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Estimation in semiparametric models with missing data
Copulas imputation kernel smoothing missing at random nuisance function partially linear model
2016/1/20
This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random.The semiparametric models allow for estimati...
Imputing Missing Data for Gene Expression Arrays
Imputing Missing Data Gene Expression Arrays
2015/8/21
Here we describe three different methods for imputation.The first is based on a reduced rank SVD of the expression matrix, the second is based on K-nearest neighbor averaging, and the third is based o...
Orthogonal Matching Pursuit with Noisy and Missing Data: Low and High Dimensional Results
Orthogonal Matching Pursuit Noisy and Missing Data High Dimensional Results Statistics Theory
2012/6/21
Many models for sparse regression typically assume that the covariates are known completely, and without noise. Particularly in high-dimensional applications, this is often not the case. This paper de...
Limit theorems for bifurcating autoregressive processes with missing data
Limit theorems bifurcating autoregressive processes missing data
2011/1/4
We study the asymptotic behavior of the least squares estimators of the unknown parameters of bifurcating autoregressive processes when some of the data are missing. We model the process of observed d...