Foundations of data science (Springfield, Mo.)最新文献_第7页

EmT: Locating empty territories of homology group generators in a dataset EmT：在数据集中定位同源群生成器的空区域

Foundations of data science (Springfield, Mo.) Pub Date : 2019-06-03 DOI: 10.3934/FODS.2019010

Xin Xu, J. Cisewski-Kehe

{"title":"EmT: Locating empty territories of homology group generators in a dataset","authors":"Xin Xu, J. Cisewski-Kehe","doi":"10.3934/FODS.2019010","DOIUrl":"https://doi.org/10.3934/FODS.2019010","url":null,"abstract":"Persistent homology is a tool within topological data analysis to detect different dimensional holes in a dataset. The boundaries of the empty territories (i.e., holes) are not well-defined and each has multiple representations. The proposed method, Empty Territory (EmT), provides representations of different dimensional holes with a specified level of complexity of the territory boundary. EmT is designed for the setting where persistent homology uses a Vietoris-Rips complex filtration, and works as a post-analysis to refine the hole representation of the persistent homology algorithm. In particular, EmT uses alpha shapes to obtain a special class of representations that captures the empty territories with a complexity determined by the size of the alpha balls. With a fixed complexity, EmT returns the representation that contains the most points within the special class of representations. This method is limited to finding 1D holes in 2D data and 2D holes in 3D data, and is illustrated on simulation datasets of a homogeneous Poisson point process in 2D and a uniform sampling in 3D. Furthermore, the method is applied to a 2D cell tower location geography dataset and 3D Sloan Digital Sky Survey (SDSS) galaxy dataset, where it works well in capturing the empty territories.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42374169","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

Levels and trends in the sex ratio at birth and missing female births for 29 states and union territories in India 1990–2016: A Bayesian modeling study 1990-2016年印度29个邦和联邦属地出生性别比和失踪女婴的水平和趋势:贝叶斯模型研究

Foundations of data science (Springfield, Mo.) Pub Date : 2019-06-03 DOI: 10.3934/FODS.2019008

Fengqing Chao, A. Yadav

{"title":"Levels and trends in the sex ratio at birth and missing female births for 29 states and union territories in India 1990–2016: A Bayesian modeling study","authors":"Fengqing Chao, A. Yadav","doi":"10.3934/FODS.2019008","DOIUrl":"https://doi.org/10.3934/FODS.2019008","url":null,"abstract":"The sex ratio at birth (SRB) has risen in India and reaches well beyond the levels under normal circumstances since the 1970s. The lasting imbalanced SRB has resulted in much more males than females in India. A population with severely distorted sex ratio is more likely to have prolonged struggle for stability and sustainability. It is crucial to estimate SRB and its imbalance for India on state level and assess the uncertainty around estimates. We develop a Bayesian model to estimate SRB in India from 1990 to 2016 for 29 states and union territories. Our analyses are based on a comprehensive database on state-level SRB with data from the sample registration system, census and Demographic and Health Surveys. The SRB varies greatly across Indian states and union territories in 2016: ranging from 1.026 (95% uncertainty interval [0.971; 1.087]) in Mizoram to 1.181 [1.143; 1.128] in Haryana. We identify 18 states and union territories with imbalanced SRB during 1990–2016, resulting in 14.9 [13.2; 16.5] million of missing female births in India. Uttar Pradesh has the largest share of the missing female births among all states and union territories, taking up to 32.8% [29.5%; 36.3%] of the total number.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47172022","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: 9

Power weighted shortest paths for clustering Euclidean data 欧氏数据聚类的权加权最短路径

Foundations of data science (Springfield, Mo.) Pub Date : 2019-05-30 DOI: 10.3934/fods.2019014

Daniel Mckenzie, S. Damelin

引用次数: 16

General risk measures for robust machine learning 鲁棒机器学习的一般风险度量

Foundations of data science (Springfield, Mo.) Pub Date : 2019-04-26 DOI: 10.3934/fods.2019011

É. Chouzenoux, Henri G'erard, J. Pesquet

引用次数: 6

Estimation and uncertainty quantification for the output from quantum simulators 量子模拟器输出的估计与不确定性量化

Foundations of data science (Springfield, Mo.) Pub Date : 2019-03-07 DOI: 10.3934/FODS.2019007

R. Bennink, A. Jasra, K. Law, P. Lougovski

引用次数: 2

Approximate bayesian inference for geostatistical generalised linear models 地质统计广义线性模型的近似贝叶斯推断

Foundations of data science (Springfield, Mo.) Pub Date : 2019-03-07 DOI: 10.3934/FODS.2019002

E. Evangelou

引用次数: 0

Combinatorial Hodge theory for equitable kidney paired donation 公平配对肾脏捐献的组合Hodge理论

Foundations of data science (Springfield, Mo.) Pub Date : 2019-03-07 DOI: 10.3934/FODS.2019004

Joshua L. Mike, V. Maroulas

{"title":"Combinatorial Hodge theory for equitable kidney paired donation","authors":"Joshua L. Mike, V. Maroulas","doi":"10.3934/FODS.2019004","DOIUrl":"https://doi.org/10.3934/FODS.2019004","url":null,"abstract":"Kidney Paired Donation (KPD) is a system whereby incompatible patient-donor pairs (PD pairs) are entered into a pool to find compatible cyclic kidney exchanges where each pair gives and receives a kidney. The donation allocation decision problem for a KPD pool has traditionally been viewed within an economic theory and integer-programming framework. While previous allocation schema work well to donate the maximum number of kidneys at a specific time, certain subgroups of patients are rarely matched in such an exchange. Consequently, these methods lead to systematic inequity in the exchange, where many patients are rejected a kidney repeatedly. Our goal is to investigate inequity within the distribution of kidney allocation among patients, and to present an algorithm which minimizes allocation disparities. The method presented is inspired by cohomology and describes the cyclic structure in a kidney exchange efficiently; this structure is then used to search for an equitable kidney allocation. Another key result of our approach is a score function defined on PD pairs which measures cycle disparity within a KPD pool; i.e., this function measures the relative chance for each PD pair to take part in the kidney exchange if cycles are chosen uniformly. Specifically, we show that PD pairs with underdemanded donors or highly sensitized patients have lower scores than typical PD pairs. Furthermore, our results demonstrate that PD pair score and the chance to obtain a kidney are positively correlated when allocation is done by utility-optimal integer programming methods. In contrast, the chance to obtain a kidney through our method is independent of score, and thus unbiased in this regard.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44209556","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: 4

Particle filters for inference of high-dimensional multivariate stochastic volatility models with cross-leverage effects 具有交叉杠杆效应的高维多元随机波动模型的粒子滤波

Foundations of data science (Springfield, Mo.) Pub Date : 2019-02-25 DOI: 10.3934/fods.2019003

Yaxian Xu, A. Jasra

{"title":"Particle filters for inference of high-dimensional multivariate stochastic volatility models with cross-leverage effects","authors":"Yaxian Xu, A. Jasra","doi":"10.3934/fods.2019003","DOIUrl":"https://doi.org/10.3934/fods.2019003","url":null,"abstract":"Multivariate stochastic volatility models are a popular and well-known class of models in the analysis of financial time series because of their abilities to capture the important stylized facts of financial returns data. We consider the problems of filtering distribution estimation and also marginal likelihood calculation for multivariate stochastic volatility models with cross-leverage effects in the high dimensional case, that is when the number of financial time series that we analyze simultaneously (denoted by begin{document}$ d $end{document} ) is large. The standard particle filter has been widely used in the literature to solve these intractable inference problems. It has excellent performance in low to moderate dimensions, but collapses in the high dimensional case. In this article, two new and advanced particle filters proposed in [ 4 ], named the space-time particle filter and the marginal space-time particle filter, are explored for these estimation problems. The better performance in both the accuracy and stability for the two advanced particle filters are shown using simulation and empirical studies in comparison with the standard particle filter. In addition, Bayesian static model parameter estimation problem is considered with the advances in particle Markov chain Monte Carlo methods. The particle marginal Metropolis-Hastings algorithm is applied together with the likelihood estimates from the space-time particle filter to infer the static model parameter successfully when that using the likelihood estimates from the standard particle filter fails.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43334711","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: 5

Spectral methods to study the robustness of residual neural networks with infinite layers 用谱方法研究无穷层残差神经网络的鲁棒性

Foundations of data science (Springfield, Mo.) Pub Date : 2019-01-01 DOI: 10.3934/fods.2020012

T. Trimborn, Stephan Gerster, G. Visconti

引用次数: 4

Issues using logistic regression with class imbalance, with a case study from credit risk modelling 使用逻辑回归与阶级不平衡的问题，并以信用风险模型为例进行研究

Foundations of data science (Springfield, Mo.) Pub Date : 2019-01-01 DOI: 10.3934/fods.2019016

Yazhe Li, T. Bellotti, N. Adams

引用次数: 9