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Data segmentation for time series based on a general moving sum approach Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2024-03-14 Claudia Kirch, Kerstin Reckruehm
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Testing against ordered alternatives in one-way ANOVA model with exponential errors Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2024-03-12 Anjana Mondal, Markus Pauly, Somesh Kumar
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Regularized nonlinear regression with dependent errors and its application to a biomechanical model Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2024-02-08 Hojun You, Kyubaek Yoon, Wei-Ying Wu, Jongeun Choi, Chae Young Lim
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Multivariate Hawkes processes with spatial covariates for spatiotemporal event data analysis Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2024-01-29 Chenlong Li, Kaiyan Cui
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Gradual change-point analysis based on Spearman matrices for multivariate time series Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2024-01-05 Jean-François Quessy
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Non-parametric adaptive bandwidth selection for kernel estimators of spatial intensity functions Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-12-22
Abstract We introduce a new fully non-parametric two-step adaptive bandwidth selection method for kernel estimators of spatial point process intensity functions based on the Campbell–Mecke formula and Abramson’s square root law. We present a simulation study to assess its performance relative to other adaptive and global bandwidth selectors, investigate the influence of the pilot estimator and apply
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Test for conditional quantile change in general conditional heteroscedastic time series models Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-12-15 Sangyeol Lee, Chang Kyeom Kim
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Multivariate frequency polygon for stationary random fields Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-11-08 Michel Carbon, Thierry Duchesne
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Identifiability of latent-variable and structural-equation models: from linear to nonlinear Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-11-04 Aapo Hyvärinen, Ilyes Khemakhem, Ricardo Monti
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On estimation of nonparametric regression models with autoregressive and moving average errors Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-10-26 Qi Zheng, Yunwei Cui, Rongning Wu
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On a projection least squares estimator for jump diffusion processes Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-09-11 Hélène Halconruy, Nicolas Marie
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Comparing regression curves: an L1-point of view Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-08-30 Patrick Bastian, Holger Dette, Lukas Koletzko, Kathrin Möllenhoff
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Gaussian quasi-information criteria for ergodic Lévy driven SDE Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-08-11 Shoichi Eguchi, Hiroki Masuda
We consider relative model comparison for the parametric coefficients of an ergodic Lévy driven model observed at high-frequency. Our asymptotics is based on the fully explicit two-stage Gaussian quasi-likelihood function (GQLF) of the Euler-approximation type. For selections of the scale and drift coefficients, we propose explicit Gaussian quasi-AIC and Gaussian quasi-BIC statistics through the stepwise
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Approximating symmetrized estimators of scatter via balanced incomplete U-statistics Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-08-08 Lutz Dümbgen, Klaus Nordhausen
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A tuning-free efficient test for marginal linear effects in high-dimensional quantile regression Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-07-18 Kai Xu, Nan An
This work is concerned with testing the marginal linear effects of high-dimensional predictors in quantile regression. We introduce a novel test that is constructed using maxima of pairwise quantile correlations, which permit consistent assessment of the marginal linear effects. The proposed testing procedure is computationally efficient with the aid of a simple multiplier bootstrap method and does
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Model averaging for estimating treatment effects Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-06-30 Zhihao Zhao, Xinyu Zhang, Guohua Zou, Alan T. K. Wan, Geoffrey K. F. Tso
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Comparative evaluation of point process forecasts Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-06-15 Jonas R. Brehmer, Tilmann Gneiting, Marcus Herrmann, Warner Marzocchi, Martin Schlather, Kirstin Strokorb
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Goodness-of-fit tests for the Weibull distribution based on the Laplace transform and Stein’s method Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-05-22 Bruno Ebner, Adrian Fischer, Norbert Henze, Celeste Mayer
We propose novel goodness-of-fit tests for the Weibull distribution with unknown parameters. These tests are based on an alternative characterizing representation of the Laplace transform related to the density approach in the context of Stein’s method. Asymptotic theory of the tests is derived, including the limit null distribution, the behaviour under contiguous alternatives, the validity of the
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Estimation of complier causal treatment effects with informatively interval-censored failure time data Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-05-15 Yuqing Ma, Peijie Wang, Jianguo Sun
Estimation of compiler causal treatment effects has been discussed by many authors under different situations but only limited literature exists for interval-censored failure time data, which often occur in many areas such as longitudinal or periodical follow-up studies. Particularly it does not seem to exist a method that can deal with informative interval censoring, which can happen naturally and
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Robust variable selection with exponential squared loss for partially linear spatial autoregressive models Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-05-03 Xiuli Wang, Jingchang Shao, Jingjing Wu, Qiang Zhao
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Statistical inference using regularized M-estimation in the reproducing kernel Hilbert space for handling missing data Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-04-27 Hengfang Wang, Jae Kwang Kim
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A goodness-of-fit test on the number of biclusters in a relational data matrix Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-04-17 Chihiro Watanabe, Taiji Suzuki
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Gene–environment interaction analysis under the Cox model Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-04-10 Kuangnan Fang, Jingmao Li, Yaqing Xu, Shuangge Ma, Qingzhao Zhang
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Parametric estimation of spatial–temporal point processes using the Stoyan–Grabarnik statistic Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-03-10 Conor Kresin, Frederic Schoenberg
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Automatic data-based bin width selection for rose diagram Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-03-09 Yasuhito Tsuruta, Masahiko Sagae
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Mixture of shifted binomial distributions for rating data Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-02-10 Shaoting Li, Jiahua Chen
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Least absolute deviation estimation for AR(1) processes with roots close to unity Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-01-23 Nannan Ma, Hailin Sang, Guangyu Yang
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Nonparametric multiple regression by projection on non-compactly supported bases Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-01-22 Florian Dussap
We study the nonparametric regression estimation problem with a random design in \({\mathbb{R}}^{p}\) with \(p\ge 2\). We do so by using a projection estimator obtained by least squares minimization. Our contribution is to consider non-compact estimation domains in \({\mathbb {R}}^{p}\), on which we recover the function, and to provide a theoretical study of the risk of the estimator relative to a
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Robust density power divergence estimates for panel data models Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2023-01-20 Abhijit Mandal, Beste Hamiye Beyaztas, Soutir Bandyopadhyay
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A copula spectral test for pairwise time reversibility Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-12-26 Shibin Zhang
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Generation of all randomizations using circuits Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-12-23 Elena Pesce, Fabio Rapallo, Eva Riccomagno, Henry P. Wynn
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Model averaging for semiparametric varying coefficient quantile regression models Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-12-22 Zishu Zhan, Yang Li, Yuhong Yang, Cunjie Lin
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Slash distributions, generalized convolutions, and extremes Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-12-20 M. Arendarczyk, T. J. Kozubowski, A. K. Panorska
An \(\alpha\)-slash distribution built upon a random variable X is a heavy tailed distribution corresponding to \(Y=X/U^{1/\alpha }\), where U is standard uniform random variable, independent of X. We point out and explore a connection between \(\alpha\)-slash distributions, which are gaining popularity in statistical practice, and generalized convolutions, which come up in the probability theory as
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A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-12-08 Zeyu Wu, Cheng Wang, Weidong Liu
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Data-driven model selection for same-realization predictions in autoregressive processes Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-11-27 Kare Kamila
This paper is about the one-step ahead prediction of the future of observations drawn from an infinite-order autoregressive AR(\(\infty \)) process. It aims to design penalties (fully data driven) ensuring that the selected model verifies the efficiency property but in the non-asymptotic framework. We show that the excess risk of the selected estimator enjoys the best bias-variance trade-off over the
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Bootstrap method for misspecified ergodic Lévy driven stochastic differential equation models Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-11-10 Yuma Uehara
In this paper, we consider possibly misspecified stochastic differential equation models driven by Lévy processes. Regardless of whether the driving noise is Gaussian or not, Gaussian quasi-likelihood estimator can estimate unknown parameters in the drift and scale coefficients. However, in the misspecified case, the asymptotic distribution of the estimator varies by the correction of the misspecification
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Tests for the existence of group effects and interactions for two-way models with dependent errors Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-10-31 Yuichi Goto, Kotone Suzuki, Xiaofei Xu, Masanobu Taniguchi
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Robust estimation for nonrandomly distributed data Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-10-12 Shaomin Li, Kangning Wang, Yong Xu
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Inhomogeneous hidden semi-Markov models for incompletely observed point processes Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-09-18 Amina Shahzadi, Ting Wang, Mark Bebbington, Matthew Parry
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Matrix completion under complex survey sampling Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-09-19 Xiaojun Mao, Zhonglei Wang, Shu Yang
Multivariate nonresponse is often encountered in complex survey sampling, and simply ignoring it leads to erroneous inference. In this paper, we propose a new matrix completion method for complex survey sampling. Different from existing works either conducting row-wise or column-wise imputation, the data matrix is treated as a whole which allows for exploiting both row and column patterns simultaneously
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Regression analysis for exponential family data in a finite population setup using two-stage cluster sample Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-09-14 Brajendra C. Sutradhar
Over the last four decades, the cluster regression analysis in a finite population (FP) setup for an exponential family such as linear or binary data was done by using a two-stage cluster sample chosen from the FP but by treating the sample as though it is a single-stage cluster sample from a super-population (SP) which contains the FP as a hypothetical sample. Because the responses within a cluster
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Asymptotic theory in network models with covariates and a growing number of node parameters Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-09-02 Qiuping Wang, Yuan Zhang, Ting Yan
We propose a general model that jointly characterizes degree heterogeneity and homophily in weighted, undirected networks. We present a moment estimation method using node degrees and homophily statistics. We establish consistency and asymptotic normality of our estimator using novel analysis. We apply our general framework to three applications, including both exponential family and non-exponential
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Quantitative robustness of instance ranking problems Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-08-30 Tino Werner
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Forward variable selection for ultra-high dimensional quantile regression models Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-08-29 Toshio Honda, Chien-Tong Lin
We propose forward variable selection procedures with a stopping rule for feature screening in ultra-high-dimensional quantile regression models. For such very large models, penalized methods do not work and some preliminary feature screening is necessary. We demonstrate the desirable theoretical properties of our forward procedures by taking care of uniformity w.r.t. subsets of covariates properly
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Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-08-27 Toshiaki Tsukurimichi, Yu Inatsu, Vo Nguyen Le Duy, Ichiro Takeuchi
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Flexible asymmetric multivariate distributions based on two-piece univariate distributions Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-08-02 Jonas Baillien, Irène Gijbels, Anneleen Verhasselt
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On the choice of the optimal single order statistic in quantile estimation Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-08-02 Mariusz Bieniek, Luiza Pańczyk
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Selective inference after feature selection via multiscale bootstrap Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-07-30 Yoshikazu Terada, Hidetoshi Shimodaira
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Group least squares regression for linear models with strongly correlated predictor variables Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-07-26 Min Tsao
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Inference using an exact distribution of test statistic for random-effects meta-analysis Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-07-26 Keisuke Hanada, Tomoyuki Sugimoto
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Nonparametric inference for additive models estimated via simplified smooth backfitting Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-07-15 Suneel Babu Chatla
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Exact statistical inference for the Wasserstein distance by selective inference Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-06-28 Vo Nguyen Le Duy, Ichiro Takeuchi
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Robust estimation of the conditional stable tail dependence function Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-06-28 Yuri Goegebeur, Armelle Guillou, Jing Qin
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Estimation with multivariate outcomes having nonignorable item nonresponse Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-06-10 Lyu Ni, Jun Shao
To estimate unknown population parameters based on \({\varvec{y}}\), a vector of multivariate outcomes having nonignorable item nonresponse that directly depends on \({\varvec{y}}\), we propose an innovative inverse propensity weighting approach when the joint distribution of \({\varvec{y}}\) and associated covariate \({\varvec{x}}\) is nonparametric and the nonresponse probability conditional on \({\varvec{y}}\)
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Discussion of “Akaike Memorial Lecture 2020: Some of the challenges of statistical applications” Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-05-28 Masataka Taguri
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Akaike Memorial Lecture 2020: Some of the challenges of statistical applications Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-05-25 John Copas
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Semiparametric modelling of two-component mixtures with stochastic dominance Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-05-24 Jingjing Wu, Tasnima Abedin, Qiang Zhao
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Joint behavior of point processes of clusters and partial sums for stationary bivariate Gaussian triangular arrays Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-05-21 Jinhui Guo, Yingyin Lu
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Inference of random effects for linear mixed-effects models with a fixed number of clusters Ann. Inst. Stat. Math. (IF 1.0) Pub Date : 2022-05-14 Chih-Hao Chang, Hsin-Cheng Huang, Ching-Kang Ing
We consider a linear mixed-effects model with a clustered structure, where the parameters are estimated using maximum likelihood (ML) based on possibly unbalanced data. Inference with this model is typically done based on asymptotic theory, assuming that the number of clusters tends to infinity with the sample size. However, when the number of clusters is fixed, classical asymptotic theory developed