing Liu, Xiao-Cai Wang, Li Tao, Feng-Xian An, Gui-Ren Jiang
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A p-step-ahead sequential adaptive algorithm for D-optimal nonlinear regression design Stat. Pap. (IF 1.3) Pub Date : 2023-11-10 Fritjof Freise, Norbert Gaffke, Rainer Schwabe
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Two-piece distribution based semi-parametric quantile regression for right censored data Stat. Pap. (IF 1.3) Pub Date : 2023-11-10 Worku Biyadgie Ewnetu, Irène Gijbels, Anneleen Verhasselt
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Penalized likelihood inference for the finite mixture of Poisson distributions from capture-recapture data Stat. Pap. (IF 1.3) Pub Date : 2023-11-03 Yang Liu, Rong Kuang, Guanfu Liu
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FDR control and power analysis for high-dimensional logistic regression via StabKoff Stat. Pap. (IF 1.3) Pub Date : 2023-10-18 Panxu Yuan, Yinfei Kong, Gaorong Li
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Space-filling designs with a Dirichlet distribution for mixture experiments Stat. Pap. (IF 1.3) Pub Date : 2023-10-07 Astrid Jourdan
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On the test of covariance between two high-dimensional random vectors Stat. Pap. (IF 1.3) Pub Date : 2023-10-07 Yongshuai Chen, Wenwen Guo, Hengjian Cui
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A weighted average limited information maximum likelihood estimator Stat. Pap. (IF 1.3) Pub Date : 2023-10-07 Muhammad Qasim
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Professor Heinz Neudecker and matrix differential calculus Stat. Pap. (IF 1.3) Pub Date : 2023-10-03 Shuangzhe Liu, Götz Trenkler, Tõnu Kollo, Dietrich von Rosen, Oskar Maria Baksalary
The late Professor Heinz Neudecker (1933–2017) made significant contributions to the development of matrix differential calculus and its applications to econometrics, psychometrics, statistics, and other areas. In this paper, we present an insightful overview of matrix-oriented findings and their consequential implications in statistics, drawn from a careful selection of works either authored by Professor
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Variable selection in proportional odds model with informatively interval-censored data Stat. Pap. (IF 1.3) Pub Date : 2023-09-29 Bo Zhao, Shuying Wang, Chunjie Wang
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On strongly dependent zero-inflated INAR(1) processes Stat. Pap. (IF 1.3) Pub Date : 2023-09-29 Jan Beran, Frieder Droullier
We consider INAR(1) processes modulated by an unobserved strongly dependent \(0-1\) process. The observed process exhibits zero inflation and long memory. A simple method is proposed for estimating the INAR-parameters without modelling the unobserved modulating process. Asymptotic results for the estimators are derived, and a zero-inflation test is introduced. Asymptotic rejection regions and asymptotic
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Quantile regression for varying-coefficient partially nonlinear models with randomly truncated data Stat. Pap. (IF 1.3) Pub Date : 2023-09-29 Hong-Xia Xu, Guo-Liang Fan, Han-Ying Liang
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Maximum Likelihood With a Time Varying Parameter Stat. Pap. (IF 1.3) Pub Date : 2023-09-29 Alberto Lanconelli, Christopher S. A. Lauria
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ROBOUT: a conditional outlier detection methodology for high-dimensional data Stat. Pap. (IF 1.3) Pub Date : 2023-09-29 Matteo Farnè, Angelos Vouldis
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Least squares estimation for a class of uncertain Vasicek model and its application to interest rates Stat. Pap. (IF 1.3) Pub Date : 2023-09-25 Chao Wei
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Change point in variance of fractionally integrated noise Stat. Pap. (IF 1.3) Pub Date : 2023-09-25 Daiqing Xi, Tianxiao Pang