arXiv: Computation最新文献_第9页

The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data using MCMC R包JMbayes用于使用MCMC拟合纵向和时间到事件数据的联合模型

arXiv: Computation Pub Date : 2014-04-30 DOI: 10.18637/JSS.V072.I07

D. Rizopoulos

引用次数: 211

An Importance Sampling Algorithm for the Ising Model with Strong Couplings 强耦合Ising模型的重要采样算法

arXiv: Computation Pub Date : 2014-04-22 DOI: 10.3929/ETHZ-A-010645962

Mehdi Molkaraie

引用次数: 6

Fast Estimation of Multinomial Logit Models: R Package mnlogit 多项Logit模型的快速估计:R包

arXiv: Computation Pub Date : 2014-04-11 DOI: 10.18637/JSS.V075.I03

Asad Hasan, Wang Zhiyu, A. S. Mahani

引用次数: 43

Approximate Integrated Likelihood via ABC methods 通过ABC方法近似集成似然

arXiv: Computation Pub Date : 2014-03-03 DOI: 10.4310/SII.2015.V8.N2.A4

C. Grazian, B. Liseo

引用次数: 7

A sequential reduction method for inference in generalized linear mixed models 广义线性混合模型推理的序贯约简方法

arXiv: Computation Pub Date : 2013-12-06 DOI: 10.1214/15-EJS991

Helen E. Ogden

引用次数: 15

Detecting structural breaks in seasonal time series by regularized optimization 基于正则化优化的季节性时间序列结构断裂检测

arXiv: Computation Pub Date : 2013-12-01 DOI: 10.1201/b16387-524

B. Wang, Jie Sun, A. Motter

{"title":"Detecting structural breaks in seasonal time series by regularized optimization","authors":"B. Wang, Jie Sun, A. Motter","doi":"10.1201/b16387-524","DOIUrl":"https://doi.org/10.1201/b16387-524","url":null,"abstract":"Real-world systems are often complex, dynamic, and nonlinear. Understanding the dynamics of a system from its observed time series is key to the prediction and control of the system's behavior. While most existing techniques tacitly assume some form of stationarity or continuity, abrupt changes, which are often due to external disturbances or sudden changes in the intrinsic dynamics, are common in time series. Structural breaks, which are time points at which the statistical patterns of a time series change, pose considerable challenges to data analysis. Without identification of such break points, the same dynamic rule would be applied to the whole period of observation, whereas false identification of structural breaks may lead to overfitting. In this paper, we cast the problem of decomposing a time series into its trend and seasonal components as an optimization problem. This problem is ill-posed due to the arbitrariness in the number of parameters. To overcome this difficulty, we propose the addition of a penalty function (i.e., a regularization term) that accounts for the number of parameters. Our approach simultaneously identifies seasonality and trend without the need of iterations, and allows the reliable detection of structural breaks. The method is applied to recorded data on fish populations and sea surface temperature, where it detects structural breaks that would have been neglected otherwise. This suggests that our method can lead to a general approach for the monitoring, prediction, and prevention of structural changes in real systems.","PeriodicalId":8446,"journal":{"name":"arXiv: Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81530365","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: 6

Bayesian nonparametric inference on the Stiefel manifold Stiefel流形上的贝叶斯非参数推理

arXiv: Computation Pub Date : 2013-11-04 DOI: 10.5705/SS.202016.0017

Lizhen Lin, Vinayak A. Rao, D. Dunson

引用次数: 19

Least quantile regression via modern optimization 最小分位数回归通过现代优化

arXiv: Computation Pub Date : 2013-10-31 DOI: 10.1214/14-AOS1223

D. Bertsimas, R. Mazumder

{"title":"Least quantile regression via modern optimization","authors":"D. Bertsimas, R. Mazumder","doi":"10.1214/14-AOS1223","DOIUrl":"https://doi.org/10.1214/14-AOS1223","url":null,"abstract":"We address the Least Quantile of Squares (LQS) (and in particular the Least Median of Squares) regression problem using modern optimization methods. We propose a Mixed Integer Optimization (MIO) formulation of the LQS problem which allows us to find a provably global optimal solution for the LQS problem. Our MIO framework has the appealing characteristic that if we terminate the algorithm early, we obtain a solution with a guarantee on its sub-optimality. We also propose continuous optimization methods based on first-order subdifferential methods, sequential linear optimization and hybrid combinations of them to obtain near optimal solutions to the LQS problem. The MIO algorithm is found to benefit significantly from high quality solutions delivered by our continuous optimization based methods. We further show that the MIO approach leads to (a) an optimal solution for any dataset, where the data-points $(y_i,mathbf{x}_i)$'s are not necessarily in general position, (b) a simple proof of the breakdown point of the LQS objective value that holds for any dataset and (c) an extension to situations where there are polyhedral constraints on the regression coefficient vector. We report computational results with both synthetic and real-world datasets showing that the MIO algorithm with warm starts from the continuous optimization methods solve small ($n=100$) and medium ($n=500$) size problems to provable optimality in under two hours, and outperform all publicly available methods for large-scale ($n={}$10,000) LQS problems.","PeriodicalId":8446,"journal":{"name":"arXiv: Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83525446","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: 65

On the role of interaction in sequential Monte Carlo algorithms 论交互作用在顺序蒙特卡罗算法中的作用

arXiv: Computation Pub Date : 2013-09-11 DOI: 10.3150/14-BEJ666

N. Whiteley, Anthony Lee, K. Heine

引用次数: 66

Polynomial chaos based uncertainty quantification in Hamiltonian, multi-time scale, and chaotic systems 基于多项式混沌的哈密顿、多时间尺度和混沌系统的不确定性量化

arXiv: Computation Pub Date : 2013-06-29 DOI: 10.3934/JCD.2014.1.357

J. M. Pasini, T. Sahai

{"title":"Polynomial chaos based uncertainty quantification in Hamiltonian, multi-time scale, and chaotic systems","authors":"J. M. Pasini, T. Sahai","doi":"10.3934/JCD.2014.1.357","DOIUrl":"https://doi.org/10.3934/JCD.2014.1.357","url":null,"abstract":"Polynomial chaos is a powerful technique for propagating uncertainty through ordinary and partial differential equations. Random variables are expanded in terms of orthogonal polynomials and differential equations are derived for the expansion coefficients. Here we study the structure and dynamics of these differential equations when the original system has Hamiltonian structure, has multiple time scales, or displays chaotic dynamics. In particular, we prove that the differential equations for the expansion coefficients in generalized polynomial chaos expansions of Hamiltonian systems retain the Hamiltonian structure relative to the ensemble average Hamiltonian. We connect this with the volume-preserving property of Hamiltonian flows to show that, for an oscillator with uncertain frequency, a finite expansion must fail at long times, regardless of the order of the expansion. Also, using a two-time scale forced nonlinear oscillator, we show that a polynomial chaos expansion of the time-averaged equations captures uncertainty in the slow evolution of the Poincar'e section of the system and that, as the time scale separation increases, the computational advantage of this procedure increases. Finally, using the forced Duffing oscillator as an example, we demonstrate that when the original dynamical system displays chaotic dynamics, the resulting dynamical system from polynomial chaos also displays chaotic dynamics, limiting its applicability.","PeriodicalId":8446,"journal":{"name":"arXiv: Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74265115","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: 6