Proceedings of the 4th International Conference on Statistics: Theory and Applications最新文献_第2页

Statistical Inference for Optimisation of Drug Delivery from Stents 优化支架给药的统计推断

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.138

L. Paun, André Fensterseifer Schmidt, S. McGinty, D. Husmeier

{"title":"Statistical Inference for Optimisation of Drug Delivery from Stents","authors":"L. Paun, André Fensterseifer Schmidt, S. McGinty, D. Husmeier","doi":"10.11159/icsta22.138","DOIUrl":"https://doi.org/10.11159/icsta22.138","url":null,"abstract":"The current study employs state-of-the-art optimisation methods for estimation of unknown parameters in a mathematical model of highly non-linear partial differential equations describing drug delivery from a drug-eluding stent. A classical optimisation scheme entails enormous run times due to the need to numerically solve the computationally expensive equations a large number of times to obtain the objective (black-box) function. We address this issue by employing an efficient global optimisation scheme, i.e. Bayesian optimisation (BO). This scheme aims to find the optimum of the black-box function by using an emulator of the original objective function to select the next query point (while balancing exploration and exploitation), and sequentially refining the emulator. Additionally, the proposed optimisation scheme is adapted to scenarios where there are hidden constraints in parameter space by incorporating a classifier that learns the infeasible parameter domains. We demonstrate that given a fixed number of expensive mathematical model evaluations, the proposed BO scheme outperforms state-of-the-art classical optimisation methods in terms of accuracy. present study focuses on applying state-of-the-art optimisation methods, namely BO in a drug-eluting stent application.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128424320","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: 1

Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model 基于马尔科夫随机场约束的高斯混合模型的心肌灌注分类

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.146

Yalei Yang, Hao Gao, C. Berry, A. Radjenovic, D. Husmeier

引用次数: 0

Effect of Monthly Mean Temperature on Accidental Mortality in the Elderly: A Time-Series Analysis in Tokyo, Kyoto, Sapporo, Japan 月平均气温对老年人意外死亡的影响:日本东京、京都、札幌的时间序列分析

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.141

M. Kanamori

引用次数: 0

Clustering and Multidimensional Scaling for Individual Difference Extraction 个体差异提取的聚类和多维尺度

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.162

M. Sato-Ilic

引用次数: 0

On Consistent Hypothesis Testing In General Hilbert Spaces 一般Hilbert空间中的一致假设检验

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.157

D. Gaigall

{"title":"On Consistent Hypothesis Testing In General Hilbert Spaces","authors":"D. Gaigall","doi":"10.11159/icsta22.157","DOIUrl":"https://doi.org/10.11159/icsta22.157","url":null,"abstract":"Extended Abstract Inference on the basis of high-dimensional and functional data are two topics which are discussed frequently in the current statistical literature. A possibility to include both topics in a single approach is working on a very general space for the underlying observations, such as a separable Hilbert space. We propose a general method for consistently hypothesis testing on the basis of random variables with values in separable Hilbert spaces. We avoid concerns with the curse of dimensionality due to a projection idea. We apply well-known test statistics from nonparametric inference to the projected data and integrate over all projections from a specific set and with respect to suitable probability measures. In contrast to classical methods, which are applicable for real-valued random variables or random vectors of dimensions lower than the sample size, the tests can be applied to random vectors of dimensions larger than the sample size or even to functional and high-dimensional data. In general, resampling procedures such as bootstrap or permutation are suitable to determine critical values. The idea can be extended to the case of incomplete observations. Moreover, we develop an efficient algorithm for implementing the method.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116976146","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

Unsupervised Classification of Categorical Time Series through Innovative Distances 基于创新距离的分类时间序列无监督分类

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.111

Ángel López-Oriona, J. A. Vilar, P. D’Urso

{"title":"Unsupervised Classification of Categorical Time Series through Innovative Distances","authors":"Ángel López-Oriona, J. A. Vilar, P. D’Urso","doi":"10.11159/icsta22.111","DOIUrl":"https://doi.org/10.11159/icsta22.111","url":null,"abstract":"- In this paper, two novel distances for nominal time series are introduced. Both of them are based on features describing the serial dependence patterns between each pair of categories. The first dissimilarity employs the so-called association measures, whereas the second computes correlation quantities between indicator processes whose uniqueness is guaranteed from standard stationary conditions. The metrics are used to construct crisp algorithms for clustering categorical series. The approaches are able to group series generated from similar underlying stochastic processes, achieve accurate results with series coming from a broad range of models and are computationally efficient. An extensive simulation study shows that the devised clustering algorithms outperform several alternative procedures proposed in the literature. Specifically, they achieve better results than approaches based on maximum likelihhod estimation, which take advantage of knowing the real underlying procedures. Both innovative dissimilarities could be useful for practitioners in the field of time series clustering.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131174411","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

Prior Dependence in L1-regularized Bayesian Regression l1正则贝叶斯回归的先验相关性

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.007

Chris Hans

{"title":"Prior Dependence in L1-regularized Bayesian Regression","authors":"Chris Hans","doi":"10.11159/icsta22.007","DOIUrl":"https://doi.org/10.11159/icsta22.007","url":null,"abstract":"The regularization of regression coefficients has become a central component of research in the statistical sciences due to its importance in applied data analysis in many other fields of science. From a Bayesian perspective, regularization is imposed naturally via prior distributions that probabilistically penalize large values of the coefficients. Research into prior distributions with connections to L1-norm penalization (e.g., “Bayesian lasso” and the “Bayesian elastic net”) has generated important insights about the nature of Bayesian penalized regression in practice. Though widely used, many such priors are restricted by the assumption that the regression coefficients are a priori independent. While independence may be reasonable in some data-analytic settings, having the ability to incorporate dependence in these prior distributions would allow for greater modeling flexibility. I describe a general class of “orthant normal” priors for regression coefficients that allows for prior dependence between regression coefficients. An interesting special case is an L1-regularized version of Zellner’s g prior. Though simulation-based posterior inference via Markov chain Monte Carlo methods is made difficult by an intractable function in the posterior density, I discuss computationally efficient methods for estimating this function that allow for full posterior inference about all model parameters.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126090852","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

Risk Measure Based on ARMA-TGARCH-GED-Copula Model 基于ARMA-TGARCH-GED-Copula模型的风险度量

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.130

Kun Wang, Wanrong Li

引用次数: 0

Jackknife Empirical Likelihood Methods for Testing the Distributional Symmetry 检验分布对称性的折刀经验似然方法

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.145

Brian Pidgeon, Yichuan Zhao

引用次数: 0

Phase Distributions of Complex Multitaper Transfer Function Estimates 复杂多锥度传递函数估计的相位分布

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.115

Skye Griffith, G. Takahara, Wesley S. Burr

{"title":"Phase Distributions of Complex Multitaper Transfer Function Estimates","authors":"Skye Griffith, G. Takahara, Wesley S. Burr","doi":"10.11159/icsta22.115","DOIUrl":"https://doi.org/10.11159/icsta22.115","url":null,"abstract":"– In time series analysis, trends are often studied via sequences of observations taken with respect to, as the name suggests, time. Time series regression models are a classic way of viewing a response time series as a function of one or multiple predictor time series. However, the default assumptions of these models fail to account for the data’s temporal trends. By instead building a complex regression model in the frequency domain, these assumptions can be relaxed, providing insight into what coherency is present in the model. The coefficient of the resulting complex regression model is known as a transfer function of frequency. There are several techniques used to estimate transfer functions, but all are subject to the bias-variance trade-off occurring as a byproduct of transformation to the frequency domain. Multitaper spectrum estimation has been shown to minimize spectral leakage (broad-band bias) while providing flexibility in terms of bandwidth selection (variance). Thus, exploration of Multitaper Transfer Function Estimators (MTFEs) is an alluring topic of research. Previous work has explored distribution theory for the modulus of MTFEs, in addition to MTFE variance across simulations, and has revealed effective methods of signal detection more robust to frequency modulation than classic alternatives. However, the distribution of an MTFE's phase has not been explored. This paper demonstrates that for models whose underlying noise is stationary and Gaussian, the MTFE at a given frequency is distributed as a complex Gaussian random variable. From the perspective of the time domain, one can infer parameters of the phase distribution of the MTFE by way of estimating the autocovariance function of the response. This phase distribution provides information which may be useful for signal detection.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122883201","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