In many applications,including disease early detection and prevention,and performance evaluation of airplanes and other durable products,we need to sequentially monitor the longitudinal pattern of cer
High-throughput biological data,such as microarray data and gene sequencing data,are plagued by unwanted variation – systematic errors introduced by variations in experimental conditions such as tempe
In oncology area,following a phase Ⅰ dose-finding trial completed in a certain population of patients,further phase Ⅰ trials are often conducted to determine the maximum tolerated dose(MTD)for differe
Covariate measurement error has attracted extensive interest in survival analysis.Since Prentice(1982),a large number of inference methods have been developed to handle error-contaminated data,and mos
In survival analysis,Accelerated Failure Time(AFT)models are very useful in modeling the relationship between failure times and associated covariates,where covariate effects are usually assumed to app
The classical optimal design methods have the capability of determining designs to achieve estimation or prediction efficiency in situations where the working model is correctly specified.
One key challenge with big-data is that it may not fit or cannot be processed in a single machine.A common approach to statistical learning in this setting is to randomly split the data among m machin