Quantile regression model for interval-censored data with competing risks [0.03%]
竞争风险下区间删失数据的分位数回归模型
Amirah Afiqah Binti Che Ramli,Yang-Jin Kim
Amirah Afiqah Binti Che Ramli
Our interest is to provide the methodology for estimating quantile regression model for interval-censored competing risk data. Lee and Kim [Analysis of interval censored competing risk data via nonparametric multiple imputation. Stat. Bioph...
Improving the within-node estimation of survival trees while retaining interpretability [0.03%]
改进生存树内的估计同时保持可解释性
Haolin Li,Yiyang Fan,Jianwen Cai
Haolin Li
In statistical learning for survival data, survival trees are favored for their capacity to detect complex relationships beyond parametric and semiparametric models. Despite this, their prediction accuracy is often suboptimal. In this paper...
Objective Bayesian trend filtering via adaptive piecewise polynomial regression [0.03%]
自适应分段多项式回归下的客观贝叶斯趋势滤波方法研究
Sang Gil Kang,Yongku Kim
Sang Gil Kang
Several methods have been developed for nonparametric regression problems, including classical approaches such as kernels, local polynomials, smoothing splines, sieves, and wavelets, as well as relatively new methods such as lasso, generali...
An empirical Bayes approach for constructing confidence intervals for clonality and entropy [0.03%]
构造克隆性和熵的置信区间的经验贝叶斯方法
Zhongren Chen,Lu Tian,Richard A Olshen
Zhongren Chen
This paper is motivated by the need to quantify human immune responses to environmental challenges. Specifically, the genome of the selected cell population from a blood sample is amplified by the PCR process, producing a large number of re...
Rob Cameron,Tianyu Guan,Haolun Shi et al.
Rob Cameron et al.
Penalized functional regression is a useful tool to estimate models for applications where the effect/coefficient function is assumed to be truncated. The truncated coefficient function occurs when the functional predictor does not influenc...
Yang Liu
Yang Liu
Overdispersion is a common phenomenon in genetic data, such as gene expression count data. In genetic association studies, it is important to investigate the association between a gene expression and a set of genetic variants from a pathway...
Shameem Alam,Javid Shabbir,Malaika Nadeem
Shameem Alam
Adaptive cluster sampling is particularly helpful whenever the target population is unique, dispersed unevenly, concealed or difficult to find. In the current investigation, under an adaptive cluster sampling approach, we propose a ratio-pr...
Parametric estimation of quantile versions of Zenga and D inequality curves: methodology and application to Weibull distribution [0.03%]
Zenga和D不平等曲线的分位数版本的参数估计：方法论及其在Weibull分布中的应用
Sylwester Pia̧tek
Sylwester Pia̧tek
Inequality (concentration) curves such as Lorenz, Bonferroni, Zenga curves, as well as a new inequality curve - the D curve, are broadly used to analyse inequalities in wealth and income distribution in certain populations. Quantile version...
On the improved estimation of the normal mixture components for longitudinal data [0.03%]
纵向数据正常混合成分估算改进的估计方法研究
Tapio Nummi,Jyrki Möttönen,Pasi Väkeväinen et al.
Tapio Nummi et al.
When analyzing real data sets, statisticians often face the question that the data are heterogeneous and it may not necessarily be possible to model this heterogeneity directly. One natural option in this case is to use the methods based on...
Halima S Twabi,Samuel O M Manda,Dylan S Small et al.
Halima S Twabi et al.
This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the pro...