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You searched for: Journal name Computational statistics & data analysis Remove constraint Journal name: Computational statistics & data analysis Publication year rev 7978-2022 Remove constraint Publication year rev: 7978-2022 Source 2022 v.173 Remove constraint Source: 2022 v.173
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1. A rank-based high-dimensional test for equality of mean vectors

Author:
Yanyan Ouyang; Jiamin Liu; Tiejun Tong; Wangli Xu
Source:
Computational statistics & data analysis 2022 v.173 pp. 107495
ISSN:
0167-9473
Subject:
data analysis; data collection; gene expression; humans; statistics
Abstract:
... The Wilcoxon signed-rank test and the Wilcoxon-Mann-Whitney test are two commonly used rank-based methods for one- and two-sample tests when the one-dimensional data are not normally distributed. The new rank-based nonparametric tests for equality of mean vectors are proposed in the high-dimensional settings. To overcome the technical challenges in data sorting, the new statistics are constructed ...
DOI:
10.1016/j.csda.2022.107495

2. Complexity reduction and approximation of multidomain systems of partially ordered data

Author:
Alberto Arcagni; Alessandro Avellone; Marco Fattore
Source:
Computational statistics & data analysis 2022 v.173 pp. 107520
ISSN:
0167-9473
Subject:
algorithms; data analysis; statistics; Italy
Abstract:
... Two greedy algorithms for the synthesis and approximation of multidomain systems of partially ordered data are proposed. Given k input partially ordered sets (posets) on the same elements, the algorithms search for the optimally approximating partial orders, minimizing the dissimilarity between the generated and input posets, based on their matrices of mutual ranking probabilities. A general appro ...
DOI:
10.1016/j.csda.2022.107520

3. Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations

Author:
Muye Nanshan; Nan Zhang; Xiaolei Xun; Jiguo Cao
Source:
Computational statistics & data analysis 2022 v.173 pp. 107483
ISSN:
0167-9473
Subject:
cell cycle; data analysis; data collection; stock exchange; yeasts
Abstract:
... Ordinary differential equations (ODE) have been widely used for modeling dynamical complex systems. For high-dimensional ODE models where the number of differential equations is large, it remains challenging to estimate the ODE parameters and to identify the sparse structure of the ODE models. Most existing methods exploit the least-square based approach and are only applicable to Gaussian observa ...
DOI:
10.1016/j.csda.2022.107483

4. Feature screening and FDR control with knockoff features for ultrahigh-dimensional right-censored data

Author:
Yingli Pan
Source:
Computational statistics & data analysis 2022 v.173 pp. 107504
ISSN:
0167-9473
Subject:
data analysis; lymphoma; probability
Abstract:
... A model-free feature screening method for ultrahigh-dimensional right-censored data is advocated. A two-step approach, with the help of knockoff features, is proposed to specify the threshold for feature screening such that the false discovery rate (FDR) is controlled under a prespecified level. The proposed two-step approach enjoys both a sure screening property with high probability and FDR cont ...
DOI:
10.1016/j.csda.2022.107504

5. Marginal M-quantile regression for multivariate dependent data

Author:
Luca Merlo; Lea Petrella; Nicola Salvati; Nikos Tzavidis
Source:
Computational statistics & data analysis 2022 v.173 pp. 107500
ISSN:
0167-9473
Subject:
data analysis; models; regression analysis
Abstract:
... An M-quantile regression model is developed for the analysis of multiple dependent outcomes by introducing the notion of directional M-quantiles for multivariate responses. In order to incorporate the correlation structure of the data into the estimation framework, a robust marginal M-quantile model is proposed extending the well-known generalized estimating equations approach to the case of regre ...
DOI:
10.1016/j.csda.2022.107500

6. On the semi-varying coefficient dynamic panel data model with autocorrelated errors

Author:
Honglei Wei; Hongfan Zhang; Hui Jiang; Lei Huang
Source:
Computational statistics & data analysis 2022 v.173 pp. 107458
ISSN:
0167-9473
Subject:
autocorrelation; dynamic models; prediction; time series analysis
Abstract:
... In nonlinear time series modeling, autocorrelation of the random errors may cause critical problems in estimation and inference. The situation becomes even worse for panel data with dynamic structure. However, most of the existing literature has not taken into account this problem. The challenge comes from the fact that the expectation of random errors conditional on lag variables is hardly to be ...
DOI:
10.1016/j.csda.2022.107458

7. Safe sample screening rules for multicategory angle-based support vector machines

Author:
Yiwei Fan; Junlong Zhao
Source:
Computational statistics & data analysis 2022 v.173 pp. 107508
ISSN:
0167-9473
Subject:
data analysis; statistics; support vector machines
Abstract:
... Support vector machines are popular techniques for classification problems, where the optimal separating hyperplane only depends on a subset of training data. To reduce computational costs, safe sample screening rules are proposed in the literature, which enable us to remove redundant samples prior to the training phase. However, existing works on safe sample screening rules mainly focus on binary ...
DOI:
10.1016/j.csda.2022.107508

8. Stochastic representation of FGM copulas using multivariate Bernoulli random variables

Author:
Christopher Blier-Wong; Hélène Cossette; Etienne Marceau
Source:
Computational statistics & data analysis 2022 v.173 pp. 107506
ISSN:
0167-9473
Subject:
Bernoulli distribution; computational methodology; data analysis; sampling
Abstract:
... A one-to-one correspondence between Fréchet's class of multivariate Bernoulli distribution with symmetric marginals and the well-known family of Farlie-Gumbel-Morgenstern (FGM) copulas is established. A new stochastic representation of the family of d-variate FGM copulas is introduced. The representation is bijective: from any d-variate Bernoulli distribution, one may define a corresponding d-vari ...
DOI:
10.1016/j.csda.2022.107506

9. A two-stage optimal subsampling estimation for missing data problems with large-scale data

Author:
Miaomiao Su; Ruoyu Wang; Qihua Wang
Source:
Computational statistics & data analysis 2022 v.173 pp. 107505
ISSN:
0167-9473
Subject:
data analysis; probability; variance
Abstract:
... Subsampling is useful to downsize data volumes and speed up calculations for large-scale data and is well studied with completely observed data. In the presence of missing data, computation is more challenging and subsampling becomes more crucial and complex. However, there is still a lack of study on subsampling for missing data problems. This paper fills the gap by studying the subsampling metho ...
DOI:
10.1016/j.csda.2022.107505

10. Bayesian multiresolution modeling of georeferenced data: An extension of ‘LatticeKrig’

Author:
John Paige; Geir-Arne Fuglstad; Andrea Riebler; Jon Wakefield
Source:
Computational statistics & data analysis 2022 v.173 pp. 107503
ISSN:
0167-9473
Subject:
Bayesian theory; canopy height; covariance; data analysis; forest canopy; forests; georeferencing; health surveys; lidar; models; secondary education; spatial data; streams; uncertainty; Alaska; Kenya
Abstract:
... ‘LatticeKrig’ (LK) is a spatial model that is often used for modeling multiresolution spatial data with flexible covariance structures. An extension to LK under a Bayesian framework is proposed that uses integrated nested Laplace approximations (INLA). The extension enables the spatial analysis of non-Gaussian responses in latent Gaussian models, joint spatial modeling with structured and unstruct ...
DOI:
10.1016/j.csda.2022.107503

11. Clustering, multicollinearity, and singular vectors

Author:
Hamid Usefi
Source:
Computational statistics & data analysis 2022 v.173 pp. 107523
ISSN:
0167-9473
Subject:
algorithms; artificial intelligence; data analysis; least squares
Abstract:
... Let A be a matrix with its Moore-Penrose pseudo-inverse A†. It is proved that, after re-ordering the columns of A, the projector P=I−A†A has a block-diagonal form, that is there is a permutation matrix Π such that ΠPΠT=diag(S1,S2,…,Sk). It is further proved that each block Si corresponds to a cluster of columns of A that are linearly dependent with each other. A clustering algorithm is provided th ...
DOI:
10.1016/j.csda.2022.107523

12. Entropy-based test for generalised Gaussian distributions

Author:
Mehmet Siddik Cadirci; Dafydd Evans; Nikolai Leonenko; Vitalii Makogin
Source:
Computational statistics & data analysis 2022 v.173 pp. 107502
ISSN:
0167-9473
Subject:
data analysis; entropy; normal distribution; testing
Abstract:
... The proof of L2 consistency for the kth nearest neighbour distance estimator of the Shannon entropy for an arbitrary fixed k≥1 is provided. It is constructed the non-parametric test of goodness-of-fit for a class of introduced generalised multivariate Gaussian distributions based on a maximum entropy principle. The theoretical results are followed by numerical studies on simulated samples. It is s ...
DOI:
10.1016/j.csda.2022.107502

13. Joint non-parametric estimation of mean and auto-covariances for Gaussian processes

Author:
Tatyana Krivobokova; Paulo Serra; Francisco Rosales; Karolina Klockmann
Source:
Computational statistics & data analysis 2022 v.173 pp. 107519
ISSN:
0167-9473
Subject:
autocorrelation; data analysis; methodology; normal distribution
Abstract:
... Gaussian processes that can be decomposed into a smooth mean function and a stationary autocorrelated noise process are considered and a fully automatic nonparametric method to simultaneous estimation of mean and auto-covariance functions of such processes is developed. The proposed empirical Bayes approach is data-driven, numerically efficient, and allows for the construction of confidence sets f ...
DOI:
10.1016/j.csda.2022.107519

14. Local and global topics in text modeling of web pages nested in web sites

Author:
Jason Wang; Robert E. Weiss
Source:
Computational statistics & data analysis 2022 v.173 pp. 107518
ISSN:
0167-9473
Subject:
Internet; data analysis; statistics
Abstract:
... Topic models assert that documents are distributions over latent topics and latent topics are distributions over words. A nested document collection has documents nested inside a higher order structure such as articles nested in journals, podcasts within authors, or web pages nested in web sites. In a single collection of documents, topics are global or shared across all documents. For web pages n ...
DOI:
10.1016/j.csda.2022.107518

15. Log-regularly varying scale mixture of normals for robust regression

Author:
Yasuyuki Hamura; Kaoru Irie; Shonosuke Sugasawa
Source:
Computational statistics & data analysis 2022 v.173 pp. 107517
ISSN:
0167-9473
Subject:
data analysis; empirical research; models; regression analysis; shrinkage
Abstract:
... Linear regression that employs the assumption of normality for the error distribution may lead to an undesirable posterior inference of regression coefficients due to potential outliers. A finite mixture of two components, one with thin and one with heavy tails, is considered as the error distribution in this study. For the heavily-tailed component, the novel class of distributions is introduced; ...
DOI:
10.1016/j.csda.2022.107517

16. Mallows model averaging with effective model size in fragmentary data prediction

Author:
Chaoxia Yuan; Fang Fang; Lyu Ni
Source:
Computational statistics & data analysis 2022 v.173 pp. 107497
ISSN:
0167-9473
Subject:
data analysis; economics; models; prediction; statistics
Abstract:
... Most existing model averaging methods consider fully observed data while fragmentary data, in which not all the covariate data are available for many subjects, becomes more and more popular nowadays with the increasing data sources in many areas such as economics, social sciences and medical studies. The main challenge of model averaging in fragmentary data is that the samples to fit candidate mod ...
DOI:
10.1016/j.csda.2022.107497

17. Thresholding tests based on affine LASSO to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension

Author:
Sylvain Sardy; Jairo Diaz-Rodriguez; Caroline Giacobino
Source:
Computational statistics & data analysis 2022 v.173 pp. 107507
ISSN:
0167-9473
Subject:
data analysis; dimensions; statistics; testing; wills
Abstract:
... Thresholding estimators such as the existing square-root and LAD LASSO, and the new affine and GLM LASSO with new link functions, have the ability to set coefficients to zero. They will yield new pivotal statistics which enjoy high power under sparse or dense alternative hypotheses. Under a general formalism, thresholding tests not only recover existing tests such as Rao score test and Fisher nonp ...
DOI:
10.1016/j.csda.2022.107507

18. Varying-coefficient hidden Markov models with zero-effect regions

Author:
Hefei Liu; Xinyuan Song; Baoxue Zhang
Source:
Computational statistics & data analysis 2022 v.173 pp. 107482
ISSN:
0167-9473
Subject:
Alzheimer disease; Bayesian theory; Markov chain; algorithms; data analysis; data collection; statistical inference
Abstract:
... In psychological, social, behavioral, and medical studies, hidden Markov models (HMMs) have been extensively applied to the simultaneous modeling of longitudinal observations and the underlying dynamic transition process. However, the existing HMMs mainly focus on constant-coefficient HMMs. This study considers a varying-coefficient HMM, which enables simultaneous investigation of the dynamic cova ...
DOI:
10.1016/j.csda.2022.107482