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

On the adaptive Lasso estimator of AR(p) time series with applications to INAR(p) and Hawkes processes 关于 AR（p）时间序列的自适应套索估计器及其在 INAR（p）和霍克斯过程中的应用

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-01-18 DOI: 10.1016/j.jspi.2024.106145

Daniela De Canditiis, Giovanni Luca Torrisi

{"title":"On the adaptive Lasso estimator of AR(p) time series with applications to INAR(p) and Hawkes processes","authors":"Daniela De Canditiis, Giovanni Luca Torrisi","doi":"10.1016/j.jspi.2024.106145","DOIUrl":"10.1016/j.jspi.2024.106145","url":null,"abstract":"<div><p>We investigate the consistency and the rate of convergence of the adaptive Lasso estimator for the parameters of linear AR(p) time series with a white noise which is a strictly stationary and ergodic martingale difference. Roughly speaking, we prove that <span><math><mrow><mo>(</mo><mi>i</mi><mo>)</mo></mrow></math></span> If the white noise has a finite second moment, then the adaptive Lasso estimator is almost sure consistent <span><math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math></span><span> If the white noise has a finite fourth moment, then the error estimate converges to zero with the same rate as the regularizing parameters of the adaptive Lasso estimator. Such theoretical findings are applied to estimate the parameters of INAR(p) time series and to estimate the fertility function of Hawkes processes. The results are validated by some numerical simulations, which show that the adaptive Lasso estimator allows for a better balancing between bias and variance with respect to the Conditional Least Square estimator and the classical Lasso estimator.</span></p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"231 ","pages":"Article 106145"},"PeriodicalIF":0.9,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139499638","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

Statistical inference for wavelet curve estimators of symmetric positive definite matrices 对称正定矩阵小波曲线估计器的统计推理

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-01-09 DOI: 10.1016/j.jspi.2023.106140

Daniel Rademacher , Johannes Krebs , Rainer von Sachs

引用次数: 0

Uniformly more powerful tests for a subset of the components of a Normal Mean Vector 对正态均值向量的子集成分进行统一的更强大测试

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-27 DOI: 10.1016/j.jspi.2023.106141

Yining Wang , Gang Li

引用次数: 0

Sparse multiple kernel learning: Minimax rates with random projection 稀疏多核学习：随机投影的最小率

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-27 DOI: 10.1016/j.jspi.2023.106142

Wenqi Lu , Zhongyi Zhu , Rui Li , Heng Lian

引用次数: 0

A new First-Order mixture integer-valued threshold autoregressive process based on binomial thinning and negative binomial thinning 基于二项稀疏化和负二项稀疏化的新一阶混合整数值阈值自回归过程

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-26 DOI: 10.1016/j.jspi.2023.106143

Danshu Sheng , Dehui Wang , Liuquan Sun

引用次数: 0

Designs for half-diallel experiments with commutative orthogonal block structure 采用换元正交块结构的半二列实验设计

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-21 DOI: 10.1016/j.jspi.2023.106139

R.A. Bailey, Peter J. Cameron, Dário Ferreira, Sandra S. Ferreira, Célia Nunes

引用次数: 0

Designs for half-diallel experiments with commutative orthogonal block structure 采用换元正交块结构的半二列实验设计

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-21 DOI: 10.1016/j.jspi.2023.106139

R.A. Bailey , Peter J. Cameron , Dário Ferreira , Sandra S. Ferreira , Célia Nunes

引用次数: 0

Kernel estimation of the transition density in bifurcating Markov chains 分叉马尔可夫链中过渡密度的核估计

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-20 DOI: 10.1016/j.jspi.2023.106138

S. Valère Bitseki Penda

引用次数: 0

Adaptively robust high-dimensional matrix factor analysis under Huber loss function 胡贝尔损失函数下的自适应鲁棒高维矩阵因子分析

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-20 DOI: 10.1016/j.jspi.2023.106137

Yinzhi Wang , Yingqiu Zhu , Qiang Sun , Lei Qin

{"title":"Adaptively robust high-dimensional matrix factor analysis under Huber loss function","authors":"Yinzhi Wang , Yingqiu Zhu , Qiang Sun , Lei Qin","doi":"10.1016/j.jspi.2023.106137","DOIUrl":"10.1016/j.jspi.2023.106137","url":null,"abstract":"<div><p>The explosion of data volume and the expansion in data dimensionality have led to a critical challenge in analyzing high-dimensional matrix time series for big data-related applications. In this regard, factor models for matrix-valued high-dimensional time series provide a powerful tool, that reduces the dimensionality of the variables with low-rank structures. However, existing high-dimensional matrix factor models suffer from two limitations in complex scenarios. One is that it is difficult to make robust inferences for datasets with heavy-tailed distributions. The other is that existing models require additional parameters for fine-tuning to guarantee performance. We propose an adaptively robust high-dimensional matrix factor model based on a specified Huber loss function to tackle the challenges mentioned above. An efficient iterative algorithm is provided to consistently determine the additional parameters of our model for robust estimation. The robustness of the model estimation is greatly improved by incorporating the Huber loss. Furthermore, we theoretically investigate the proposed method and derive the convergence rates of the robust estimators to examine its performance. Simulations show that the proposed method outperforms previous models in the estimation of heavy-tailed data. A real-world data analysis on a financial portfolio dataset illustrates that the method can be used to extract useful knowledge from high-dimensional matrix time series.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"231 ","pages":"Article 106137"},"PeriodicalIF":0.9,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138817793","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

Optimal subsampling for the Cox proportional hazards model with massive survival data 大量生存数据的考克斯比例危害模型的最佳子采样

IF 0.9 4区数学

Journal of Statistical Planning and Inference Pub Date : 2023-12-19 DOI: 10.1016/j.jspi.2023.106136

Nan Qiao , Wangcheng Li , Feng Xiao , Cunjie Lin

引用次数: 0