arXiv - STAT - Other Statistics最新文献_第5页

To democratize research with sensitive data, we should make synthetic data more accessible 为实现敏感数据研究的民主化，我们应该让合成数据更容易获取

arXiv - STAT - Other Statistics Pub Date : 2024-04-26 DOI: arxiv-2404.17271

Erik-Jan van Kesteren

引用次数: 0

An Investigation into Distance Measures in Cluster Analysis 对聚类分析中的距离测量方法的研究

arXiv - STAT - Other Statistics Pub Date : 2024-04-21 DOI: arxiv-2404.13664

Zoe Shapcott

引用次数: 0

Seasonal and Periodic Patterns of PM2.5 in Manhattan using the Variable Bandpass Periodic Block Bootstrap 使用可变带通周期块引导法分析曼哈顿 PM2.5 的季节性和周期性模式

arXiv - STAT - Other Statistics Pub Date : 2024-04-12 DOI: arxiv-2404.08738

Yanan Sun, Edward Valachovic

{"title":"Seasonal and Periodic Patterns of PM2.5 in Manhattan using the Variable Bandpass Periodic Block Bootstrap","authors":"Yanan Sun, Edward Valachovic","doi":"arxiv-2404.08738","DOIUrl":"https://doi.org/arxiv-2404.08738","url":null,"abstract":"Air quality is a critical component of environmental health. Monitoring and\u0000analysis of particulate matter with a diameter of 2.5 micrometers or smaller\u0000(PM2.5) plays a pivotal role in understanding air quality changes. This study\u0000focuses on the application of a new bandpass bootstrap approach, termed the\u0000Variable Bandpass Periodic Block Bootstrap (VBPBB), for analyzing time series\u0000data which provides modeled predictions of daily mean PM2.5 concentrations over\u000016 years in Manhattan, New York, the United States. The VBPBB can be used to\u0000explore periodically correlated (PC) principal components for this daily mean\u0000PM2.5 dataset. This method uses bandpass filters to isolate distinct PC\u0000components from datasets, removing unwanted interference including noise, and\u0000bootstraps the PC components. This preserves the PC structure and permits a\u0000better understanding of the periodic characteristics of time series data. The\u0000results of the VBPBB are compared against outcomes from alternative block\u0000bootstrapping techniques. The findings of this research indicate potential\u0000trends of elevated PM2.5 levels, providing evidence of significant semi-annual\u0000and weekly patterns missed by other methods.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-Parametric Estimation of Multiple Periodic Components in Turkey's Electricity Consumption 土耳其电力消费中多周期成分的非参数估计

arXiv - STAT - Other Statistics Pub Date : 2024-04-04 DOI: arxiv-2404.03786

Jie Yao, Edward Valachovic

{"title":"Non-Parametric Estimation of Multiple Periodic Components in Turkey's Electricity Consumption","authors":"Jie Yao, Edward Valachovic","doi":"arxiv-2404.03786","DOIUrl":"https://doi.org/arxiv-2404.03786","url":null,"abstract":"Electric generation and consumption are an essential component of\u0000contemporary living, influencing diverse facets of our daily routines,\u0000convenience, and economic progress. There is a high demand for characterizing\u0000the periodic pattern of electricity consumption. VBPBB employs a bandpass\u0000filter aligned to retain the frequency of a PC component and eliminating\u0000interference from other components. This leads to a significant reduction in\u0000the size of bootstrapped confidence intervals. Furthermore, other PC bootstrap\u0000methods preserve one but not multiple periodically correlated components,\u0000resulting in superior performance compared to other methods by providing a more\u0000precise estimation of the sampling distribution for the desired\u0000characteristics. The study of the periodic means of Turkey electricity\u0000consumption using VBPBB is presented and compared with outcomes from\u0000alternative bootstrapping approaches. These findings offer significant evidence\u0000supporting the existence of daily, weekly, and annual PC patterns, along with\u0000information on their timing and confidence intervals for their effects. This\u0000information is valuable for enhancing predictions and preparations for future\u0000responses to electricity consumption.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient estimation for a smoothing thin plate spline in a two-dimensional space 二维空间中平滑薄板样条线的高效估算

arXiv - STAT - Other Statistics Pub Date : 2024-04-02 DOI: arxiv-2404.01902

Joaquin Cavieres, Michael Karkulik

{"title":"Efficient estimation for a smoothing thin plate spline in a two-dimensional space","authors":"Joaquin Cavieres, Michael Karkulik","doi":"arxiv-2404.01902","DOIUrl":"https://doi.org/arxiv-2404.01902","url":null,"abstract":"Using a deterministic framework allows us to estimate a function with the\u0000purpose of interpolating data in spatial statistics. Radial basis functions are\u0000commonly used for scattered data interpolation in a d-dimensional space,\u0000however, interpolation problems have to deal with dense matrices. For the case\u0000of smoothing thin plate splines, we propose an efficient way to address this\u0000problem by compressing the dense matrix by an hierarchical matrix\u0000($mathcal{H}$-matrix) and using the conjugate gradient method to solve the\u0000linear system of equations. A simulation study was conducted to assess the\u0000effectiveness of the spatial interpolation method. The results indicated that\u0000employing an $mathcal{H}$-matrix along with the conjugate gradient method\u0000allows for efficient computations while maintaining a minimal error. We also\u0000provide a sensitivity analysis that covers a range of smoothing and compression\u0000parameter values, along with a Monte Carlo simulation aimed at quantifying\u0000uncertainty in the approximated function. Lastly, we present a comparative\u0000study between the proposed approach and thin plate regression using the \"mgcv\"\u0000package of the statistical software R. The comparison results demonstrate\u0000similar interpolation performance between the two methods.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization 利用连续优化实现线性降维模型的最佳子集求解路径

arXiv - STAT - Other Statistics Pub Date : 2024-03-29 DOI: arxiv-2403.20007

Benoit Liquet, Sarat Moka, Samuel Muller

{"title":"Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization","authors":"Benoit Liquet, Sarat Moka, Samuel Muller","doi":"arxiv-2403.20007","DOIUrl":"https://doi.org/arxiv-2403.20007","url":null,"abstract":"The selection of best variables is a challenging problem in supervised and\u0000unsupervised learning, especially in high dimensional contexts where the number\u0000of variables is usually much larger than the number of observations. In this\u0000paper, we focus on two multivariate statistical methods: principal components\u0000analysis and partial least squares. Both approaches are popular linear\u0000dimension-reduction methods with numerous applications in several fields\u0000including in genomics, biology, environmental science, and engineering. In\u0000particular, these approaches build principal components, new variables that are\u0000combinations of all the original variables. A main drawback of principal\u0000components is the difficulty to interpret them when the number of variables is\u0000large. To define principal components from the most relevant variables, we\u0000propose to cast the best subset solution path method into principal component\u0000analysis and partial least square frameworks. We offer a new alternative by\u0000exploiting a continuous optimization algorithm for best subset solution path.\u0000Empirical studies show the efficacy of our approach for providing the best\u0000subset solution path. The usage of our algorithm is further exposed through the\u0000analysis of two real datasets. The first dataset is analyzed using the\u0000principle component analysis while the analysis of the second dataset is based\u0000on partial least square framework.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Why Name Popularity is a Good Test of Historicity 为什么名字的流行性是历史性的良好检验标准？

arXiv - STAT - Other Statistics Pub Date : 2024-03-21 DOI: arxiv-2403.14883

Luuk van de Weghe, Jason Wilson

引用次数: 0

Risk Quadrangle and Robust Optimization Based on $varphi$-Divergence 基于$varphi$-发散的风险四边形和稳健优化

arXiv - STAT - Other Statistics Pub Date : 2024-03-16 DOI: arxiv-2403.10987

Cheng Peng, Anton Malandii, Stan Uryasev

引用次数: 0

Algorithmic syntactic causal identification 算法句法因果识别

arXiv - STAT - Other Statistics Pub Date : 2024-03-14 DOI: arxiv-2403.09580

Dhurim Cakiqi, Max A. Little

{"title":"Algorithmic syntactic causal identification","authors":"Dhurim Cakiqi, Max A. Little","doi":"arxiv-2403.09580","DOIUrl":"https://doi.org/arxiv-2403.09580","url":null,"abstract":"Causal identification in causal Bayes nets (CBNs) is an important tool in\u0000causal inference allowing the derivation of interventional distributions from\u0000observational distributions where this is possible in principle. However, most\u0000existing formulations of causal identification using techniques such as\u0000d-separation and do-calculus are expressed within the mathematical language of\u0000classical probability theory on CBNs. However, there are many causal settings\u0000where probability theory and hence current causal identification techniques are\u0000inapplicable such as relational databases, dataflow programs such as hardware\u0000description languages, distributed systems and most modern machine learning\u0000algorithms. We show that this restriction can be lifted by replacing the use of\u0000classical probability theory with the alternative axiomatic foundation of\u0000symmetric monoidal categories. In this alternative axiomatization, we show how\u0000an unambiguous and clean distinction can be drawn between the general syntax of\u0000causal models and any specific semantic implementation of that causal model.\u0000This allows a purely syntactic algorithmic description of general causal\u0000identification by a translation of recent formulations of the general ID\u0000algorithm through fixing. Our description is given entirely in terms of the\u0000non-parametric ADMG structure specifying a causal model and the algebraic\u0000signature of the corresponding monoidal category, to which a sequence of\u0000manipulations is then applied so as to arrive at a modified monoidal category\u0000in which the desired, purely syntactic interventional causal model, is\u0000obtained. We use this idea to derive purely syntactic analogues of classical\u0000back-door and front-door causal adjustment, and illustrate an application to a\u0000more complex causal model.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140150232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guidelines for the Creation of Analysis Ready Data 创建分析就绪数据指南

arXiv - STAT - Other Statistics Pub Date : 2024-03-12 DOI: arxiv-2403.08127

Harriette Phillips, Aiden Price, Owen Forbes, Claire Boulange, Kerrie Mengersen, Marketa Reeves, Rebecca Glauert

引用次数: 0