The Robust EM-type Algorithms for Log-concave Mixtures of Regression Models [0.03%]
对回归模型的对数concavemixtures的鲁棒EM型算法
Hao Hu,Weixin Yao,Yichao Wu
Hao Hu
Finite mixture of regression (FMR) models can be reformulated as incomplete data problems and they can be estimated via the expectation-maximization (EM) algorithm. The main drawback is the strong parametric assumption such as FMR models wi...
Himel Mallick,Nengjun Yi
Himel Mallick
A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided in...
Ori Rosen,Wesley K Thompson
Ori Rosen
In this paper we present a model for the analysis of multivariate functional data with unequally spaced observation times that may differ among subjects. Our method is formulated as a Bayesian mixed-effects model in which the fixed part cor...
Informativeness of Diagnostic Marker Values and the Impact of Data Grouping [0.03%]
诊断标志物数值的显著性及数据分组的影响
Hua Ma,Andriy I Bandos,David Gur
Hua Ma
Assessing performance of diagnostic markers is a necessary step for their use in decision making regarding various conditions of interest in diagnostic medicine and other fields. Globally useful markers could, however, have ranges of values...
A parametric model to estimate the proportion from true null using a distribution for p-values [0.03%]
基于p值分布估计真正验假比例的参数模型
Chang Yu,Daniel Zelterman
Chang Yu
Microarray studies generate a large number of p-values from many gene expression comparisons. The estimate of the proportion of the p-values sampled from the null hypothesis draws broad interest. The two-component mixture model is often use...
S Faye Williamson,Peter Jacko,Sofía S Villar et al.
S Faye Williamson et al.
Development of treatments for rare diseases is challenging due to the limited number of patients available for participation. Learning about treatment effectiveness with a view to treat patients in the larger outside population, as in the t...
Zhongkai Liu,Rui Song,Donglin Zeng et al.
Zhongkai Liu et al.
Marginal screening has been established as a fast and effective method for high dimensional variable selection method. There are some drawbacks associated with marginal screening, since the marginal model can be viewed as a model misspecifi...
Shengtong Han,Hongmei Zhang,Wilfried Karmaus et al.
Shengtong Han et al.
Background noise in cluster analyses can potentially mask the true underlying patterns. To tease out patterns uniquely to certain populations, a Bayesian semi-parametric clustering method is presented. It infers and adjusts background noise...
Daniel Ahfock,Saumyadipta Pyne,Sharon X Lee et al.
Daniel Ahfock et al.
The statistical matching problem involves the integration of multiple datasets where some variables are not observed jointly. This missing data pattern leaves most statistical models unidentifiable. Statistical inference is still possible w...
Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits [0.03%]
具有异构稀疏性的正则化分位数回归及其在数量性状研究中的应用
Qianchuan He,Linglong Kong,Yanhua Wang et al.
Qianchuan He et al.
Genetic studies often involve quantitative traits. Identifying genetic features that influence quantitative traits can help to uncover the etiology of diseases. Quantile regression method considers the conditional quantiles of the response ...