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Estimation in semiparametric models with missing data
Copulas imputation kernel smoothing missing at random nuisance function partially linear model
2016/1/25
We propose a novel varying coefficient model, called princi-pal varying coefficient model (PVCM), by characterizing the varying coeffi-cients through linear combinations of a few principal functions. ...
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...
ROBUST METRIC STRUCTURE FROM MOTION FOR AN EXTENDED SEQUENCE WITH OUTLIERS AND MISSING DATA
structure from motion robust estimation projective reconstruction metric upgrade
2015/8/14
In this paper, we propose a robust metric structure from motion (SfM) algorithm for an extended sequence with outliers and missing
data. There are three main contributions in the proposed SfM algorit...
The Effect of Missing Data Treatment on Mantel-Haenszel DIF Detection
Missing Data Mantel-Haenszel DIF Detection
2015/7/2
Most implementations of the Mantel-Haenszel differential item functioning procedure
delete records with missing responses or replace missing responses with scores of 0. These
treatments of missing d...
Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification
cognitive function microsimulation misclassification panel data
2014/11/26
Background: A continuous-time three-state model can be used to describe change in cognitive function in the older population. State 1 corresponds to normal cognitive function, state 2 to cognitive imp...
Does the Missing Data Imputation Method Affect the Composition and Performance of Prognostic Models?
Data Multivariable imputation via chained equations Expectation maximum algorithm
2015/9/25
Background: We already showed the superiority of imputation of missing data (via Multivariable Imputation via Chained Equations (MICE) method) over exclusion of them; however, the methodology of MICE ...
Efficient EM Training of Gaussian Mixtures with Missing Data
Gaussian mixtures missing data EM algorithm imputation.
2012/11/23
In data-mining applications, we are frequently faced with a large fraction of missing entries in the data matrix, which is problematic for most discriminant machine learning algorithms. A solution tha...
Changepoint detection for high-dimensional time series with missing data
Change point detection high-dimensional time series missing data
2012/9/17
This paper describes a novel approach to changepoint detection when the observed high-dimensional data may have missing elements. The performance of classical methods for changepoint detection typical...
Simultaneous SNP identification in association studies with missing data
Hierarchical models Bayes models Gibbs sampling genome-wide association.
2012/9/18
Association testing aims to discover the underlying relationship between genotypes (usually Single Nucleotide Polymorphisms, or SNPs) and phenotypes(attributes, or traits). The typically large data se...
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...
Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases
missing data imputation statistics corrected for missing data item performance behavioral databases model goodness of fit
2011/3/22
This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, an...
Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases
missing data imputation statistics corrected for missing data item performance behavioral databases model goodness of fit
2011/3/23
This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, an...
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...