Computational Statistics & Data Analysis最新文献_第10页

An embedded diachronic sense change model with a case study from ancient Greek 以古希腊文为例的嵌入式非同步意义变化模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-06-21 DOI: 10.1016/j.csda.2024.108011

Schyan Zafar, Geoff K. Nicholls

{"title":"An embedded diachronic sense change model with a case study from ancient Greek","authors":"Schyan Zafar, Geoff K. Nicholls","doi":"10.1016/j.csda.2024.108011","DOIUrl":"https://doi.org/10.1016/j.csda.2024.108011","url":null,"abstract":"<div><p>Word meanings change over time, and word <em>senses</em> evolve, emerge or die out in the process. For ancient languages, where the corpora are often small and sparse, modelling such changes accurately proves challenging, and quantifying uncertainty in sense-change estimates consequently becomes important. GASC (Genre-Aware Semantic Change) and DiSC (Diachronic Sense Change) are existing generative models that have been used to analyse sense change for target words from an ancient Greek text corpus, using unsupervised learning without the help of any pre-training. These models represent the senses of a given target word such as “kosmos” (meaning decoration, order or world) as distributions over context words, and sense prevalence as a distribution over senses. The models are fitted using Markov Chain Monte Carlo (MCMC) methods to measure temporal changes in these representations. This paper introduces EDiSC, an Embedded DiSC model, which combines word embeddings with DiSC to provide superior model performance. It is shown empirically that EDiSC offers improved predictive accuracy, ground-truth recovery and uncertainty quantification, as well as better sampling efficiency and scalability properties with MCMC methods. The challenges of fitting these models are also discussed.</p></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"199 ","pages":"Article 108011"},"PeriodicalIF":1.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167947324000951/pdfft?md5=12930590074b9c3008e514576f2c4ba0&pid=1-s2.0-S0167947324000951-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A double Pólya-Gamma data augmentation scheme for a hierarchical Negative Binomial - Binomial data model 分层负二项-二项数据模型的双 Pólya-Gamma 数据扩充方案

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-06-20 DOI: 10.1016/j.csda.2024.108009

Xuan Ma, Jenný Brynjarsdóttir, Thomas LaFramboise

引用次数: 0

Strong orthogonal Latin hypercubes for computer experiments 用于计算机实验的强正交拉丁超立方体

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-06-20 DOI: 10.1016/j.csda.2024.107999

Chunyan Wang , Dennis K.J. Lin

引用次数: 0

Nonnegative GARCH-type models with conditional Gamma distributions and their applications 具有条件伽马分布的非负 GARCH 型模型及其应用

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-06-13 DOI: 10.1016/j.csda.2024.108006

Eunju Hwang, ChanHyeok Jeon

引用次数: 0

Conditional mean dimension reduction for tensor time series 张量时间序列的条件均值降维

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-06-11 DOI: 10.1016/j.csda.2024.107998

Chung Eun Lee , Xin Zhang

引用次数: 0

Study of imputation procedures for nonparametric density estimation based on missing censored lifetimes 基于缺失普查寿命的非参数密度估计的估算程序研究

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-06-06 DOI: 10.1016/j.csda.2024.107994

Sam Efromovich, Lirit Fuksman

引用次数: 0

Inference for high-dimensional linear expectile regression with de-biasing method 用去偏差法进行高维线性预期回归推断

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-06-06 DOI: 10.1016/j.csda.2024.107997

Xiang Li , Yu-Ning Li , Li-Xin Zhang , Jun Zhao

引用次数: 0

Latent event history models for quasi-reaction systems 准反应系统的潜在事件历史模型

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-05-31 DOI: 10.1016/j.csda.2024.107996

Matteo Framba , Veronica Vinciotti , Ernst C. Wit

{"title":"Latent event history models for quasi-reaction systems","authors":"Matteo Framba , Veronica Vinciotti , Ernst C. Wit","doi":"10.1016/j.csda.2024.107996","DOIUrl":"https://doi.org/10.1016/j.csda.2024.107996","url":null,"abstract":"<div><p>Various processes, such as cell differentiation and disease spreading, can be modelled as quasi-reaction systems of particles using stochastic differential equations. The existing Local Linear Approximation (LLA) method infers the parameters driving these systems from measurements of particle abundances over time. While dense observations of the process in time should in theory improve parameter estimation, LLA fails in these situations due to numerical instability. Defining a latent event history model of the underlying quasi-reaction system resolves this problem. A computationally efficient Expectation-Maximization algorithm is proposed for parameter estimation, incorporating an extended Kalman filter for evaluating the latent reactions. A simulation study demonstrates the method's performance and highlights the settings where it is particularly advantageous compared to the existing LLA approaches. An illustration of the method applied to the diffusion of COVID-19 in Italy is presented.</p></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"198 ","pages":"Article 107996"},"PeriodicalIF":1.8,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016794732400080X/pdfft?md5=524e7377774b8a5df2e3a994373e6394&pid=1-s2.0-S016794732400080X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141243341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Medoid splits for efficient random forests in metric spaces 度量空间中高效随机森林的 Medoid 分裂

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-05-31 DOI: 10.1016/j.csda.2024.107995

Matthieu Bulté , Helle Sørensen

引用次数: 0

Consistent skinny Gibbs in probit regression 概率回归中的一致瘦吉布斯

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-05-27 DOI: 10.1016/j.csda.2024.107993

Jiarong Ouyang, Xuan Cao

{"title":"Consistent skinny Gibbs in probit regression","authors":"Jiarong Ouyang, Xuan Cao","doi":"10.1016/j.csda.2024.107993","DOIUrl":"https://doi.org/10.1016/j.csda.2024.107993","url":null,"abstract":"<div><p>Spike and slab priors have emerged as effective and computationally scalable tools for Bayesian variable selection in high-dimensional linear regression. However, the crucial model selection consistency and efficient computational strategies using spike and slab priors in probit regression have rarely been investigated. A hierarchical probit model with continuous spike and slab priors over regression coefficients is considered, and a highly scalable Gibbs sampler with a computational complexity that grows only linearly in the dimension of predictors is proposed. Specifically, the “Skinny Gibbs” algorithm is adapted to the setting of probit and negative binomial regression and model selection consistency for the proposed method under probit model is established, when the number of covariates is allowed to grow much larger than the sample size. Through simulation studies, the method is shown to achieve superior empirical performance compared with other state-of-the art methods. Gene expression data from 51 asthmatic and 44 non-asthmatic samples are analyzed and the performance for predicting asthma using the proposed approach is compared with existing approaches.</p></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"198 ","pages":"Article 107993"},"PeriodicalIF":1.8,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141243339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 0