C Y Wang,Ziding Feng
C Y Wang
Boosting is an important tool in classification methodology. It combines the performance of many weak classifiers to produce a powerful committee, and its validity can be explained by additive modeling and maximum likelihood. The method has...
Cristiano Varin,Claudia Czado
Cristiano Varin
Longitudinal data with binary and ordinal outcomes routinely appear in medical applications. Existing methods are typically designed to deal with short measurement series. In contrast, modern longitudinal data can result in large numbers of...
Bayesian ranking and selection methods using hierarchical mixture models in microarray studies [0.03%]
微阵列研究中使用分层混合模型的贝叶斯排序和选择方法
Hisashi Noma,Shigeyuki Matsui,Takashi Omori et al.
Hisashi Noma et al.
The main purpose of microarray studies is screening to identify differentially expressed genes as candidates for further investigation. Because of limited resources in this stage, prioritizing or ranking genes is a relevant statistical task...
The competing risks illness-death model under cross-sectional sampling [0.03%]
横断面采样下的竞争风险 illness-death 模型
Micha Mandel
Micha Mandel
The competing risks illness-death model describes the dynamics of healthy subjects who may move to an "illness" state before entering into one of several competing terminal states. A motivating example concerns patients in a hospital who ma...
Michael G Kenward,James H Roger
Michael G Kenward
It is our experience that in many settings, crossover trials that have within-period baseline measurements are analyzed wrongly. A "conventional" analysis of covariance in this setting uses each baseline as a covariate for the following out...
Varying-coefficient models for longitudinal processes with continuous-time informative dropout [0.03%]
具有连续时间信息性脱落的纵向过程的变系数模型
Li Su,Joseph W Hogan
Li Su
Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dr...
PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data [0.03%]
一种利用微阵列数据预测癌症中绝对等位基因拷贝数变异的算法：PICNIC算法
Chris D Greenman,Graham Bignell,Adam Butler et al.
Chris D Greenman et al.
High-throughput oligonucleotide microarrays are commonly employed to investigate genetic disease, including cancer. The algorithms employed to extract genotypes and copy number variation function optimally for diploid genomes usually associ...
Bayesian random-effects threshold regression with application to survival data with nonproportional hazards [0.03%]
具有非比例风险的生存数据的贝叶斯随机效应阈值回归及其应用
Michael L Pennell,G A Whitmore,Mei-Ling Ting Lee
Michael L Pennell
In epidemiological and clinical studies, time-to-event data often violate the assumptions of Cox regression due to the presence of time-dependent covariate effects and unmeasured risk factors. An alternative approach, which does not require...
Yik Y Teo
Yik Y Teo
Genome-wide association studies (GWAS) have become the method of choice for investigating the genetic basis of common diseases and complex traits. The immense scale of these experiments is unprecedented, involving thousands of samples and u...
The analysis of heterogeneous time trends in multivariate age-period-cohort models [0.03%]
多元年龄-时期-世代模型中异质性时间趋势的分析
Andrea Riebler,Leonhard Held
Andrea Riebler
Age-period-cohort (APC) models are frequently used to analyze mortality or morbidity rates stratified by age group and period. For the case in which rates are given in different strata, multivariate APC models have been considered only rece...