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A flexible Bayesian tool for CoDa mixed models: logistic-normal distribution with Dirichlet covariance Stat. Comput. (IF 2.2) Pub Date : 2024-04-16 Joaquín Martínez-Minaya, Haavard Rue
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A communication-efficient, online changepoint detection method for monitoring distributed sensor networks Stat. Comput. (IF 2.2) Pub Date : 2024-04-14 Ziyang Yang, Idris A. Eckley, Paul Fearnhead
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Parsimonious consensus hierarchies, partitions and fuzzy partitioning of a set of hierarchies Stat. Comput. (IF 2.2) Pub Date : 2024-04-12 Ilaria Bombelli, Maurizio Vichi
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Correction to: Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm Stat. Comput. (IF 2.2) Pub Date : 2024-04-08 Marion Naveau, Guillaume Kon Kam King, Renaud Rincent, Laure Sansonnet, Maud Delattre
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Reversed particle filtering for hidden markov models Stat. Comput. (IF 2.2) Pub Date : 2024-04-08 Frank Rotiroti, Stephen G. Walker
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Screen then select: a strategy for correlated predictors in high-dimensional quantile regression Stat. Comput. (IF 2.2) Pub Date : 2024-04-08 Xuejun Jiang, Yakun Liang, Haofeng Wang
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R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models Stat. Comput. (IF 2.2) Pub Date : 2024-04-06 Bao Anh Vu, David Gunawan, Andrew Zammit-Mangion
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Automated generation of initial points for adaptive rejection sampling of log-concave distributions Stat. Comput. (IF 2.2) Pub Date : 2024-04-05 Jonathan James
Adaptive rejection sampling requires that users provide points that span the distribution’s mode. If these points are far from the mode, it significantly increases computational costs. This paper introduces a simple, automated approach for selecting initial points that uses numerical optimization to quickly bracket the mode. When an initial point is given that resides in a high-density area, the method
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Parsimonious ultrametric Gaussian mixture models Stat. Comput. (IF 2.2) Pub Date : 2024-04-01 Carlo Cavicchia, Maurizio Vichi, Giorgia Zaccaria
Gaussian mixture models represent a conceptually and mathematically elegant class of models for casting the density of a heterogeneous population where the observed data is collected from a population composed of a finite set of G homogeneous subpopulations with a Gaussian distribution. A limitation of these models is that they suffer from the curse of dimensionality, and the number of parameters becomes
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Stochastic three-term conjugate gradient method with variance technique for non-convex learning Stat. Comput. (IF 2.2) Pub Date : 2024-03-27 Chen Ouyang, Chenkaixiang Lu, Xiong Zhao, Ruping Huang, Gonglin Yuan, Yiyan Jiang
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Novel sampling method for the von Mises–Fisher distribution Stat. Comput. (IF 2.2) Pub Date : 2024-03-26
Abstract The von Mises–Fisher distribution is a widely used probability model in directional statistics. An algorithm for generating pseudo-random vectors from this distribution was suggested by Wood (Commun Stat Simul Comput 23(1):157–164, 1994), which is based on a rejection sampling scheme. This paper proposes an alternative to this rejection sampling approach for drawing pseudo-random vectors from
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Variable selection using axis-aligned random projections for partial least-squares regression Stat. Comput. (IF 2.2) Pub Date : 2024-03-23
Abstract In high-dimensional data modeling, variable selection plays a crucial role in improving predictive accuracy and enhancing model interpretability through sparse representation. Unfortunately, certain variable selection methods encounter challenges such as insufficient model sparsity, high computational overhead, and difficulties in handling large-scale data. Recently, axis-aligned random projection
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An expectile computation cookbook Stat. Comput. (IF 2.2) Pub Date : 2024-03-23
Abstract A substantial body of work in the last 15 years has shown that expectiles constitute an excellent candidate for becoming a standard tool in probabilistic and statistical modeling. Surprisingly, the question of how expectiles may be efficiently calculated has been left largely untouched. We fill this gap by, first, providing a general outlook on the computation of expectiles that relies on
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Generalized spherical principal component analysis Stat. Comput. (IF 2.2) Pub Date : 2024-03-23 Sarah Leyder, Jakob Raymaekers, Tim Verdonck
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Simultaneous estimation and variable selection for a non-crossing multiple quantile regression using deep neural networks Stat. Comput. (IF 2.2) Pub Date : 2024-03-22 Jungmin Shin, Seunghyun Gwak, Seung Jun Shin, Sungwan Bang
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Resampling-based confidence intervals and bands for the average treatment effect in observational studies with competing risks Stat. Comput. (IF 2.2) Pub Date : 2024-03-21 Jasmin Rühl, Sarah Friedrich
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A constant-per-iteration likelihood ratio test for online changepoint detection for exponential family models Stat. Comput. (IF 2.2) Pub Date : 2024-03-19 Kes Ward, Gaetano Romano, Idris Eckley, Paul Fearnhead
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Improving model choice in classification: an approach based on clustering of covariance matrices Stat. Comput. (IF 2.2) Pub Date : 2024-03-19 David Rodríguez-Vítores, Carlos Matrán
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Functional mixtures-of-experts Stat. Comput. (IF 2.2) Pub Date : 2024-03-18 Faïcel Chamroukhi, Nhat Thien Pham, Van Hà Hoang, Geoffrey J. McLachlan
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Expectile and M-quantile regression for panel data Stat. Comput. (IF 2.2) Pub Date : 2024-03-17 Ian Meneghel Danilevicz, Valdério Anselmo Reisen, Pascal Bondon
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Doubly robust estimation of optimal treatment regimes for survival data using an instrumental variable Stat. Comput. (IF 2.2) Pub Date : 2024-03-16 Xia Junwen, Zhan Zishu, Zhang Jingxiao
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Matrix regression heterogeneity analysis Stat. Comput. (IF 2.2) Pub Date : 2024-03-16 Fengchuan Zhang, Sanguo Zhang, Shi-Ming Li, Mingyang Ren
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Quantile ratio regression Stat. Comput. (IF 2.2) Pub Date : 2024-03-14 Alessio Farcomeni, Marco Geraci
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Robust score matching for compositional data Stat. Comput. (IF 2.2) Pub Date : 2024-03-13 Janice L. Scealy, Kassel L. Hingee, John T. Kent, Andrew T. A. Wood
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Quantile generalized measures of correlation Stat. Comput. (IF 2.2) Pub Date : 2024-03-12 Xinyu Zhang, Hongwei Shi, Niwen Zhou, Falong Tan, Xu Guo
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Bayesian variable selection for matrix autoregressive models Stat. Comput. (IF 2.2) Pub Date : 2024-03-11 Alessandro Celani, Paolo Pagnottoni, Galin Jones
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Large-scale correlation screening under dependence for brain functional connectivity network inference Stat. Comput. (IF 2.2) Pub Date : 2024-03-09 Hanâ Lbath, Alexander Petersen, Sophie Achard
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Multiple-output quantile regression neural network Stat. Comput. (IF 2.2) Pub Date : 2024-03-08 Ruiting Hao, Xiaorong Yang
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Total effects with constrained features Stat. Comput. (IF 2.2) Pub Date : 2024-03-05
Abstract Recent studies have emphasized the connection between machine learning feature importance measures and total order sensitivity indices (total effects, henceforth). Feature correlations and the need to avoid unrestricted permutations make the estimation of these indices challenging. Additionally, there is no established theory or approach for non-Cartesian domains. We propose four alternative
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Estimation of regime-switching diffusions via Fourier transforms Stat. Comput. (IF 2.2) Pub Date : 2024-03-05 Thomas Lux
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High-dimensional sparse single–index regression via Hilbert–Schmidt independence criterion Stat. Comput. (IF 2.2) Pub Date : 2024-02-27 Xin Chen, Chang Deng, Shuaida He, Runxiong Wu, Jia Zhang
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Improvements on scalable stochastic Bayesian inference methods for multivariate Hawkes process Stat. Comput. (IF 2.2) Pub Date : 2024-02-27 Alex Ziyu Jiang, Abel Rodriguez
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Maximum likelihood estimation of log-concave densities on tree space Stat. Comput. (IF 2.2) Pub Date : 2024-02-23 Yuki Takazawa, Tomonari Sei
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Do applied statisticians prefer more randomness or less? Bootstrap or Jackknife? Stat. Comput. (IF 2.2) Pub Date : 2024-02-22 Yannis G. Yatracos
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Forward stability and model path selection Stat. Comput. (IF 2.2) Pub Date : 2024-02-20 Nicholas Kissel, Lucas Mentch
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Clustering longitudinal ordinal data via finite mixture of matrix-variate distributions Stat. Comput. (IF 2.2) Pub Date : 2024-02-17 Francesco Amato, Julien Jacques, Isabelle Prim-Allaz
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The minimum covariance determinant estimator for interval-valued data Stat. Comput. (IF 2.2) Pub Date : 2024-02-17 Wan Tian, Zhongfeng Qin
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Enmsp: an elastic-net multi-step screening procedure for high-dimensional regression Stat. Comput. (IF 2.2) Pub Date : 2024-02-16 Yushan Xue, Jie Ren, Bin Yang
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Bayesian parameter inference for partially observed stochastic volterra equations Stat. Comput. (IF 2.2) Pub Date : 2024-02-14 Ajay Jasra, Hamza Ruzayqat, Amin Wu
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Subsampling approach for least squares fitting of semi-parametric accelerated failure time models to massive survival data Stat. Comput. (IF 2.2) Pub Date : 2024-02-14 Zehan Yang, HaiYing Wang, Jun Yan
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New mixed portmanteau tests for time series models Stat. Comput. (IF 2.2) Pub Date : 2024-02-12
Abstract This article proposes omnibus portmanteau tests for contrasting adequacy of time series models. The test statistics are based on combining the autocorrelation function of the conditional residuals, the autocorrelation function of the conditional squared residuals, and the cross-correlation function between these residuals and their squares. The maximum likelihood estimator is used to derive
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COMBSS: best subset selection via continuous optimization Stat. Comput. (IF 2.2) Pub Date : 2024-02-12
Abstract The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very large compared to the number of data samples. Existing optimal methods for solving this problem tend to be slow while fast methods tend to have low accuracy
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A robust quantile regression for bounded variables based on the Kumaraswamy Rectangular distribution Stat. Comput. (IF 2.2) Pub Date : 2024-02-10
Abstract Quantile regression (QR) models offer an interesting alternative compared with ordinary regression models for the response mean. Besides allowing a more appropriate characterization of the response distribution, the former is less sensitive to outlying observations than the latter. Indeed, the QR models allow modeling other characteristics of the response distribution, such as the lower and/or
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A reliable data-based smoothing parameter selection method for circular kernel estimation Stat. Comput. (IF 2.2) Pub Date : 2024-02-07 Jose Ameijeiras-Alonso
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Fast generation of exchangeable sequences of clusters data Stat. Comput. (IF 2.2) Pub Date : 2024-02-07 Keith Levin, Brenda Betancourt
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Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences Stat. Comput. (IF 2.2) Pub Date : 2024-02-05 Carlos Echegoyen, Aritz Pérez, Guzmán Santafé, Unai Pérez-Goya, María Dolores Ugarte
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Global–local shrinkage multivariate logit-beta priors for multiple response-type data Stat. Comput. (IF 2.2) Pub Date : 2024-02-03 Hongyu Wu, Jonathan R. Bradley
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Kent feature embedding for classification of compositional data with zeros Stat. Comput. (IF 2.2) Pub Date : 2024-01-31 Shan Lu, Wenjing Wang, Rong Guan
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The sparse dynamic factor model: a regularised quasi-maximum likelihood approach Stat. Comput. (IF 2.2) Pub Date : 2024-01-22 Luke Mosley, Tak-Shing T. Chan, Alex Gibberd
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Variational Bayesian analysis of survival data using a log-logistic accelerated failure time model Stat. Comput. (IF 2.2) Pub Date : 2024-01-18 Chengqian Xian, Camila P. E. de Souza, Wenqing He, Felipe F. Rodrigues, Renfang Tian
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Expectile hidden Markov regression models for analyzing cryptocurrency returns Stat. Comput. (IF 2.2) Pub Date : 2024-01-13 Beatrice Foroni, Luca Merlo, Lea Petrella
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Efficient exponential tilting with applications Stat. Comput. (IF 2.2) Pub Date : 2024-01-13 Cheng-Der Fuh, Chuan-Ju Wang
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Bayesian contiguity constrained clustering Stat. Comput. (IF 2.2) Pub Date : 2024-01-12 Etienne Côme
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Nonparametric Bayesian online change point detection using kernel density estimation with nonparametric hazard function Stat. Comput. (IF 2.2) Pub Date : 2024-01-12 Naruesorn Prabpon, Kitakorn Homsud, Pat Vatiwutipong
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Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms Stat. Comput. (IF 2.2) Pub Date : 2024-01-10 Luca Brusa, Fulvia Pennoni, Francesco Bartolucci
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On the f-divergences between densities of a multivariate location or scale family Stat. Comput. (IF 2.2) Pub Date : 2024-01-05 Frank Nielsen, Kazuki Okamura
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NuZZ: Numerical Zig-Zag for general models Stat. Comput. (IF 2.2) Pub Date : 2024-01-05 Filippo Pagani, Augustin Chevallier, Sam Power, Thomas House, Simon Cotter
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Accelerated gradient methods for sparse statistical learning with nonconvex penalties Stat. Comput. (IF 2.2) Pub Date : 2024-01-02 Kai Yang, Masoud Asgharian, Sahir Bhatnagar
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Estimation of a likelihood ratio ordered family of distributions Stat. Comput. (IF 2.2) Pub Date : 2023-12-31 Alexandre Mösching, Lutz Dümbgen
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Detecting and diagnosing prior and likelihood sensitivity with power-scaling Stat. Comput. (IF 2.2) Pub Date : 2023-12-31 Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, Aki Vehtari