Journal of Statistical Planning and Inference最新文献_第10页

Construction of 2fi-optimal row–column designs 构建 2fi- 最佳行列设计

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-05-21 DOI: 10.1016/j.jspi.2024.106192

Yingnan Zhang, Jiangmin Pan, Lei Shi

引用次数: 0

Beta regression misspecification tests 贝塔回归失当检验

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-05-21 DOI: 10.1016/j.jspi.2024.106193

Francisco Cribari-Neto, José Jairo Santana-e-Silva, Klaus L.P. Vasconcellos

引用次数: 0

Self-normalized inference for stationarity of irregular spatial data 不规则空间数据静止性的自归一化推论

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-05-15 DOI: 10.1016/j.jspi.2024.106191

Richeng Hu , Ngai-Hang Chan , Rongmao Zhang

引用次数: 0

Reduced-bias estimation of the extreme conditional tail expectation for Box–Cox transforms of heavy-tailed distributions 重尾分布的 Box-Cox 变换的极端条件尾期望的减偏差估计

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-05-10 DOI: 10.1016/j.jspi.2024.106189

Michaël Allouche , Jonathan El Methni , Stéphane Girard

{"title":"Reduced-bias estimation of the extreme conditional tail expectation for Box–Cox transforms of heavy-tailed distributions","authors":"Michaël Allouche , Jonathan El Methni , Stéphane Girard","doi":"10.1016/j.jspi.2024.106189","DOIUrl":"10.1016/j.jspi.2024.106189","url":null,"abstract":"<div><p>Conditional tail expectation (CTE) is a coherent risk measure defined as the mean of the loss distribution above a high quantile. The existence of the CTE as well as the asymptotic properties of associated estimators however require integrability conditions that may be violated when dealing with heavy-tailed distributions. We introduce Box–Cox transforms of the CTE that have two benefits. First, they alleviate these theoretical issues. Second, they enable to recover a number of risk measures such as conditional tail expectation, expected shortfall, conditional value-at-risk or conditional tail variance. The construction of dedicated estimators is based on the investigation of the asymptotic relationship between Box–Cox transforms of the CTE and quantiles at extreme probability levels, as well as on an extrapolation formula established in the heavy-tailed context. We quantify and estimate the bias induced by the use of these approximations and then introduce reduced-bias estimators whose asymptotic properties are rigorously shown. Their finite-sample properties are assessed on a simulation study and illustrated on real data, highlighting the practical interest of both the bias reduction and the Box–Cox transform.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"233 ","pages":"Article 106189"},"PeriodicalIF":0.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141035013","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

Efficient inference of parent-of-origin effect using case-control mother–child genotype data 利用病例对照母子基因型数据有效推断原生父母效应

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-05-09 DOI: 10.1016/j.jspi.2024.106190

Yuang Tian , Hong Zhang , Alexandre Bureau , Hagit Hochner , Jinbo Chen

{"title":"Efficient inference of parent-of-origin effect using case-control mother–child genotype data","authors":"Yuang Tian , Hong Zhang , Alexandre Bureau , Hagit Hochner , Jinbo Chen","doi":"10.1016/j.jspi.2024.106190","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106190","url":null,"abstract":"<div><p>Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother–child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect through a logistic regression model, with predictors including maternal and child genotypes, parental origins, and covariates. The parental origins may not be fully inferred from genotypes of a target genetic marker, so we propose to use genotypes of markers tightly linked to the target marker to increase inference efficiency. A robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation–maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-likelihood. This algorithm differs from the conventional expectation–maximization algorithm in the sense that it is based on a modified instead of the original profile log-likelihood function. The convergence of the algorithm is established under some mild regularity conditions. This expectation–maximization algorithm also allows convenient handling of missing child genotypes. Large sample properties, including weak consistency, asymptotic normality, and asymptotic efficiency, are established for the proposed estimator under some mild regularity conditions. Finite sample properties are evaluated through extensive simulation studies and the application to a real dataset.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"233 ","pages":"Article 106190"},"PeriodicalIF":0.9,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140950303","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 dynamic count process 动态计数过程

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-04-26 DOI: 10.1016/j.jspi.2024.106187

Namhyun Kim , Pipat Wongsa-art , Yingcun Xia

{"title":"A dynamic count process","authors":"Namhyun Kim , Pipat Wongsa-art , Yingcun Xia","doi":"10.1016/j.jspi.2024.106187","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106187","url":null,"abstract":"<div><p>The current paper aims to complement the recent development of the observation-driven models of dynamic counts with a parametric-driven one for a general case, particularly discrete two parameters exponential family distributions. The current paper proposes a finite semiparametric exponential mixture of SETAR processes of the conditional mean of counts to capture the nonlinearity and complexity. Because of the intrinsic latency of the conditional mean, the general additive state-space representation of dynamic counts is firstly proposed then stationarity and geometric ergodicity are established under a mild set of conditions. We also propose to estimate the unknown parameters by using quasi maximum likelihood estimation and establishes the asymptotic properties of the quasi maximum likelihood estimators (QMLEs), particularly <span><math><msqrt><mrow><mi>T</mi></mrow></msqrt></math></span>-consistency and normality under the relatively mild set of conditions. Furthermore, the finite sample properties of the QMLEs are investigated via simulation exercises and an illustration of the proposed process is presented by applying the proposed method to the intraday transaction counts per minute of AstraZeneca stock.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"233 ","pages":"Article 106187"},"PeriodicalIF":0.9,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140894991","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

Consistency of the maximum likelihood estimator of population tree in a coalescent framework 聚合框架中种群树最大似然估计的一致性

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-04-21 DOI: 10.1016/j.jspi.2024.106172

Arindam RoyChoudhury

引用次数: 0

Augmented projection Wasserstein distances: Multi-dimensional projection with neural surface 增强投影瓦瑟斯坦距离：带神经表面的多维投影

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-04-19 DOI: 10.1016/j.jspi.2024.106185

Miyu Sugimoto , Ryo Okano , Masaaki Imaizumi

{"title":"Augmented projection Wasserstein distances: Multi-dimensional projection with neural surface","authors":"Miyu Sugimoto , Ryo Okano , Masaaki Imaizumi","doi":"10.1016/j.jspi.2024.106185","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106185","url":null,"abstract":"<div><p>The Wasserstein distance is a fundamental tool for comparing probability distributions and has found broad applications in various fields, including image generation using generative adversarial networks. Despite its useful properties, the performance of the Wasserstein distance decreases when data is high-dimensional, known as the curse of dimensionality. To mitigate this issue, an extension of the Wasserstein distance has been developed, such as the sliced Wasserstein distance using one-dimensional projection. However, such an extension loses information on the original data, due to the linear projection onto the one-dimensional space. In this paper, we propose novel distances named augmented projection Wasserstein distances (APWDs) to address these issues, which utilize multi-dimensional projection with a nonlinear surface by a neural network. The APWDs employ a two-step procedure; it first maps data onto a nonlinear surface by a neural network, then linearly projects the mapped data into a multidimensional space. We also give an algorithm to select a subspace for the multi-dimensional projection. The APWDs are computationally effective while preserving nonlinear information of data. We theoretically confirm that the APWDs mitigate the curse of dimensionality from data. Our experiments demonstrate the APWDs’ outstanding performance and robustness to noise, particularly in the context of nonlinear high-dimensional data.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"233 ","pages":"Article 106185"},"PeriodicalIF":0.9,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378375824000429/pdfft?md5=d9eef2f8ec0fb76099ca4281dc2a0b63&pid=1-s2.0-S0378375824000429-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140632638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Distributed optimal subsampling for quantile regression with massive data 海量数据量化回归的分布式最优子采样

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-04-18 DOI: 10.1016/j.jspi.2024.106186

Yue Chao, Xuejun Ma, Boya Zhu

引用次数: 0

Entropic regularization of neural networks: Self-similar approximations 神经网络的熵正则化：自相似近似

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-04-16 DOI: 10.1016/j.jspi.2024.106181

Amir R. Asadi, Po-Ling Loh

引用次数: 0