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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. ...
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...
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
Probability (math.PR) Statistics Theory (math.ST)
2010/12/17
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...
Missing Data：A Comparison of Neural Network and Expectation Maximisation Techniques
Missing Data Neural Network Expectation Maximisation Techniques
2010/4/28
Two techniques have emerged from the recent literature as candidate solutions to the problem
of missing data imputation, and these are the Expectation Maximisation (EM) Algorithm and the
auto-associ...
Deviance Information Criteria for Missing Data Models
completion deviance DIC EM algorithm MAP model comparison mixture model random effect model
2009/9/21
The deviance information criterion(DIC)introduced by Spiegelhalter et al.
(2002) for model assessment and model comparison is directly inspired by linear
and generalised linear models, but it is ope...
A Proposal to Improve the Patient Survey Focusing on the Recent Trend of Increase in the Missing Data
nonresponse number of patients Patient Survey ratio estimation stratified random sampling variable weighting
2009/3/9
The Patient Survey is a designated statistical survey conducted every three years with the objective of obtaining basic data on the current status of patients in medical institutions in Japan. One of ...