Computational Statistics最新文献_第8页

Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models 有区别的统一化：推断组合状态空间（包括随机流行病模型）上马尔可夫链的新方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-26 DOI: 10.1007/s00180-024-01454-9

Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang

{"title":"Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models","authors":"Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang","doi":"10.1007/s00180-024-01454-9","DOIUrl":"https://doi.org/10.1007/s00180-024-01454-9","url":null,"abstract":"We consider continuous-time Markov chains that describe the stochastic evolution of a dynamical system by a transition-rate matrix Q which depends on a parameter (theta ). Computing the probability distribution over states at time t requires the matrix exponential (exp ,left( tQright) ,), and inferring (theta ) from data requires its derivative (partial exp ,left( tQright) ,/partial theta ). Both are challenging to compute when the state space and hence the size of Q is huge. This can happen when the state space consists of all combinations of the values of several interacting discrete variables. Often it is even impossible to store Q. However, when Q can be written as a sum of tensor products, computing (exp ,left( tQright) ,) becomes feasible by the uniformization method, which does not require explicit storage of Q. Here we provide an analogous algorithm for computing (partial exp ,left( tQright) ,/partial theta ), the differentiated uniformization method. We demonstrate our algorithm for the stochastic SIR model of epidemic spread, for which we show that Q can be written as a sum of tensor products. We estimate monthly infection and recovery rates during the first wave of the COVID-19 pandemic in Austria and quantify their uncertainty in a full Bayesian analysis. Implementation and data are available at https://github.com/spang-lab/TenSIR.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"74 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new approach to nonparametric estimation of multivariate spectral density function using basis expansion 利用基扩展对多元谱密度函数进行非参数估计的新方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-20 DOI: 10.1007/s00180-023-01451-4

Shirin Nezampour, Alireza Nematollahi, Robert T. Krafty, Mehdi Maadooliat

引用次数: 0

Censored broken adaptive ridge regression in high-dimension 高维度矢量破碎自适应脊回归

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-17 DOI: 10.1007/s00180-023-01446-1

Jeongjin Lee, Taehwa Choi, Sangbum Choi

{"title":"Censored broken adaptive ridge regression in high-dimension","authors":"Jeongjin Lee, Taehwa Choi, Sangbum Choi","doi":"10.1007/s00180-023-01446-1","DOIUrl":"https://doi.org/10.1007/s00180-023-01446-1","url":null,"abstract":"Broken adaptive ridge (BAR) is a penalized regression method that performs variable selection via a computationally scalable surrogate to (L_0) regularization. The BAR regression has many appealing features; it converges to selection with (L_0) penalties as a result of reweighting (L_2) penalties, and satisfies the oracle property with grouping effect for highly correlated covariates. In this paper, we investigate the BAR procedure for variable selection in a semiparametric accelerated failure time model with complex high-dimensional censored data. Coupled with Buckley-James-type responses, BAR-based variable selection procedures can be performed when event times are censored in complex ways, such as right-censored, left-censored, or double-censored. Our approach utilizes a two-stage cyclic coordinate descent algorithm to minimize the objective function by iteratively estimating the pseudo survival response and regression coefficients along the direction of coordinates. Under some weak regularity conditions, we establish both the oracle property and the grouping effect of the proposed BAR estimator. Numerical studies are conducted to investigate the finite-sample performance of the proposed algorithm and an application to real data is provided as a data example.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"262 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-dimensional penalized Bernstein support vector classifier 高维惩罚伯恩斯坦支持向量分类器

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-16 DOI: 10.1007/s00180-023-01448-z

Rachid Kharoubi, Abdallah Mkhadri, Karim Oualkacha

{"title":"High-dimensional penalized Bernstein support vector classifier","authors":"Rachid Kharoubi, Abdallah Mkhadri, Karim Oualkacha","doi":"10.1007/s00180-023-01448-z","DOIUrl":"https://doi.org/10.1007/s00180-023-01448-z","url":null,"abstract":"The support vector machine (SVM) is a powerful classifier used for binary classification to improve the prediction accuracy. However, the nondifferentiability of the SVM hinge loss function can lead to computational difficulties in high-dimensional settings. To overcome this problem, we rely on the Bernstein polynomial and propose a new smoothed version of the SVM hinge loss called the Bernstein support vector machine (BernSVC). This extension is suitable for the high dimension regime. As the BernSVC objective loss function is twice differentiable everywhere, we propose two efficient algorithms for computing the solution of the penalized BernSVC. The first algorithm is based on coordinate descent with the maximization-majorization principle and the second algorithm is the iterative reweighted least squares-type algorithm. Under standard assumptions, we derive a cone condition and a restricted strong convexity to establish an upper bound for the weighted lasso BernSVC estimator. By using a local linear approximation, we extend the latter result to the penalized BernSVC with nonconvex penalties SCAD and MCP. Our bound holds with high probability and achieves the so-called fast rate under mild conditions on the design matrix. Simulation studies are considered to illustrate the prediction accuracy of BernSVC relative to its competitors and also to compare the performance of the two algorithms in terms of computational timing and error estimation. The use of the proposed method is illustrated through analysis of three large-scale real data examples.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"262 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Random forest based quantile-oriented sensitivity analysis indices estimation 基于随机森林的面向量值的敏感性分析指数估算

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-12 DOI: 10.1007/s00180-023-01450-5

Kévin Elie-Dit-Cosaque, Véronique Maume-Deschamps

引用次数: 0

Structured dictionary learning of rating migration matrices for credit risk modeling 用于信用风险建模的评级迁移矩阵的结构化词典学习

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-10 DOI: 10.1007/s00180-023-01449-y

引用次数: 0

A latent variable approach for modeling recall-based time-to-event data with Weibull distribution 基于 Weibull 分布的事件时间回忆数据建模的潜在变量方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-03 DOI: 10.1007/s00180-023-01444-3

引用次数: 0

Testing for linearity in scalar-on-function regression with responses missing at random 测试随机缺失响应的标量-函数回归的线性度

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-03 DOI: 10.1007/s00180-023-01445-2

Manuel Febrero-Bande, Pedro Galeano, Eduardo García-Portugués, Wenceslao González-Manteiga

{"title":"Testing for linearity in scalar-on-function regression with responses missing at random","authors":"Manuel Febrero-Bande, Pedro Galeano, Eduardo García-Portugués, Wenceslao González-Manteiga","doi":"10.1007/s00180-023-01445-2","DOIUrl":"https://doi.org/10.1007/s00180-023-01445-2","url":null,"abstract":"A goodness-of-fit test for the Functional Linear Model with Scalar Response (FLMSR) with responses Missing at Random (MAR) is proposed in this paper. The test statistic relies on a marked empirical process indexed by the projected functional covariate and its distribution under the null hypothesis is calibrated using a wild bootstrap procedure. The computation and performance of the test rely on having an accurate estimator of the functional slope of the FLMSR when the sample has MAR responses. Three estimation methods based on the Functional Principal Components (FPCs) of the covariate are considered. First, the simplified method estimates the functional slope by simply discarding observations with missing responses. Second, the imputed method estimates the functional slope by imputing the missing responses using the simplified estimator. Third, the inverse probability weighted method incorporates the missing response generation mechanism when imputing. Furthermore, both cross-validation and LASSO regression are used to select the FPCs used by each estimator. Several Monte Carlo experiments are conducted to analyze the behavior of the testing procedure in combination with the functional slope estimators. Results indicate that estimators performing missing-response imputation achieve the highest power. The testing procedure is applied to check for linear dependence between the average number of sunny days per year and the mean curve of daily temperatures at weather stations in Spain.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"8 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139093938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation and prediction with data quality indexes in linear regressions 利用线性回归中的数据质量指标进行估计和预测

IF 1.3 4区数学

Computational Statistics Pub Date : 2023-12-20 DOI: 10.1007/s00180-023-01441-6

引用次数: 0

An extended Langevinized ensemble Kalman filter for non-Gaussian dynamic systems 用于非高斯动态系统的扩展朗格文集合卡尔曼滤波器

IF 1.3 4区数学

Computational Statistics Pub Date : 2023-12-14 DOI: 10.1007/s00180-023-01443-4

Peiyi Zhang, Tianning Dong, Faming Liang

引用次数: 0