Results 241 to 250 of about 1,716,816 (278)

Missing value imputation on missing completely at random data using multilayer perceptrons

Neural Networks, 2011
Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of ...
Esther-Lydia, Silva-Ramírez, Rafael, Pino-Mejías, Manuel, López-Coello, María-Dolores, Cubiles-de-la-Vega   +3 more
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Quantile regression with covariates missing at random

Statistica Sinica, 2014
Regression quantiles can be underpowered or biased when there are miss- ing values in some covariates. We propose a method that produces consistent linear quantile estimation in the presence of missing covariates. The proposed method cor- rects bias by constructing unbiased estimating equations that simultaneously hold at all the quantile levels.
Ying Wei, Yunwen Yang
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Dimension reduction with missing response at random

Computational Statistics & Data Analysis, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guo, Xu, Wang, Tao, Xu, Wangli, Zhu, Lixing   +3 more
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Top-down attentionwith features missing at random

2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011
In this paper we present a top-down attention model designed for an environment in which features are missing completely at random. Following (Hansen et al., 2011) we model top-down attention as a sequential decision making process driven by a task - modeled as a classification problem - in an environment with random subsets of features missing, but ...
Seliz G. Karadogan, Letizia Marchegiani, Jan Larsen, Lars Kai Hansen   +3 more
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Rank‐based inference with responses missing not at random

Canadian Journal of Statistics, 2018
AbstractMissing data have become almost inevitable whenever data are collected. In this paper, interest is given to responses missing not at random in the context of regression modeling. Many of the existing methods for estimating the model parameters lack robustness or efficiency.
Huybrechts F. Bindele, Akim Adekpedjou
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Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions

Multivariate Behavioral Research, 2014
Mixture modeling is a popular method that accounts for unobserved population heterogeneity using multiple latent classes that differ in response patterns. Psychologists use conditional mixture models to incorporate covariates into between-class and/or within-class regressions.
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Nonparametric Regression With Predictors Missing at Random

Journal of the American Statistical Association, 2011
Nonparametric regression with predictors missing at random (MAR), where the probability of missing depends only on observed variables, is considered. Univariate predictor is the primary case of interest. A new adaptive orthogonal series estimator is developed.
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Nonparametric regression with responses missing at random

Journal of Statistical Planning and Inference, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Quantile difference estimation with censoring indicators missing at random

Lifetime Data Analysis
In this paper, we define estimators of distribution functions when the data are right-censored and the censoring indicators are missing at random, and establish their strong representations and asymptotic normality. Besides, based on empirical likelihood method, we define maximum empirical likelihood estimators and smoothed log-empirical likelihood ...
Kong, Cui-Juan, Liang, Han-Ying
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Identifying Variables Responsible for Data not Missing at Random

Psychometrika, 2009
When data are not missing at random (NMAR), maximum likelihood (ML) procedure will not generate consistent parameter estimates unless the missing data mechanism is correctly modeled. Understanding NMAR mechanism in a data set would allow one to better use the ML methodology.
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