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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...
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...
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...
In Kriging interpolation, the types of variogram model are very finite, which make the variogram very difficult to describe the spatial distributional characteristics of true data. In order to overcom...
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...
Weighted low-rank approximation (WLRA), a dimensionality reduction technique for data anal- ysis, has been successfully used in several applications, such as in collaborative filtering to design reco...
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.
In this paper we consider the problem of missing data in a time series analysis. We propose asymmetrical r = s winsorized mean to handle the problem of missing data. Beside that we suggested the Neym...
We consider samples with monotone missing data, drawn from a normal population to test if the covariance matrix is equal to a given positive definite matrix. We propose an imputation procedure for the...
In this paper we address the problem of maximizing the correlation between two vectors of time series data, when one of the vectors has missing data and the timing of the missing data is unknown. The ...

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