Results 31 to 40 of about 211,055 (241)

Multiple imputation methods for missing multilevel ordinal outcomes

BMC Medical Research Methodology, 2023
Background Multiple imputation (MI) is an established technique for handling missing data in observational studies. Joint modelling (JM) and fully conditional specification (FCS) are commonly used methods for imputing multilevel data. However, MI methods
Mei Dong, Aya Mitani
doaj   +1 more source

Econometrics: A bird's eye view [PDF]

, 2008
As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non ...
Geweke, J, Horowitz, JL, Pesaran, MH
core   +1 more source

Demystifying Emergence [PDF]

, 2016
Are the special sciences autonomous from physics? Those who say they are need to explain how dependent special science properties could feature in irreducible causal explanations, but that’s no easy task.
Yates, David
core   +1 more source

Statistical Inference in Missing Data by MCMC and Non-MCMC Multiple Imputation Algorithms: Assessing the Effects of Between-Imputation Iterations

Data Science Journal, 2017
Incomplete data are ubiquitous in social sciences; as a consequence, available data are inefficient (ineffective) and often biased. In the literature, multiple imputation is known to be the standard method to handle missing data.
Masayoshi Takahashi
doaj   +1 more source

Convex mixture regression for quantitative risk assessment [PDF]

, 2018
There is wide interest in studying how the distribution of a continuous response changes with a predictor. We are motivated by environmental applications in which the predictor is the dose of an exposure and the response is a health outcome. A main focus
Canale, Antonio, Dunson, David B, Durante, Daniele   +2 more
core   +2 more sources

Two-stage multiple imputation with a longitudinal composite variable. [PDF]

BMC Med Res Methodol
Background Missing data are common in longitudinal studies. Multiple imputation (MI) is widely used to handle missing data. However, most of the MI methods assume various missing data types as missing at random (MAR) in imputation.
Wang X, Larson MG, Liu C.
europepmc   +2 more sources

Predictors of perinatal death in the presence of missing data: A birth registry-based study in northern Tanzania. [PDF]

PLoS ONE, 2020
BackgroundMore than five million perinatal deaths occur each year globally. Despite efforts put forward during the millennium development goals era, perinatal deaths continue to increase relative to under-five deaths, especially in low- and middle-income
Innocent B Mboya, Michael J Mahande, Joseph Obure, Henry G Mwambi   +3 more
doaj   +1 more source

A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study

BMC Medical Research Methodology, 2017
Background Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection.
Anurika Priyanjali De Silva, Margarita Moreno-Betancur, Alysha Madhu De Livera, Katherine Jane Lee, Julie Anne Simpson   +4 more
doaj   +1 more source

Addressing health disparities using multiply imputed injury surveillance data

International Journal for Equity in Health, 2023
Background Assessing disparities in injury is crucial for injury prevention and for evaluating injury prevention strategies, but efforts have been hampered by missing data.
Yang Liu, Amy F. Wolkin, Marcie-jo Kresnow, Thomas Schroeder   +3 more
doaj   +1 more source