【摘 要】
：
We discuss the estimation issues for response adaptive clinical trials.The asymptotic efficiency of maximum likelihood estimators of the parameters and the treatment effect are developed.We also explo
【机 构】
：
Memorial University of Newfoundland Adjunct to University of Manitoba
【出 处】
：
The Third IMS-China International Conference on Statistics a
论文部分内容阅读
We discuss the estimation issues for response adaptive clinical trials.The asymptotic efficiency of maximum likelihood estimators of the parameters and the treatment effect are developed.We also explore the efficiency of estimation methods when sample sizes are small.
其他文献
We will discuss a general semi-parametric estimation strategy for analyses of longitudinal binary response data ascertained from outcome dependent sampling designs.The Sequential Offsetted Logistic Re
Gene-gene interactions have long been recognized to be fundamentally inportant to understand genetic causes of complex disease traits.At present, identifying gene-gene interactions from genome-wide ca
Genomic imprinting and maternal effects are two epigenetic factors that have been increasingly explored for their roles in the etiology of complex diseases.This is part of a concerted effort to find t
Feature extraction corresponds to reduced rank modeling which has been widespread real-world applications.This talk investigates non-asymptotic performance of rank penalized estimators.The constructio
Much of the recent statistics, biostatistics and econometric literature investigate conditional moment restrictions (or estimating equations) models in the presence of censored observations.Parametric
In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problem
We propose a novel sparse matrix graphical model for matrix-variate data.By penalizing the row and column precision matrices, our method gives sparse estimates of these matrices.The resulting estimate
Using nonparametric estimation method, this paper studies the dynamic evolution features of jumps in volatility of domestic stock market.We build the heterogeneous autoregressive model of realized vol
We propose an algorithm, called orthogonalizing EM (OEM), intended for fitting penalized regression models.It is motivated by the experimental design problem of fitting penalized regression with an or
We study the smoothed log-concave maximum likelihood estimator of a probability distribution in arbitrary dimensions.This is a fully automatic nonparametric density estimator, obtained as a canonical