Computational Statistics & Data Analysis最新文献_第9页

An accurate computational approach for partial likelihood using Poisson-binomial distributions 用泊松二项分布计算部分似然的精确方法

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-02-24 DOI: 10.1016/j.csda.2025.108161

Youngjin Cho, Yili Hong, Pang Du

{"title":"An accurate computational approach for partial likelihood using Poisson-binomial distributions","authors":"Youngjin Cho, Yili Hong, Pang Du","doi":"10.1016/j.csda.2025.108161","DOIUrl":"10.1016/j.csda.2025.108161","url":null,"abstract":"<div><div>In a Cox model, the partial likelihood, as the product of a series of conditional probabilities, is used to estimate the regression coefficients. In practice, those conditional probabilities are approximated by risk score ratios based on a continuous time model, and thus result in parameter estimates from only an approximate partial likelihood. Through a revisit to the original partial likelihood idea, an accurate partial likelihood computing method for the Cox model is proposed, which calculates the exact conditional probability using the Poisson-binomial distribution. New estimating and inference procedures are developed, and theoretical results are established for the proposed computational procedure. Although ties are common in real studies, current theories for the Cox model mostly do not consider cases for tied data. In contrast, the new approach includes the theory for grouped data, which allows ties, and also includes the theory for continuous data without ties, providing a unified framework for computing partial likelihood for data with or without ties. Numerical results show that the proposed method outperforms current methods in reducing bias and mean squared error, while achieving improved confidence interval coverage rates, especially when there are many ties or when the variability in risk scores is large. Comparisons between methods in real applications have been made.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"208 ","pages":"Article 108161"},"PeriodicalIF":1.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Communication-efficient estimation and inference for high-dimensional longitudinal data 高维纵向数据的高效通信估计与推断

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-02-24 DOI: 10.1016/j.csda.2025.108154

Xing Li, Yanjing Peng, Lei Wang

引用次数: 0

Testing the constancy of the variance for time series with a trend 测试有趋势的时间序列方差的恒定性

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-02-21 DOI: 10.1016/j.csda.2025.108147

Lei Jin , Li Cai , Suojin Wang

{"title":"Testing the constancy of the variance for time series with a trend","authors":"Lei Jin , Li Cai , Suojin Wang","doi":"10.1016/j.csda.2025.108147","DOIUrl":"10.1016/j.csda.2025.108147","url":null,"abstract":"<div><div>The assumption of constant variance is fundamental in numerous statistical procedures for time series analysis. Nonlinear time series may exhibit time-varying local conditional variance, even when they are globally homoscedastic. Two novel tests are proposed to assess the constancy of variance in time series with a possible time-varying mean trend. Unlike previous approaches, the new tests rely on Walsh transformations of squared processes after recentering the time series data. It is shown that the corresponding Walsh coefficients have desirable properties, such as asymptotic independence. Both a max-type statistic and an order selection statistic are developed, along with their asymptotic null distributions. Furthermore, the consistency of the proposed statistics under a sequence of local alternatives is established. An extensive simulation study is conducted to examine the finite-sample performance of the procedures in comparison with existing methodologies. The empirical results show that the proposed methods are more powerful in many situations while maintaining reasonable Type I error rates, especially for nonlinear time series. The proposed methods are applied to test the global homoscedasticity of a financial time series, a well log time series with a non-constant mean structure, and a vibration time series.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"208 ","pages":"Article 108147"},"PeriodicalIF":1.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Exact designs for order-of-addition experiments under a transition-effect model 过渡效应模型下加阶实验的精确设计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-02-21 DOI: 10.1016/j.csda.2025.108162

Jiayi Zheng, Nicholas Rios

{"title":"Exact designs for order-of-addition experiments under a transition-effect model","authors":"Jiayi Zheng, Nicholas Rios","doi":"10.1016/j.csda.2025.108162","DOIUrl":"10.1016/j.csda.2025.108162","url":null,"abstract":"<div><div>In the chemical, pharmaceutical, and food industries, sometimes the order of adding a set of components has an impact on the final product. These are instances of the Order-of-Addition (OofA) problem, which aims to find the optimal sequence of the components. Extensive research on this topic has been conducted, but almost all designs are found by optimizing the <em>D</em>−optimality criterion. However, when prediction of the response is important, there is still a need for <em>I</em>−optimal designs. Furthermore, designs are needed for experiments where some orders are infeasible due to constraints. A new model for OofA experiments is presented that uses transition effects to model the effect of order on the response. Three algorithms are proposed to find <em>D</em>− and <em>I</em>−efficient exact designs under this new model: Simulated Annealing, a metaheuristic algorithm, Bubble Sorting, a greedy local optimization algorithm, and the Greedy Randomized Adaptive Search Procedure (GRASP), another metaheuristic algorithm. These three algorithms are generalized to handle block constraints, where components are grouped into blocks with a fixed order. Finally, two examples are shown to illustrate the effectiveness of the proposed designs and models, even under block constraints.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"208 ","pages":"Article 108162"},"PeriodicalIF":1.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Efficient sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm 基于分割经验贝叶斯ECM算法的高效稀疏高维线性回归

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-02-10 DOI: 10.1016/j.csda.2025.108146

Alexander C. McLain , Anja Zgodic , Howard Bondell

{"title":"Efficient sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm","authors":"Alexander C. McLain , Anja Zgodic , Howard Bondell","doi":"10.1016/j.csda.2025.108146","DOIUrl":"10.1016/j.csda.2025.108146","url":null,"abstract":"<div><div>Bayesian variable selection methods are powerful techniques for fitting sparse high-dimensional linear regression models. However, many are computationally intensive or require restrictive prior distributions on model parameters. A computationally efficient and powerful Bayesian approach is presented for sparse high-dimensional linear regression, requiring only minimal prior assumptions on parameters through plug-in empirical Bayes estimates of hyperparameters. The method employs a Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm to estimate maximum <em>a posteriori</em> (MAP) values of parameters via computationally efficient coordinate-wise optimization. The popular two-group approach to multiple testing motivates the E-step, resulting in a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm for sparse high-dimensional linear regression. Both one-at-a-time and all-at-once optimization can be used to complete PROBE. Extensive simulation studies and analyses of cancer cell drug responses are conducted to compare PROBE's empirical properties with those of related methods. Implementation is available through the R package <span>probe</span>.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"207 ","pages":"Article 108146"},"PeriodicalIF":1.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differentially private estimation of weighted average treatment effects for binary outcomes 二元结果加权平均治疗效果的差分私有估计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-02-10 DOI: 10.1016/j.csda.2025.108145

Sharmistha Guha , Jerome P. Reiter

引用次数: 0

Stratified distance space improves the efficiency of sequential samplers for approximate Bayesian computation 分层距离空间提高了序列采样器近似贝叶斯计算的效率

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-01-27 DOI: 10.1016/j.csda.2025.108141

Henri Pesonen , Jukka Corander

{"title":"Stratified distance space improves the efficiency of sequential samplers for approximate Bayesian computation","authors":"Henri Pesonen , Jukka Corander","doi":"10.1016/j.csda.2025.108141","DOIUrl":"10.1016/j.csda.2025.108141","url":null,"abstract":"<div><div>Approximate Bayesian computation (ABC) methods are standard tools for inferring parameters of complex models when the likelihood function is analytically intractable. A popular approach to improving the poor acceptance rate of the basic rejection sampling ABC algorithm is to use sequential Monte Carlo (ABC SMC) to produce a sequence of proposal distributions adapting towards the posterior, instead of generating values from the prior distribution of the model parameters. Proposal distribution for the subsequent iteration is typically obtained from a weighted set of samples, often called particles, of the current iteration of this sequence. Current methods for constructing these proposal distributions treat all the particles equivalently, regardless of the corresponding value generated by the sampler, which may lead to inefficiency when propagating the information across iterations of the algorithm. To improve sampler efficiency, a modified approach called stratified distance ABC SMC is introduced. The algorithm stratifies particles based on their distance between the corresponding synthetic and observed data, and then constructs distinct proposal distributions for all the strata. Taking into account the distribution of distances across the particle space leads to substantially improved acceptance rate of the rejection sampling. It is shown that further efficiency could be gained by using a newly proposed stopping rule for the sequential process based on the stratified posterior samples and these advances are demonstrated by several examples.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"207 ","pages":"Article 108141"},"PeriodicalIF":1.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Confidence intervals for tree-structured varying coefficients 树状结构变系数的置信区间

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-01-27 DOI: 10.1016/j.csda.2025.108142

Nikolai Spuck , Matthias Schmid , Malte Monin , Moritz Berger

引用次数: 0

Efficient computation of sparse and robust maximum association estimators 稀疏鲁棒最大关联估计的高效计算

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-01-26 DOI: 10.1016/j.csda.2025.108133

Pia Pfeiffer , Andreas Alfons , Peter Filzmoser

引用次数: 0

Functional time transformation model with applications to digital health 功能时间转换模型及其在数字健康中的应用

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-01-22 DOI: 10.1016/j.csda.2025.108131

Rahul Ghosal , Marcos Matabuena , Sujit K. Ghosh

{"title":"Functional time transformation model with applications to digital health","authors":"Rahul Ghosal , Marcos Matabuena , Sujit K. Ghosh","doi":"10.1016/j.csda.2025.108131","DOIUrl":"10.1016/j.csda.2025.108131","url":null,"abstract":"<div><div>The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the proportional hazard (PH) assumption might not always be suitable in real-world applications. Motivated by physiological signals encountered in digital medicine, we develop a more general and flexible functional time-transformation model for estimating the conditional survival function with both functional and scalar covariates. A partially functional regression model is used to directly model the survival time on the covariates through an unknown monotone transformation and a known error distribution. We use Bernstein polynomials to model the monotone transformation function and the smooth functional coefficients. A sieve method of maximum likelihood is employed for estimation. Numerical simulations illustrate a satisfactory performance of the proposed method in estimation and inference. We demonstrate the application of the proposed model through two case studies involving wearable data i) Understanding the association between diurnal physical activity pattern and all-cause mortality based on accelerometer data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014 and ii) Modelling Time-to-Hypoglycemia events in a cohort of diabetic patients based on distributional representation of continuous glucose monitoring (CGM) data. The results provide important epidemiological insights into the direct association between survival times and the physiological signals and also exhibit superior predictive performance compared to traditional summary-based biomarkers in the CGM study.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"207 ","pages":"Article 108131"},"PeriodicalIF":1.5,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0