Statistics and Its Interface最新文献_第5页

Robust conditional spectral analysis of replicated time series 复制时间序列的鲁棒条件谱分析

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/21-sii698

Zeda Li

{"title":"Robust conditional spectral analysis of replicated time series","authors":"Zeda Li","doi":"10.4310/21-sii698","DOIUrl":"https://doi.org/10.4310/21-sii698","url":null,"abstract":"Classical second-order spectral analysis, which is based on the Fourier transform of the autocovariance functions, focuses on summarizing the oscillatory behaviors of a time series. However, this type of analysis is subject to two major limitations: ﬁrst, being covariance-based, it cannot captures oscillatory information beyond the second moment, such as time-irreversibility and kurtosis, and cannot accommodate heavy-tail dependence and inﬁnite variance; second, focusing on a single time series, it is unable to quantify the association between multiple time series and other covariates of interests. In this article, we propose a novel nonparametric approach to the spectral analysis of multiple time series and the associated covariates. The procedure is based on the copula spectral density kernel, which inherits the robust-ness properties of quantile regression and does not require any distributional assumptions such as the existence of ﬁnite moments. Copula spectral density kernels of diﬀerent pairs are modeled jointly as a matrix to allow ﬂexible smoothing. Through a tensor-product spline model of Cholesky components of the conditional copula spectral density matrix, the approach provides ﬂexible nonparametric estimates of the copula spectral density matrix as nonparametric functions of frequency and covariate while preserving geometric con-straints. Empirical performance is evaluated in simulation studies and illustrated through an analysis of stride interval time series.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71151106","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: 1

An iterative algorithm with adaptive weights and sparse Laplacian shrinkage for regression problems 回归问题的自适应加权和稀疏拉普拉斯收缩迭代算法

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii732

Xingyu Chen, Yuehan Yang

引用次数: 0

Two-stage multivariate dynamic linear models to extract environmental and climate signals in coastal ecosystem data 两阶段多元动态线性模型提取沿海生态系统数据中的环境和气候信号

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii731

J. Strock, G. Puggioni, S. Menden‐Deuer

引用次数: 0

Confidence in the treatment decision for an individual patient: strategies for sequential assessment. 对个体患者治疗决定的信心：顺序评估策略。

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 Epub Date: 2023-04-14 DOI: 10.4310/22-sii737

Nina Orwitz, Thaddeus Tarpey, Eva Petkova

{"title":"Confidence in the treatment decision for an individual patient: strategies for sequential assessment.","authors":"Nina Orwitz, Thaddeus Tarpey, Eva Petkova","doi":"10.4310/22-sii737","DOIUrl":"10.4310/22-sii737","url":null,"abstract":"<p><p>Evolving medical technologies have motivated the development of treatment decision rules (TDRs) that incorporate complex, costly data (e.g., imaging). In clinical practice, we aim for TDRs to be valuable by reducing unnecessary testing while still identifying the best possible treatment for a patient. Regardless of how well any TDR performs in the target population, there is an associated degree of uncertainty about its optimality for a specific patient. In this paper, we aim to quantify, via a confidence measure, the uncertainty in a TDR as patient data from sequential procedures accumulate in real-time. We first propose estimating confidence using the distance of a patient's vector of covariates to a treatment decision boundary, with further distances corresponding to higher certainty. We further propose measuring confidence through the conditional probabilities of ultimately (with all possible information available) being assigned a particular treatment, given that the same treatment is assigned with the patient's currently available data or given the treatment recommendation made using only the currently available patient data. As patient data accumulate, the treatment decision is updated and confidence reassessed until a sufficiently high confidence level is achieved. We present results from simulation studies and illustrate the methods using a motivating example from a depression clinical trial. Recommendations for practical use of the measures are proposed.</p>","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"16 3","pages":"475-491"},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238081/pdf/nihms-1895097.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9575344","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

Analyses of the impact of country specific macro risk variables on gold futures contract and its position as an asset class: evidence from India 特定国家宏观风险变量对黄金期货合约及其资产类别地位的影响分析:来自印度的证据

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/21-sii697

Rupel Nargunam, William W. S. Wei, N. Anuradha

引用次数: 0

Study of automatic choice of parameters for forecasting in singular spectrum analysis 奇异谱分析中预测参数的自动选择研究

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/21-sii707

Safia Al-Marhoobi, A. Pepelyshev

引用次数: 0

A pairwise pseudo-likelihood approach for the additive hazards model with left-truncated and interval-censored data 具有左截距和区间截距数据的加性风险模型的两两伪似然方法