Journal of Statistical Planning and Inference最新文献_第5页

Stable convergence of conditional least squares estimators for supercritical continuous state and continuous time branching processes with immigration 有移民的超临界连续状态和连续时间分支过程的条件最小二乘估计子的稳定收敛性

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-07-22 DOI: 10.1016/j.jspi.2024.106213

Mátyás Barczy

引用次数: 0

Some clustering-based change-point detection methods applicable to high dimension, low sample size data 一些适用于高维度、低样本量数据的基于聚类的变化点检测方法

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-07-16 DOI: 10.1016/j.jspi.2024.106212

Trisha Dawn , Angshuman Roy , Alokesh Manna , Anil K. Ghosh

引用次数: 0

Regression to the mean for overdispersed count data 过度分散计数数据的均值回归

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-07-05 DOI: 10.1016/j.jspi.2024.106211

Kiran Iftikhar , Manzoor Khan , Jake Olivier

{"title":"Regression to the mean for overdispersed count data","authors":"Kiran Iftikhar , Manzoor Khan , Jake Olivier","doi":"10.1016/j.jspi.2024.106211","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106211","url":null,"abstract":"<div><p>In repeated measurements, regression to the mean (RTM) is a tendency of subjects with observed extreme values to move closer to the mean when measured a second time. Not accounting for RTM could lead to incorrect decisions such as when observed natural variation is incorrectly attributed to the effect of a treatment/intervention. A strategy for addressing RTM is to decompose the <em>total effect</em>, the expected difference in paired random variables conditional on the first being in the tail of its distribution, into regression to the mean and unbiased treatment effects. The unbiased treatment effect can then be estimated by subtraction. Formulae are available in the literature to quantify RTM for Poisson distributed data which are constrained by mean–variance equivalence, although there are many real life examples of overdispersed count data that are not well approximated by the Poisson. The negative binomial can be considered an explicit overdispersed Poisson process where the Poisson intensity is chosen from a gamma distribution. In this study, the truncated bivariate negative binomial distribution is used to decompose the total effect formulae into RTM and treatment effects. Maximum likelihood estimators (MLE) and method of moments estimators are developed for the total, RTM, and treatment effects. A simulation study is carried out to investigate the properties of the estimators and compare them with those developed under the assumption of the Poisson process. Data on the incidence of dengue cases reported from 2007 to 2017 are used to estimate the total, RTM, and treatment effects.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"234 ","pages":"Article 106211"},"PeriodicalIF":0.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606665","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

Oracle-efficient estimation and global inferences for variance function of functional data 函数数据方差函数的 Oracle 高效估计和全局推断

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-07-04 DOI: 10.1016/j.jspi.2024.106210

Li Cai , Suojin Wang

{"title":"Oracle-efficient estimation and global inferences for variance function of functional data","authors":"Li Cai , Suojin Wang","doi":"10.1016/j.jspi.2024.106210","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106210","url":null,"abstract":"<div><p>A new two-step reconstruction-based moment estimator and an asymptotically correct smooth simultaneous confidence band as a global inference tool are proposed for the heteroscedastic variance function of dense functional data. Step one involves spline smoothing for individual trajectory reconstructions and step two employs kernel regression on the individual squared residuals to estimate each trajectory variability. Then by the method of moment an estimator for the variance function of functional data is constructed. The estimation procedure is innovative by synthesizing spline smoothing and kernel regression together, which allows one not only to apply the fast computing speed of spline regression but also to employ the flexible local estimation and the extreme value theory of kernel smoothing. The resulting estimator for the variance function is shown to be oracle-efficient in the sense that it is uniformly as efficient as the ideal estimator when all trajectories were known by “oracle”. As a result, an asymptotically correct simultaneous confidence band for the variance function is established. Simulation results support our asymptotic theory with fast computation. As an illustration, the proposed method is applied to the analyses of two real data sets leading to a number of discoveries.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"234 ","pages":"Article 106210"},"PeriodicalIF":0.8,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593789","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

Column expanded Latin hypercube designs 列扩展拉丁超立方设计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-06-27 DOI: 10.1016/j.jspi.2024.106208

Qiao Wei, Jian-Feng Yang, Min-Qian Liu

引用次数: 0

The impact of misclassification on covariate-adaptive randomized clinical trials with generalized linear models 误分类对使用广义线性模型的协变量自适应随机临床试验的影响

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-06-27 DOI: 10.1016/j.jspi.2024.106209

Tong Wang, Wei Ma

{"title":"The impact of misclassification on covariate-adaptive randomized clinical trials with generalized linear models","authors":"Tong Wang, Wei Ma","doi":"10.1016/j.jspi.2024.106209","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106209","url":null,"abstract":"<div><p>Covariate-adaptive randomization (CAR) is a type of randomization method that uses covariate information to enhance the comparability between different treatment groups. Under such randomization, the covariate is usually well balanced, i.e., the imbalance between the treatment group and placebo group is controlled. In practice, the covariate is sometimes misclassified. The covariate misclassification affects the CAR itself and statistical inferences after the CAR. In this paper, we examine the impact of covariate misclassification on CAR from two aspects. First, we study the balancing properties of CAR with unequal allocation in the presence of covariate misclassification. We show the convergence rate of the imbalance and compare it with that under true covariate. Second, we study the hypothesis test under CAR with misclassified covariates in a generalized linear model (GLM) framework. We consider both the unadjusted and adjusted models. To illustrate the theoretical results, we discuss the validity of test procedures for three commonly-used GLM, i.e., logistic regression, Poisson regression and exponential model. Specifically, we show that the adjusted model is often invalid when the misclassified covariates are adjusted. In this case, we provide a simple correction for the inflated Type-I error. The correction is useful and easy to implement because it does not require misclassification specification and estimation of the misclassification rate. Our study enriches the literature on the impact of covariate misclassification on CAR and provides a practical approach for handling misclassification.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"234 ","pages":"Article 106209"},"PeriodicalIF":0.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593759","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 zero-estimator approach for estimating the signal level in a high-dimensional model-free setting 在高维无模型环境中估计信号水平的零估计器方法

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-06-22 DOI: 10.1016/j.jspi.2024.106207

Ilan Livne, David Azriel, Yair Goldberg

引用次数: 0

Layer sparsity in neural networks 神经网络中的层稀疏性

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-06-09 DOI: 10.1016/j.jspi.2024.106195

Mohamed Hebiri , Johannes Lederer , Mahsa Taheri

引用次数: 0

High dimensional discriminant rules with shrinkage estimators of the covariance matrix and mean vector 使用协方差矩阵和均值向量收缩估计器的高维判别规则

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-06-08 DOI: 10.1016/j.jspi.2024.106199

Jaehoan Kim , Junyong Park , Hoyoung Park

{"title":"High dimensional discriminant rules with shrinkage estimators of the covariance matrix and mean vector","authors":"Jaehoan Kim , Junyong Park , Hoyoung Park","doi":"10.1016/j.jspi.2024.106199","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106199","url":null,"abstract":"<div><p>Linear discriminant analysis (LDA) is a typical method for classification problems with large dimensions and small samples. There are various types of LDA methods that are based on the different types of estimators for the covariance matrices and mean vectors. In this paper, we consider shrinkage methods based on a non-parametric approach. For the precision matrix, methods based on the sparsity structure or data splitting are examined. Regarding the estimation of mean vectors, Non-parametric Empirical Bayes (NPEB) methods and Non-parametric Maximum Likelihood Estimation (NPMLE) methods, also known as <span><math><mi>f</mi></math></span>-modeling and <span><math><mi>g</mi></math></span>-modeling, respectively, are adopted. The performance of linear discriminant rules based on combined estimation strategies of the covariance matrix and mean vectors are analyzed in this study. Particularly, the study presents a theoretical result on the performance of the NPEB method and compares it with previous studies. Simulation studies with various covariance matrices and mean vector structures are conducted to evaluate the methods discussed in this paper. Furthermore, real data examples such as gene expressions and EEG data are also presented.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"234 ","pages":"Article 106199"},"PeriodicalIF":0.9,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422982","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

Mixed-integer linear programming for computing optimal experimental designs 计算最佳实验设计的混合整数线性规划

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-06-06 DOI: 10.1016/j.jspi.2024.106200

Radoslav Harman, Samuel Rosa

引用次数: 0