Computational Statistics最新文献_第9页

Correction to: Sequential linear regression for conditional mean imputation of longitudinal continuous outcomes under reference‑based assumptions 更正：基于参照假设的序列线性回归用于纵向连续结果的条件平均估算

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-24 DOI: 10.1007/s00180-024-01467-4

Sean Yiu

引用次数: 0

Variational Bayesian Lasso for spline regression 用于样条回归的变异贝叶斯套索法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-24 DOI: 10.1007/s00180-024-01470-9

Larissa C. Alves, Ronaldo Dias, Helio S. Migon

引用次数: 0

Bayesian estimation of the number of species from Poisson-Lindley stochastic abundance model using non-informative priors 利用非信息先验从泊松-林德利随机丰度模型中对物种数量进行贝叶斯估计

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-23 DOI: 10.1007/s00180-024-01464-7

Anurag Pathak, Manoj Kumar, Sanjay Kumar Singh, Umesh Singh, Sandeep Kumar

引用次数: 0

Generation of normal distributions revisited 重新审视正态分布的生成

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-23 DOI: 10.1007/s00180-024-01468-3

Takayuki Umeda

引用次数: 0

Bayesian regression models in gretl: the BayTool package gretl 中的贝叶斯回归模型：BayTool 软件包

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-21 DOI: 10.1007/s00180-024-01466-5

Luca Pedini

引用次数: 0

Bayesian sequential probability ratio test for vaccine efficacy trials 疫苗效力试验的贝叶斯序列概率比检验

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-20 DOI: 10.1007/s00180-024-01458-5

Erina Paul, Santosh Sutradhar, Jonathan Hartzel, Devan V. Mehrotra

{"title":"Bayesian sequential probability ratio test for vaccine efficacy trials","authors":"Erina Paul, Santosh Sutradhar, Jonathan Hartzel, Devan V. Mehrotra","doi":"10.1007/s00180-024-01458-5","DOIUrl":"https://doi.org/10.1007/s00180-024-01458-5","url":null,"abstract":"Designing vaccine efficacy (VE) trials often requires recruiting large numbers of participants when the diseases of interest have a low incidence. When developing novel vaccines, such as for COVID-19 disease, the plausible range of VE is quite large at the design stage. Thus, the number of events needed to demonstrate efficacy above a pre-defined regulatory threshold can be difficult to predict and the time needed to accrue the necessary events can often be long. Therefore, it is advantageous to evaluate the efficacy at earlier interim analysis in the trial to potentially allow the trials to stop early for overwhelming VE or futility. In such cases, incorporating interim analyses through the use of the sequential probability ratio test (SPRT) can be helpful to allow for multiple analyses while controlling for both type-I and type-II errors. In this article, we propose a Bayesian SPRT for designing a vaccine trial for comparing a test vaccine with a control assuming two Poisson incidence rates. We provide guidance on how to choose the prior distribution and how to optimize the number of events for interim analyses to maximize the efficiency of the design. Through simulations, we demonstrate how the proposed Bayesian SPRT performs better when compared with the corresponding frequentist SPRT. An R repository to implement the proposed method is placed at: https://github.com/Merck/bayesiansprt.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139927751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Overlapping coefficient in network-based semi-supervised clustering 基于网络的半监督聚类中的重叠系数

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-19 DOI: 10.1007/s00180-024-01457-6

Claudio Conversano, Luca Frigau, Giulia Contu

{"title":"Overlapping coefficient in network-based semi-supervised clustering","authors":"Claudio Conversano, Luca Frigau, Giulia Contu","doi":"10.1007/s00180-024-01457-6","DOIUrl":"https://doi.org/10.1007/s00180-024-01457-6","url":null,"abstract":"Network-based Semi-Supervised Clustering (NeSSC) is a semi-supervised approach for clustering in the presence of an outcome variable. It uses a classification or regression model on resampled versions of the original data to produce a proximity matrix that indicates the magnitude of the similarity between pairs of observations measured with respect to the outcome. This matrix is transformed into a complex network on which a community detection algorithm is applied to search for underlying community structures which is a partition of the instances into highly homogeneous clusters to be evaluated in terms of the outcome. In this paper, we focus on the case the outcome variable to be used in NeSSC is numeric and propose an alternative selection criterion of the optimal partition based on a measure of overlapping between density curves as well as a penalization criterion which takes accounts for the number of clusters in a candidate partition. Next, we consider the performance of the proposed method for some artificial datasets and for 20 different real datasets and compare NeSSC with the other three popular methods of semi-supervised clustering with a numeric outcome. Results show that NeSSC with the overlapping criterion works particularly well when a reduced number of clusters are scattered localized.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139927826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

First exit and Dirichlet problem for the nonisotropic tempered $$alpha$$ -stable processes 非各向同性节制 $$alpha$$ 稳定过程的首次出口和德里赫特问题

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-15 DOI: 10.1007/s00180-024-01462-9

Xing Liu, Weihua Deng

引用次数: 0

Some new invariant sum tests and MAD tests for the assessment of Benford’s law 用于评估本福德定律的一些新的不变量总和检验和 MAD 检验

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-13 DOI: 10.1007/s00180-024-01463-8

Wolfgang Kössler, Hans-J. Lenz, Xing D. Wang

{"title":"Some new invariant sum tests and MAD tests for the assessment of Benford’s law","authors":"Wolfgang Kössler, Hans-J. Lenz, Xing D. Wang","doi":"10.1007/s00180-024-01463-8","DOIUrl":"https://doi.org/10.1007/s00180-024-01463-8","url":null,"abstract":"The Benford law is used world-wide for detecting non-conformance or data fraud of numerical data. It says that the significand of a data set from the universe is not uniformly, but logarithmically distributed. Especially, the first non-zero digit is One with an approximate probability of 0.3. There are several tests available for testing Benford, the best known are Pearson’s (chi ^2)-test, the Kolmogorov–Smirnov test and a modified version of the MAD-test. In the present paper we propose some tests, three of the four invariant sum tests are new and they are motivated by the sum invariance property of the Benford law. Two distance measures are investigated, Euclidean and Mahalanobis distance of the standardized sums to the orign. We use the significands corresponding to the first significant digit as well as the second significant digit, respectively. Moreover, we suggest inproved versions of the MAD-test and obtain critical values that are independent of the sample sizes. For illustration the tests are applied to specifically selected data sets where prior knowledge is available about being or not being Benford. Furthermore we discuss the role of truncation of distributions.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Convergence of the CUSUM estimation for a mean shift in linear processes with random coefficients 具有随机系数的线性过程中均值移动的 CUSUM 估计的收敛性

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-12 DOI: 10.1007/s00180-024-01465-6

Yi Wu, Wei Wang, Xuejun Wang

引用次数: 0