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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
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On the Baum–Katz theorem for randomly weighted sums of negatively associated random variables with general normalizing sequences and applications in some random design regression models Stat. Pap. (IF 1.3) Pub Date : 2023-09-19 Son Ta Cong, Cuong Tran Manh, Hang Bui Khanh, Dung Le Van
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A semiparametric dynamic higher-order spatial autoregressive model Stat. Pap. (IF 1.3) Pub Date : 2023-09-20 Tizheng Li, Yuping Wang, Ke Fang
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Detection of multiple change-points in high-dimensional panel data with cross-sectional and temporal dependence Stat. Pap. (IF 1.3) Pub Date : 2023-09-20 Marie-Christine Düker, Seok-Oh Jeong, Taewook Lee, Changryong Baek
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On the validity of the bootstrap hypothesis testing in functional linear regression Stat. Pap. (IF 1.3) Pub Date : 2023-09-20 Omid Khademnoe, S. Mohammad E. Hosseini-Nasab
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GMM estimation and variable selection of partially linear additive spatial autoregressive model Stat. Pap. (IF 1.3) Pub Date : 2023-09-19 Fang Lu, Guoliang Tian, Jing Yang
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Uniformly most powerful tests under weak restrictions Stat. Pap. (IF 1.3) Pub Date : 2023-09-19 Jin Zhang
Neyman–Pearson lemma establishes the most powerful tests for simple hypotheses, inducing the uniformly most powerful (UMP) tests for one-sided hypotheses on one-parameter models. For general hypotheses, there is no the UMP test without restrictions, but the classical UMP unbiased tests are too restricted and complex to easily apply. Hence, we create the simple UMP tests under much weaker restrictions
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Robust optimal subsampling based on weighted asymmetric least squares Stat. Pap. (IF 1.3) Pub Date : 2023-09-19 Min Ren, Shengli Zhao, Mingqiu Wang, Xinbei Zhu
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Bartlett corrections for zero-adjusted generalized linear models Stat. Pap. (IF 1.3) Pub Date : 2023-09-19 Tiago M. Magalhães, Gustavo H. A. Pereira, Denise A. Botter, Mônica C. Sandoval
Zero-adjusted generalized linear models (ZAGLMs) are used in many areas to fit variables that are discrete at zero and continuous on the positive real numbers. As in other classes of regression models, hypothesis testing inference in the class of ZAGLMs is usually performed using the likelihood ratio statistic. However, the LR test is substantially size distorted when the sample size is small. In this
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Integrating rather than collecting: statistical matching in the data flood era Stat. Pap. (IF 1.3) Pub Date : 2023-09-19 Riccardo D’Alberto, Meri Raggi
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Conditions for finiteness and bounds on moments of generalized order statistics Stat. Pap. (IF 1.3) Pub Date : 2023-09-19 Mariusz Bieniek, Tomasz Rychlik
We present necessary and sufficient conditions for the finiteness of moments of a fixed order of generalized order statistics based on an arbitrary life baseline distribution with a finite expectation. The conditions depend on a relation between the moment order and the minimal parameter of the generalized order statistic and its multiplicity. Furthermore, under these conditions we determine sharp
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Privacy-preserving and homogeneity-pursuit integrative analysis for high-dimensional censored data Stat. Pap. (IF 1.3) Pub Date : 2023-09-19 Xin Ye, Baihua He, Yanyan Liu, Shuangge Ma
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Smoothed empirical likelihood for the difference of two quantiles with the paired sample Stat. Pap. (IF 1.3) Pub Date : 2023-09-12 Pangpang Liu, Yichuan Zhao
In this paper, we propose a novel smoothed empirical likelihood method for the difference of quantiles with paired samples. While the empirical likelihood for the difference of two quantiles with independent samples has been studied, it is crucial to develop a statistical procedure that accounts for the dependence between paired samples from \({\varvec{X}}=(X_1, X_2)\). To this end, we propose two
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Kendall’s tau-based inference for gradually changing dependence structures Stat. Pap. (IF 1.3) Pub Date : 2023-09-08 Félix Camirand Lemyre, Jean-François Quessy