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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...
Stochastic Quantification of Missing Mechanisms in Dynamical Systems
ynamical Systems Missing Mechanisms
2015/4/3
Due to lack of knowledge:
Missing mechanisms (ignored “too small" or “too fast" or
“unimportant" processes)
Unresolved scales (not represented in models)
Model uncertainties
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...
An Improved Kriging Interpolation Technique Based on SVM and Its Recovery Experiment in Oceanic Missing Data
Least Square Support Vector Machine Kriging Interpolation Variogram SVM-Kriging
2013/1/30
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...
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...
Low-Rank Matrix Approximation with Weights or Missing Data is NP-hard
low-rank matrix approximation weighted low-rank approximation missing data
2011/1/17
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...
Limit theorems for bifurcating autoregressive processes with missing data
bifurcating autoregressive process missing data
2011/1/20
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.
Applications of operational calculus: Rectangular prisms and the estimation of a missing datum
Interpolation data replacement operational equations
2010/9/21
Operational equations can be used for the estimation of data missing from prismatic arrays. This paper continues the discussion of that subject. New interpolating equations for data in prismatic and r...
A "missing" family of classical orthogonal polynomials
missing family classical orthogonal polynomials
2010/11/12
We study a family of "classical" orthogonal polynomials which satisfy (apart from a 3-term recurrence relation) an eigenvalue problem with a differential operator of Dunkl-type. These polynomials can...
Stability of the Recovery of Missing Samples in Derivative Oversampling
frame Riesz basis shift-invariant space sampling formulas
2010/12/3
This paper deals with the problem of reconstructing a band-limited signal when a nite subset of its samples and of its derivative are missing. The technique used, due to P.J.S.G. Ferreira, is based o...
New method to estimate missing data by using the asymmetrical winsorized mean in a time series
Missing Data Winsorized mean Neyman Allocation
2010/9/14
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...
Testing a normal covariance matrix for small samples with monotone missing data
monotone missing data Bellman gamma distribution
2010/9/17
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...
Maximizing correlation in the presence of missing data
Integer programming combinatorial optimization genetic algorithm
2010/9/14
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 ...