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Backgroud: Epistatic Miniarray Profiles (EMAP) enables the research of genetic interaction as an importan-t method to construct large-scale genetic interaction network. However, high proportion of mis...
Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
Dimension reduction Kernel smoothing Mann-Whitney statistic Missing out- comes Observational studies Selection bias
2016/1/25
The conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcome...
Backgroud: Epistatic Miniarray Profiles (EMAP) enables the research of genetic interaction as an importan-t method to construct large-scale genetic interaction network. However, high proportion of mis...
Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
Dimension reduction Kernel smoothing Mann-Whitney statistic
2016/1/20
The conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcome...
Nonparametric Regression with Discrete Covariate and Missing Values
Nonparametric Regression Discrete kernel smoothing Imputation Missing Values Variance Reduction
2016/1/19
We consider nonparametric regression with a mixture of continuous and discrete ex-planatory variables where realizations of the response variable may be missing. An impu-tation based nonparametric reg...
Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values
Convergence asymptotic normality variational Bayesian approximations exponential family models missing values
2012/9/19
We study the properties of variational Bayes approximations for exponential family mod-els with missing values. It is shown that the iterative algorithm for obtaining the varia-tional Bayesian estimat...
Missing values and sparse inverse covariance estimation
Missing values sparse inverse covariance estimation
2010/3/19
We propose an `1-regularized likelihood method for estimating the inverse
covariance matrix in the high-dimensional multivariate normal model
in presence of missing data. Our method is based on the ...
Empirical likelihood for estimating equations with missing values
Empirical likelihood estimating equations Kernel estimation missing values nonparametric imputation
2010/3/18
We consider an empirical likelihood inference for parameters defined
by general estimating equations when some components of the
random observations are subject to missingness. As the nature of the
...
Missing values：processing with the Kohonen algorithm
Missing values processing Kohonen algorithm
2010/4/26
Missing values：processing with the Kohonen algorithm。