Computational Statistics最新文献_第3页

Semiparametric regression analysis of panel binary data with an informative observation process 具有信息观测过程的面板二元数据的半参数回归分析

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-29 DOI: 10.1007/s00180-024-01528-8

Lei Ge, Yang Li, Jianguo Sun

引用次数: 0

Profile transformations for reciprocal averaging and singular value decomposition 用于倒数平均和奇异值分解的轮廓变换

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-26 DOI: 10.1007/s00180-024-01517-x

Ting-Wu Wang, Eric J. Beh, Rosaria Lombardo, Ian W. Renner

{"title":"Profile transformations for reciprocal averaging and singular value decomposition","authors":"Ting-Wu Wang, Eric J. Beh, Rosaria Lombardo, Ian W. Renner","doi":"10.1007/s00180-024-01517-x","DOIUrl":"https://doi.org/10.1007/s00180-024-01517-x","url":null,"abstract":"Power transformations of count data, including cell frequencies of a contingency table, have been well understood for nearly 100 years, with much of the attention focused on the square root transformation. Over the past 15 years, this topic has been the focus of some new insights into areas of correspondence analysis where two forms of power transformation have been discussed. One type considers the impact of raising the joint proportions of the cell frequencies of a table to a known power while the other examines the power transformation of the relative distribution of the cell frequencies. While the foundations of the graphical features of correspondence analysis rest with the numerical algorithms like reciprocal averaging, and other analogous techniques, discussions of the role of power transformations in reciprocal averaging have not been described. Therefore, this paper examines this link where a power transformation is applied to the cell frequencies of a two-way contingency table. In doing so, we show that reciprocal averaging can be performed under such a transformation to obtain row and column scores that provide the maximum association between the variables and the greatest discrimination between the categories. Finally, we discuss the connection between performing reciprocal averaging and singular value decomposition under this type of power transformation. The R function, powerRA.exe is included in the Appendix and performs reciprocal averaging of a power transformation of the cell frequencies of a two-way contingency table.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772120","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

Positive time series regression models: theoretical and computational aspects 正时间序列回归模型：理论与计算方面

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-24 DOI: 10.1007/s00180-024-01531-z

Taiane Schaedler Prass, Guilherme Pumi, Cleiton Guollo Taufemback, Jonas Hendler Carlos

引用次数: 0

The root-Gaussian Cox Process for spatial-temporal disease mapping with aggregated data 根高斯考克斯过程（root-Gaussian Cox Process）：利用汇总数据绘制时空疾病图谱

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-18 DOI: 10.1007/s00180-024-01532-y

Zeytu Gashaw Asfaw, Patrick E. Brown, Jamie Stafford

引用次数: 0

Site-specific nitrogen recommendation: fast, accurate, and feasible Bayesian kriging 针对具体地点的氮推荐：快速、准确、可行的贝叶斯克里金法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-18 DOI: 10.1007/s00180-024-01527-9

Davood Poursina, B. Wade Brorsen

引用次数: 0

Computational econometrics with gretl 使用 Gretl 计算计量经济学

IF 1 4区数学

Computational Statistics Pub Date : 2024-07-13 DOI: 10.1007/s00180-024-01523-z

A. T. Yalta, Allin Cottrell, Paulo C. Rodrigues

引用次数: 0

Bayesian diagnostics in a partially linear model with first-order autoregressive skew-normal errors 具有一阶自回归偏态误差的部分线性模型的贝叶斯诊断法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-11 DOI: 10.1007/s00180-024-01504-2

Yonghui Liu, Jiawei Lu, Gilberto A. Paula, Shuangzhe Liu

引用次数: 0

Empirical likelihood change point detection in quantile regression models 量子回归模型中的经验似然变化点检测

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-10 DOI: 10.1007/s00180-024-01526-w

Suthakaran Ratnasingam, Ramadha D. Piyadi Gamage

引用次数: 0

Robust variable selection for additive coefficient models 加法系数模型的稳健变量选择

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-05 DOI: 10.1007/s00180-024-01524-y

Hang Zou, Xiaowen Huang, Yunlu Jiang

{"title":"Robust variable selection for additive coefficient models","authors":"Hang Zou, Xiaowen Huang, Yunlu Jiang","doi":"10.1007/s00180-024-01524-y","DOIUrl":"https://doi.org/10.1007/s00180-024-01524-y","url":null,"abstract":"Additive coefficient models generalize linear regression models by assuming that the relationship between the response and some covariates is linear, while their regression coefficients are additive functions. Because of its advantages in dealing with the “curse of dimensionality”, additive coefficient models gain a lot of attention. The commonly used estimation methods for additive coefficient models are not robust against high leverage points. To circumvent this difficulty, we develop a robust variable selection procedure based on the exponential squared loss function and group penalty for the additive coefficient models, which can tackle outliers in the response and covariates simultaneously. Under some regularity conditions, we show that the oracle estimator is a local solution of the proposed method. Furthermore, we apply the local linear approximation and minorization-maximization algorithm for the implementation of the proposed estimator. Meanwhile, we propose a data-driven procedure to select the tuning parameters. Simulation studies and an application to a plasma beta-carotene level data set illustrate that the proposed method can offer more reliable results than other existing methods in contamination schemes.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570110","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

A New Approach to Modeling the Cure Rate in the Presence of Interval Censored Data. 在存在区间剔除数据的情况下建立治愈率模型的新方法》（A New Approach to Modeling the Cure Rate in Presence of Interval Censored Data.

IF 1 4区数学

Computational Statistics Pub Date : 2024-07-01 Epub Date: 2023-07-15 DOI: 10.1007/s00180-023-01389-7

Suvra Pal, Yingwei Peng, Wisdom Aselisewine

{"title":"A New Approach to Modeling the Cure Rate in the Presence of Interval Censored Data.","authors":"Suvra Pal, Yingwei Peng, Wisdom Aselisewine","doi":"10.1007/s00180-023-01389-7","DOIUrl":"10.1007/s00180-023-01389-7","url":null,"abstract":"We consider interval censored data with a cured subgroup that arises from longitudinal followup studies with a heterogeneous population where a certain proportion of subjects is not susceptible to the event of interest. We propose a two component mixture cure model, where the first component describing the probability of cure is modeled by a support vector machine-based approach and the second component describing the survival distribution of the uncured group is modeled by a proportional hazard structure. Our proposed model provides flexibility in capturing complex effects of covariates on the probability of cure unlike the traditional models that rely on modeling the cure probability using a generalized linear model with a known link function. For the estimation of model parameters, we develop an expectation maximization-based estimation algorithm. We conduct simulation studies and show that our proposed model performs better in capturing complex effects of covariates on the cure probability when compared to the traditional logit link-based two component mixture cure model. This results in more accurate (smaller bias) and precise (smaller mean square error) estimates of the cure probabilities, which in-turn improves the predictive accuracy of the latent cured status. We further show that our model's ability to capture complex covariate effects also improves the estimation results corresponding to the survival distribution of the uncured. Finally, we apply the proposed model and estimation procedure to an interval censored data on smoking cessation.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11338591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79026133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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