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...
Robust estimation using multivariate t innovations for vector autoregressive models via ECM algorithm [0.03%]
基于ECM算法的向量自回归模型的t分布残差 robust估计方法研究
Uchenna C Nduka,Tobias E Ugah,Chinyeaka H Izunobi
Uchenna C Nduka
This paper considers the vector autoregressive model of order p, VAR(p), with multivariate t error distributions, the latter being more prevalent in real life than the usual multivariate normal distribution. It is believed that the maximum-...
Jamil Ownuk,Hossein Baghishani,Ahmad Nezakati
Jamil Ownuk
While there has been considerable research on the analysis of extreme values and outliers by using heavy-tailed distributions, little is known about the semi-heavy-tailed behaviors of data when there are a few suspicious outliers. To addres...
Joint model for bivariate zero-inflated recurrent event data with terminal events [0.03%]
具有终止事件的二元零膨胀复发性事件数据联合模型
Yang-Jin Kim
Yang-Jin Kim
Bivariate recurrent event data are observed when subjects are at risk of experiencing two different type of recurrent events. In this paper, our interest is to suggest statistical model when there is a substantial portion of subjects not ex...
Analysis of interval-censored competing risks data under missing causes [0.03%]
缺失原因竞争风险区间审查数据的分析方法研究
Debanjan Mitra,Ujjwal Das,Kalyan Das
Debanjan Mitra
In this article, interval-censored competing risks data are analyzed when some of the causes of failure are missing. The vertical modeling approach has been proposed here. This approach utilizes the data to extract information to the maximu...
Navdeep Singh,Parminder Singh
Navdeep Singh
For a sequence of k independent normally distributed random samples from k populations with different means and variances, it is common to know which successive populations differ significantly with respect to a parameter. In this article, ...