Haruka Yamashita,Yoshinobu Kawahara
Haruka Yamashita
Analysis with principal points is a useful statistical tool for summarizing large data. In this paper, we propose a subgradient-based algorithm to calculate a set of principal points for multivariate binary data by the formulating it as a p...
Daniel Fernández,Ivy Liu,Roy Costilla et al.
Daniel Fernández et al.
Deciding on the best statistical method to apply when the response variable is ordinal is essential because the way the categories are ordered in the data is relevant as it could change the results of the analysis. Although the models for c...
Qiaohui Lin,Brenda Betancourt,Benjamin A Goldstein et al.
Qiaohui Lin et al.
Appointment no-shows have a negative impact on patient health and have caused substantial loss in resources and revenue for health care systems. Intervention strategies to reduce no-show rates can be more effective if targeted to the subpop...
Application of measurement error models to correct for systematic differences among readers and vendors in echocardiography measurements: the CARDIA study [0.03%]
计量错误模型在纠正超声心动图测量中读者和供应商之间的系统性差异中的应用：CARDIA研究
Aisha Betoko,Chike Nwabuo,Bharath Ambale Venkatesh et al.
Aisha Betoko et al.
We illustrate the application of linear measurement error models to calibrate echocardiography measurements acquired 20 years apart in the CARDIA study. Of 4242 echocardiograms acquired at Year-5 (1990-1991), 36% were reread 20 years later....
Samira F Abushilah,Charles C Taylor,Arief Gusnanto
Samira F Abushilah
Clustering amino acids is one of the most challenging problems in functional and structural prediction of protein. Previous studies have proposed clusters based on measurements of physical and biochemical characteristics of the amino acids ...
Guangbao Guo
Guangbao Guo
It is a major research topic of limited generalized linear models, namely, generalized linear models with limited dependent variables. The models are developed in many research fields. However, quasi-likelihood estimation of the models is a...
Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects [0.03%]
基于组学特征的肿瘤病理亚型预测模型研究
Özlem Kaymaz,Khaled Alqahtani,Henry M Wood et al.
Özlem Kaymaz et al.
The purpose of this study is to highlight the application of sparse logistic regression models in dealing with prediction of tumour pathological subtypes based on lung cancer patients' genomic information. We consider sparse logistic regres...
Bayesian bandwidth estimation and semi-metric selection for a functional partial linear model with unknown error density [0.03%]
未知误差密度下带宽的贝叶斯估计及半度量的选择在函数部分线性模型中的应用
Han Lin Shang
Han Lin Shang
This study examines the optimal selections of bandwidth and semi-metric for a functional partial linear model. Our proposed method begins by estimating the unknown error density using a kernel density estimator of residuals, where the regre...
Order restricted classical inference of a Weibull multiple step-stress model [0.03%]
有序限制的经典推断在Weibull多重步应力模型中的应用
Ayan Pal,Sharmishtha Mitra,Debasis Kundu
Ayan Pal
In this paper, a multiple step-stress model is designed and analyzed when the data are Type-I censored. Lifetime distributions of the experimental units at each stress level are assumed to follow a two-parameter Weibull distribution. Furthe...
A discrete analog of Gumbel distribution: properties, parameter estimation and applications [0.03%]
耿贝尔分布的离散相似分布：性质，参数估计及其应用
Subrata Chakraborty,Dhrubajyoti Chakravarty,Josmar Mazucheli et al.
Subrata Chakraborty et al.
A discrete version of the Gumbel distribution (Type-I Extreme Value distribution) has been derived by using the general approach of discretization of a continuous distribution. Important distributional and reliability properties have been e...