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Maximizing adjusted covariance: new supervised dimension reduction for classification Comput. Stat. (IF 1.3) Pub Date : 2024-04-02 Hyejoon Park, Hyunjoong Kim, Yung-Seop Lee
This study proposes a new linear dimension reduction technique called Maximizing Adjusted Covariance (MAC), which is suitable for supervised classification. The new approach is to adjust the covariance matrix between input and target variables using the within-class sum of squares, thereby promoting class separation after linear dimension reduction. MAC has a low computational cost and can complement
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A class of transformed joint quantile time series models with applications to health studies Comput. Stat. (IF 1.3) Pub Date : 2024-04-01 Fahimeh Tourani-Farani, Zeynab Aghabazaz, Iraj Kazemi
Extensions of quantile regression modeling for time series analysis are extensively employed in medical and health studies. This study introduces a specific class of transformed quantile-dispersion regression models for non-stationary time series. These models possess the flexibility to incorporate the time-varying structure into the model specification, enabling precise predictions for future decisions
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Matrix-variate normal mean-variance Birnbaum–Saunders distributions and related mixture models Comput. Stat. (IF 1.3) Pub Date : 2024-04-01 Salvatore D. Tomarchio
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A subspace aggregating algorithm for accurate classification Comput. Stat. (IF 1.3) Pub Date : 2024-03-09 Saeid Amiri, Reza Modarres
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Imbalanced data sampling design based on grid boundary domain for big data Comput. Stat. (IF 1.3) Pub Date : 2024-03-08
Abstract The data distribution is often associated with a priori-known probability, and the occurrence probability of interest events is small, so a large amount of imbalanced data appears in sociology, economics, engineering, and various other fields. The existing over- and under-sampling methods are widely used in imbalanced data classification problems, but over-sampling leads to overfitting, and
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Sparse estimation of linear model via Bayesian method $$^*$$ Comput. Stat. (IF 1.3) Pub Date : 2024-03-04
Abstract This paper considers the sparse estimation problem of regression coefficients in the linear model. Note that the global–local shrinkage priors do not allow the regression coefficients to be truly estimated as zero, we propose three threshold rules and compare their contraction properties, and also tandem those rules with the popular horseshoe prior and the horseshoe+ prior that are normally
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Degree selection methods for curve estimation via Bernstein polynomials Comput. Stat. (IF 1.3) Pub Date : 2024-03-02
Abstract Bernstein Polynomial (BP) bases can uniformly approximate any continuous function based on observed noisy samples. However, a persistent challenge is the data-driven selection of a suitable degree for the BPs. In the absence of noise, asymptotic theory suggests that a larger degree leads to better approximation. However, in the presence of noise, which reduces bias, a larger degree also results
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Automatic piecewise linear regression Comput. Stat. (IF 1.3) Pub Date : 2024-03-01 Mathias von Ottenbreit, Riccardo De Bin
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Variational Bayesian Lasso for spline regression Comput. Stat. (IF 1.3) Pub Date : 2024-02-24 Larissa C. Alves, Ronaldo Dias, Helio S. Migon
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Generation of normal distributions revisited Comput. Stat. (IF 1.3) Pub Date : 2024-02-23 Takayuki Umeda
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Bayesian estimation of the number of species from Poisson-Lindley stochastic abundance model using non-informative priors Comput. Stat. (IF 1.3) Pub Date : 2024-02-23 Anurag Pathak, Manoj Kumar, Sanjay Kumar Singh, Umesh Singh, Sandeep Kumar
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Bayesian regression models in gretl: the BayTool package Comput. Stat. (IF 1.3) Pub Date : 2024-02-21 Luca Pedini
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Bayesian sequential probability ratio test for vaccine efficacy trials Comput. Stat. (IF 1.3) Pub Date : 2024-02-20 Erina Paul, Santosh Sutradhar, Jonathan Hartzel, Devan V. Mehrotra
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Overlapping coefficient in network-based semi-supervised clustering Comput. Stat. (IF 1.3) Pub Date : 2024-02-19 Claudio Conversano, Luca Frigau, Giulia Contu
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First exit and Dirichlet problem for the nonisotropic tempered $$\alpha$$ -stable processes Comput. Stat. (IF 1.3) Pub Date : 2024-02-15 Xing Liu, Weihua Deng
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Some new invariant sum tests and MAD tests for the assessment of Benford’s law Comput. Stat. (IF 1.3) Pub Date : 2024-02-13 Wolfgang Kössler, Hans-J. Lenz, Xing D. Wang
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Convergence of the CUSUM estimation for a mean shift in linear processes with random coefficients Comput. Stat. (IF 1.3) Pub Date : 2024-02-12 Yi Wu, Wei Wang, Xuejun Wang
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Finite mixture of regression models for censored data based on the skew-t distribution Comput. Stat. (IF 1.3) Pub Date : 2024-02-10 Jiwon Park, Dipak K. Dey, Víctor H. Lachos
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Analysis of estimating the Bayes rule for Gaussian mixture models with a specified missing-data mechanism Comput. Stat. (IF 1.3) Pub Date : 2024-02-10
Abstract Semi-supervised learning approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the generative model framework with a missingness mechanism for unclassified observations, as introduced by Ahfock and McLachlan (Stat Comput 30:1–12, 2020). We show that in a partially classified sample, a classifier using Bayes’ rule of allocation
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A simulation model to analyze the behavior of a faculty retirement plan: a case study in Mexico Comput. Stat. (IF 1.3) Pub Date : 2024-02-09 Marco Antonio Montufar-Benítez, Jaime Mora-Vargas, Carlos Arturo Soto-Campos, Gilberto Pérez-Lechuga, José Raúl Castro-Esparza
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Fitting concentric elliptical shapes under general model Comput. Stat. (IF 1.3) Pub Date : 2024-02-09
Abstract Fitting concentric ellipses is a crucial yet challenging task in image processing, pattern recognition, and astronomy. To address this complexity, researchers have introduced simplified models by imposing geometric assumptions. These assumptions enable the linearization of the model through reparameterization, allowing for the extension of various fitting methods. However, these restrictive
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Semiparametric regression modelling of current status competing risks data: a Bayesian approach Comput. Stat. (IF 1.3) Pub Date : 2024-01-31 Pavithra Hariharan, P. G. Sankaran
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Exploring local explanations of nonlinear models using animated linear projections Comput. Stat. (IF 1.3) Pub Date : 2024-01-31 Nicholas Spyrison, Dianne Cook, Przemyslaw Biecek
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Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models Comput. Stat. (IF 1.3) Pub Date : 2024-01-26 Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang
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A new approach to nonparametric estimation of multivariate spectral density function using basis expansion Comput. Stat. (IF 1.3) Pub Date : 2024-01-20 Shirin Nezampour, Alireza Nematollahi, Robert T. Krafty, Mehdi Maadooliat
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Censored broken adaptive ridge regression in high-dimension Comput. Stat. (IF 1.3) Pub Date : 2024-01-17 Jeongjin Lee, Taehwa Choi, Sangbum Choi
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High-dimensional penalized Bernstein support vector classifier Comput. Stat. (IF 1.3) Pub Date : 2024-01-16 Rachid Kharoubi, Abdallah Mkhadri, Karim Oualkacha
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Random forest based quantile-oriented sensitivity analysis indices estimation Comput. Stat. (IF 1.3) Pub Date : 2024-01-12 Kévin Elie-Dit-Cosaque, Véronique Maume-Deschamps
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Structured dictionary learning of rating migration matrices for credit risk modeling Comput. Stat. (IF 1.3) Pub Date : 2024-01-10
Abstract Rating migration matrix is a crux to assess credit risks. Modeling and predicting these matrices are then an issue of great importance for risk managers in any financial institution. As a challenger to usual parametric modeling approaches, we propose a new structured dictionary learning model with auto-regressive regularization that is able to meet key expectations and constraints: small amount
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Testing for linearity in scalar-on-function regression with responses missing at random Comput. Stat. (IF 1.3) Pub Date : 2024-01-03 Manuel Febrero-Bande, Pedro Galeano, Eduardo García-Portugués, Wenceslao González-Manteiga
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A latent variable approach for modeling recall-based time-to-event data with Weibull distribution Comput. Stat. (IF 1.3) Pub Date : 2024-01-03
Abstract The ability of individuals to recall events is influenced by the time interval between the monitoring time and the occurrence of the event. In this article, we introduce a non-recall probability function that incorporates this information into our modeling framework. We model the time-to-event using the Weibull distribution and adopt a latent variable approach to handle situations where recall
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Estimation and prediction with data quality indexes in linear regressions Comput. Stat. (IF 1.3) Pub Date : 2023-12-20
Abstract Despite many statistical applications brush the question of data quality aside, it is a fundamental concern inherent to external data collection. In this paper, data quality relates to the confidence one can have about the covariate values in a regression framework. More precisely, we study how to integrate the information of data quality given by a \((n \times p)\) -matrix, with n the number
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An extended Langevinized ensemble Kalman filter for non-Gaussian dynamic systems Comput. Stat. (IF 1.3) Pub Date : 2023-12-14 Peiyi Zhang, Tianning Dong, Faming Liang
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An effective method for identifying clusters of robot strengths Comput. Stat. (IF 1.3) Pub Date : 2023-12-11 Jen-Chieh Teng, Chin-Tsang Chiang, Alvin Lim
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High dimensional controlled variable selection with model-X knockoffs in the AFT model Comput. Stat. (IF 1.3) Pub Date : 2023-12-09 Baihua He, Di Xia, Yingli Pan
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Dimension reduction and visualization of multiple time series data: a symbolic data analysis approach Comput. Stat. (IF 1.3) Pub Date : 2023-12-06 Emily Chia-Yu Su, Han-Ming Wu
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An expectation maximization algorithm for the hidden markov models with multiparameter student-t observations Comput. Stat. (IF 1.3) Pub Date : 2023-12-06 Emna Ghorbel, Mahdi Louati
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Sequential linear regression for conditional mean imputation of longitudinal continuous outcomes under reference-based assumptions Comput. Stat. (IF 1.3) Pub Date : 2023-12-03 Sean Yiu
In clinical trials of longitudinal continuous outcomes, reference based imputation (RBI) has commonly been applied to handle missing outcome data in settings where the estimand incorporates the effects of intercurrent events, e.g. treatment discontinuation. RBI was originally developed in the multiple imputation framework, however recently conditional mean imputation (CMI) combined with the jackknife
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Pair programming with ChatGPT for sampling and estimation of copulas Comput. Stat. (IF 1.3) Pub Date : 2023-12-01 Jan Górecki
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Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis Comput. Stat. (IF 1.3) Pub Date : 2023-11-26 Wenxing Guo, Xueying Zhang, Bei Jiang, Linglong Kong, Yaozhong Hu
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Two-sample Behrens–Fisher problems for high-dimensional data: a normal reference F-type test Comput. Stat. (IF 1.3) Pub Date : 2023-11-24 Tianming Zhu, Pengfei Wang, Jin-Ting Zhang
The problem of testing the equality of mean vectors for high-dimensional data has been intensively investigated in the literature. However, most of the existing tests impose strong assumptions on the underlying group covariance matrices which may not be satisfied or hardly be checked in practice. In this article, an F-type test for two-sample Behrens–Fisher problems for high-dimensional data is proposed
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A new bandwidth selection method for nonparametric modal regression based on generalized hyperbolic distributions Comput. Stat. (IF 1.3) Pub Date : 2023-11-18 Hongpeng Yuan, Sijia Xiang, Weixin Yao
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Simultaneous subgroup identification and variable selection for high dimensional data Comput. Stat. (IF 1.3) Pub Date : 2023-11-17 Huicong Yu, Jiaqi Wu, Weiping Zhang
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Nonparametric estimation of expected shortfall for α-mixing financial losses Comput. Stat. (IF 1.3) Pub Date : 2023-11-14 Xuejun Wang, Yi Wu, Wei Wang
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A software reliability model incorporating fault removal efficiency and it’s release policy Comput. Stat. (IF 1.3) Pub Date : 2023-11-09 Umashankar Samal, Ajay Kumar
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Fuzzy clustering of time series based on weighted conditional higher moments Comput. Stat. (IF 1.3) Pub Date : 2023-11-05 Roy Cerqueti, Pierpaolo D’Urso, Livia De Giovanni, Raffaele Mattera, Vincenzina Vitale
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Estimation and testing of kink regression model with endogenous regressors Comput. Stat. (IF 1.3) Pub Date : 2023-11-06 Yan Sun, Wei Huang
Kink regression model which assumes continuity at the threshold point has wide applications in statistics and economics. Existing estimation methods are obtained under a rather important assumption that the errors are mean independent of the threshold variable, namely, it is exogenous. However, endogeneity can arise as a result of omitted variables, lagged dependent variable, measurement error and
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Nonparametric confidence intervals for generalized Lorenz curve using modified empirical likelihood Comput. Stat. (IF 1.3) Pub Date : 2023-11-03 Suthakaran Ratnasingam, Spencer Wallace, Imran Amani, Jade Romero
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Combination of optimization-free kriging models for high-dimensional problems Comput. Stat. (IF 1.3) Pub Date : 2023-10-27 Tanguy Appriou, Didier Rullière, David Gaudrie
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Incremental singular value decomposition for some numerical aspects of multiblock redundancy analysis Comput. Stat. (IF 1.3) Pub Date : 2023-10-24 Alba Martinez-Ruiz, Natale Carlo Lauro
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Nonparametric binary regression models with spherical predictors based on the random forests kernel Comput. Stat. (IF 1.3) Pub Date : 2023-10-23 Xu Qin, Huiqun Gao
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Empirical likelihood ratio tests for homogeneity of component lifetime distributions based on system lifetime data Comput. Stat. (IF 1.3) Pub Date : 2023-10-21 Jingjing Qu, Hon Keung Tony Ng, Chul Moon
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Dominance of posterior predictive densities over plug-in densities for order statistics in exponential distributions Comput. Stat. (IF 1.3) Pub Date : 2023-10-18 Kouhei Nishi, Takeshi Kurosawa, Nobuyuki Ozeki
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Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: an optimal control approach Comput. Stat. (IF 1.3) Pub Date : 2023-10-14 Quentin Clairon, Chloé Pasin, Irene Balelli, Rodolphe Thiébaut, Mélanie Prague
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Nonparametric derivative estimation with bimodal kernels under correlated errors Comput. Stat. (IF 1.3) Pub Date : 2023-10-09 Deru Kong, Shengli Zhao, WenWu Wang
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Variational Bayesian analysis for two-part latent variable model Comput. Stat. (IF 1.3) Pub Date : 2023-10-04 Yemao Xia, Jinye Chen, Depeng Jiang
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Housing variables and immigration: an exploratory analysis in New York City Comput. Stat. (IF 1.3) Pub Date : 2023-09-29 Jhonatan Medri, Braden D. Probst, Jürgen Symanzik
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Power Burr X-T family of distributions: properties, estimation methods and real-life applications Comput. Stat. (IF 1.3) Pub Date : 2023-09-26 Rana Muhammad Usman, Maryam Ilyas
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An adaptive method for bandwidth selection in circular kernel density estimation Comput. Stat. (IF 1.3) Pub Date : 2023-09-27 Stanislav Zámečník, Ivana Horová, Stanislav Katina, Kamila Hasilová
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A general stream sampling design Comput. Stat. (IF 1.3) Pub Date : 2023-09-20 Bardia Panahbehagh, Raphaël Jauslin, Yves Tillé