Journal of Statistical Planning and Inference最新文献_第9页

Measures of conditional dependence for nonlinearity, asymmetry and beyond 非线性、不对称及其他条件依赖性的测量方法

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-03-16 DOI: 10.1016/j.jspi.2024.106165

Lianyan Fu , Luyang Zhang

{"title":"Measures of conditional dependence for nonlinearity, asymmetry and beyond","authors":"Lianyan Fu , Luyang Zhang","doi":"10.1016/j.jspi.2024.106165","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106165","url":null,"abstract":"<div><p>Detecting the correlation between two random variables is widely used in many empirical problems in economics. Among them, Pearson’s correlation can be used to quantify the degree of dependence between variables. However, it cannot handle asymmetric correlations. To deal with this situation, we proposed a pair of widely applicable measures of conditional dependence (<span><math><mrow><mi>M</mi><mi>C</mi><mi>D</mi><mi>s</mi></mrow></math></span>), which can not only account for the asymmetry but also the linear or nonlinear conditional dependencies in the presence of multiple variables. We give instances: when the paired measures are the same, resulting in symmetric correlation measures that are equivalent to the square of the Pearson coefficient; when no condition variables are given, <span><math><mrow><mi>M</mi><mi>C</mi><mi>D</mi><mi>s</mi></mrow></math></span> are used to assess the relationship between two variables. Consequently, Pearson’s correlation is a particular instance of <span><math><mrow><mi>M</mi><mi>C</mi><mi>D</mi><mi>s</mi></mrow></math></span>. Theoretical attributes of <span><math><mrow><mi>M</mi><mi>C</mi><mi>D</mi><mi>s</mi></mrow></math></span> show that they have wide applicability. In statistical inference, we develop the joint asymptotics of kernel-based estimators for <span><math><mrow><mi>M</mi><mi>C</mi><mi>D</mi><mi>s</mi></mrow></math></span>, which can be applied to determine whether two randomly generated variables exhibit symmetric conditional dependence in the presence of confounding variables. In the simulation, we verify the efficacy of the proposed <span><math><mrow><mi>M</mi><mi>C</mi><mi>D</mi><mi>s</mi></mrow></math></span>. Then we use real data to analyze the asymmetric impact of <span><math><mrow><mi>M</mi><mi>C</mi><mi>D</mi><mi>s</mi></mrow></math></span> on stock market movements.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"232 ","pages":"Article 106165"},"PeriodicalIF":0.9,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140163834","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

Multiple testing in genome-wide association studies via hierarchical hidden Markov models 通过分层隐马尔可夫模型在全基因组关联研究中进行多重测试

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-29 DOI: 10.1016/j.jspi.2024.106161

Pengfei Wang, Zhaofeng Tian

{"title":"Multiple testing in genome-wide association studies via hierarchical hidden Markov models","authors":"Pengfei Wang, Zhaofeng Tian","doi":"10.1016/j.jspi.2024.106161","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106161","url":null,"abstract":"<div><p>Problems of large-scale multiple testing are often encountered in modern scientific research. Conventional multiple testing procedures usually suffer considerable loss of testing efficiency when correlations among tests are ignored. In fact, appropriate use of correlation information not only enhances the efficacy of the testing procedure, but also improves the interpretability of the results. Since the disease- or trait-related single nucleotide polymorphisms (SNPs) tend to be clustered and exhibit serial correlations, hidden Markov model (HMM) based multiple testing procedures have been successfully applied in genome-wide association studies (GWAS). However, modeling the entire chromosome using a single HMM is somewhat rough. To overcome this issue, this paper employs the hierarchical hidden Markov model (HHMM) to describe local correlations among tests, and develops a multiple testing procedure that can automatically divide different class of chromosome regions, while taking into account local correlations among tests. We first propose an oracle procedure that is shown theoretically to be valid, and in fact optimal in some sense. We then develop a date-driven procedure to mimic the oracle version. Extensive simulations and a real data example show that the novel multiple testing procedure outperforms its competitors.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"232 ","pages":"Article 106161"},"PeriodicalIF":0.9,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140041559","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 for ultrahigh dimensional precision matrix estimation 超高维精确矩阵估算新方法

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-28 DOI: 10.1016/j.jspi.2024.106164

Wanfeng Liang , Yuhao Zhang , Jiyang Wang , Yue Wu , Xiaoyan Ma

引用次数: 0

Deep learning for ψ-weakly dependent processes ψ弱依赖过程的深度学习

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-28 DOI: 10.1016/j.jspi.2024.106163

William Kengne, Modou Wade

{"title":"Deep learning for ψ-weakly dependent processes","authors":"William Kengne, Modou Wade","doi":"10.1016/j.jspi.2024.106163","DOIUrl":"https://doi.org/10.1016/j.jspi.2024.106163","url":null,"abstract":"<div><p>In this paper, we perform deep neural networks for learning stationary <span><math><mi>ψ</mi></math></span>-weakly dependent processes. Such weak-dependence property includes a class of weak dependence conditions such as mixing, association<span><math><mrow><mo>⋯</mo><mspace></mspace></mrow></math></span> and the setting considered here covers many commonly used situations such as: regression estimation, time series prediction, time series classification<span><math><mrow><mo>⋯</mo><mspace></mspace></mrow></math></span> The consistency of the empirical risk minimization algorithm in the class of deep neural networks predictors is established. We achieve the generalization bound and obtain an asymptotic learning rate, which is less than <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mi>α</mi></mrow></msup><mo>)</mo></mrow></mrow></math></span>, for all <span><math><mrow><mi>α</mi><mo>></mo><mn>2</mn></mrow></math></span>. A bound of the excess risk, for a wide class of target functions, is also derived. Applications to binary time series classification and prediction in affine causal models with exogenous covariates are carried out. Some simulation results are provided, as well as an application to the US recession data.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"232 ","pages":"Article 106163"},"PeriodicalIF":0.9,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139999248","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

D4R: Doubly robust reduced rank regression in high dimension D4R：高维度下的双稳健缩减秩回归

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-27 DOI: 10.1016/j.jspi.2024.106162

Xiaoyan Ma , Lili Wei , Wanfeng Liang

引用次数: 0

On card guessing with two types of cards 用两种卡片猜牌

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-16 DOI: 10.1016/j.jspi.2024.106160

Markus Kuba , Alois Panholzer

{"title":"On card guessing with two types of cards","authors":"Markus Kuba , Alois Panholzer","doi":"10.1016/j.jspi.2024.106160","DOIUrl":"10.1016/j.jspi.2024.106160","url":null,"abstract":"<div><p>We consider a card guessing strategy for a stack of cards with two different types of cards, say <span><math><msub><mrow><mi>m</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> cards of type red (heart or diamond) and <span><math><msub><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> cards of type black (clubs or spades). Given a deck of <span><math><mrow><mi>M</mi><mo>=</mo><msub><mrow><mi>m</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>+</mo><msub><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> cards, we propose a refined counting of the number of correct colour guesses, when the guesser is provided with complete information, in other words, when the numbers <span><math><msub><mrow><mi>m</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> and <span><math><msub><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> and the colour of each drawn card are known. We decompose the correct guessed cards into three different types by taking into account the probability of making a correct guess, and provide joint distributional results for the underlying random variables as well as joint limit laws.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"232 ","pages":"Article 106160"},"PeriodicalIF":0.9,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139924736","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

Feature screening via concordance indices for left-truncated and right-censored survival data 通过左截断和右截断生存数据的一致性指数进行特征筛选

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-10 DOI: 10.1016/j.jspi.2024.106153

Li-Pang Chen

引用次数: 0

A new non-parametric estimation of the expected shortfall for dependent financial losses 对从属财务损失的预期缺口进行新的非参数估计

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-03 DOI: 10.1016/j.jspi.2024.106151

Khouzeima Moutanabbir , Mohammed Bouaddi

{"title":"A new non-parametric estimation of the expected shortfall for dependent financial losses","authors":"Khouzeima Moutanabbir , Mohammed Bouaddi","doi":"10.1016/j.jspi.2024.106151","DOIUrl":"10.1016/j.jspi.2024.106151","url":null,"abstract":"<div><p>In this paper, we address the problem of kernel estimation of the Expected Shortfall (ES) risk measure for financial losses that satisfy the <span><math><mi>α</mi></math></span>-mixing conditions. First, we introduce a new non-parametric estimator for the ES measure using a kernel estimation. Given that the ES measure is the sum of the Value-at-Risk and the mean-excess function, we provide an estimation of the ES as a sum of the estimators of these two components. Our new estimator has a closed-form expression that depends on the choice of the kernel smoothing function, and we derive these expressions in the case of Gaussian, Uniform, and Epanechnikov kernel functions. We study the asymptotic properties of this new estimator and compare it to the Scaillet estimator. Capitalizing on the properties of these two estimators, we combine them to create a new estimator for the ES which reduces the bias and lowers the mean square error. The combined estimator shows better stability with respect to the choice of the kernel smoothing parameter. Our findings are illustrated through some numerical examples that help us to assess the small sample properties of the different estimators considered in this paper.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"232 ","pages":"Article 106151"},"PeriodicalIF":0.9,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378375824000089/pdfft?md5=41ea07fd0d26fc2bbea00de05c1c0468&pid=1-s2.0-S0378375824000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139680115","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

Scale tests for a multilevel step-stress model with exponential lifetimes under Type-II censoring 第二类普查下指数寿命多级阶跃应力模型的规模检验

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-03 DOI: 10.1016/j.jspi.2024.106152

Maria Kateri, Nikolay I. Nikolov

引用次数: 0

Construction of high-dimensional high-separation distance designs 构建高维高分离距离设计

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-02-01 DOI: 10.1016/j.jspi.2024.106150

Xu He , Fasheng Sun

引用次数: 0