Results 81 to 90 of about 762,527 (191)

Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study

Epidemiology, Biostatistics and Public Health, 2016
BackgroundThe purpose of this simulation study is to assess the performance of multiple imputation compared to complete case analysis when assumptions of missing data mechanisms are violated.MethodsThe authors performed a stochastic simulation study to ...
Sander MJ van Kuijk, Wolfgang Viechtbauer, Louis L Peeters, Luc Smits   +3 more
doaj   +1 more source

Assessing the effectiveness of a sthocastic regression imputation method for ordered categorical data [PDF]

, 2008
The main aim of this paper is to describe a workable method based on stochastic regression and multiple imputation analysis (MISR) to recover for missingness in surveys where multi-item Likert-type scale are used to measure a latent attribute (namely ...
Porcu, Mariano, Sulis , Isabella
core  

Modeling students' background and academic performance with missing values using classification tree [PDF]

, 2014
Student's academic performance is a prime concern to high level educational institution since it will react the performance of the institution. The difierences in academic performance among students are topics that has drawn interest of many academic ...
Hasan, Norsida
core  

A Label Propagation Approach for Missing Data Imputation

IEEE Access
Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random.
Filipe Loyola Lopes, Arthur Dantas Mangussi, Ricardo Cardoso Pereira, Miriam Seoane Santos, Pedro Henriques Abreu, Ana Carolina Lorena   +5 more
doaj   +1 more source

Estimating excess hazard ratios and net survival when covariate data are missing: strategies for multiple imputation.

, 2015
BACKGROUND: Net survival is the survival probability we would observe if the disease under study were the only cause of death. When estimated from routinely collected population-based cancer registry data, this indicator is a key metric for cancer ...
Carpenter, James R, Falcaro, Milena, Nur, Ula, Rachet, Bernard   +3 more
core   +1 more source

Optimal Transfer Learning for Missing Not-at-Random Matrix Completion

We study transfer learning for matrix completion in a Missing Not-at-Random (MNAR) setting that is motivated by biological problems. The target matrix $Q$ has entire rows and columns missing, making estimation impossible without side information. To address this, we use a noisy and incomplete source matrix $P$, which relates to $Q$ via a feature shift ...
Jalan, Akhil, Jedra, Yassir, Mazumdar, Arya, Mukherjee, Soumendu Sundar, Sarkar, Purnamrita   +4 more
openaire   +2 more sources

Imputation strategies for missing binary outcomes in cluster randomized trials

BMC Medical Research Methodology, 2011
Background Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized.
Akhtar-Danesh Noori, Ma Jinhui, Dolovich Lisa, Thabane Lehana   +3 more
doaj   +1 more source

Testing the Missing Completely at Random Assumption for Functional Data

We consider functional data which have only been observed on a subset of their domain. This paper aims to develop statistical tests to determine whether the function and the domain over which it is observed are independent. The assumption that data is missing completely at random (MCAR) is essential for many functional data methods handling incomplete ...
Ofner, Maximilian, Hörmann, Siegfried, Kraus, David, Liebl, Dominik   +3 more
openaire   +2 more sources

Model Averaging for Generalized Linear Model with Covariates that are Missing completely at Random

, 2017
In this paper, we consider the estimation of generalized linear models with covariates that are missing completely at random. We propose a model averaging estimation method and prove that the corresponding model averaging estimator is asymptotically optimal under certain assumptions. Simulaiton results illustrate that this method has better performance
Liu, Qingfeng, Zheng, Miaomiao
openaire   +2 more sources

Performance Comparison of Various Imputation Methods for Missing Data Mechanisms (MAR, MCAR, and MNAR) in a Nonstationary Time Series

International Journal of Mathematics and Mathematical Sciences
Handling missing data in a time series is necessary for forecasting as they can significantly impact representation and pose serious problems such as loss of efficiency and unreliable results.
Chantha Wongoutong
doaj   +1 more source