Fractional Imputation in Survey Sampling: A Comparative Review [PDF]
, 2015 Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in survey sampling. In FI, several imputed values with their fractional weights are created for each missing item.
Kim, Jae Kwang +2 more
core +6 more sources
Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda. [PDF]
Int J Health Geogr, 2020 Introduction In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than 10 years
Thomson DR +3 more
europepmc +2 more sources
Survey sampling design in wave 1 of the Global Flourishing Study. [PDF]
Eur J EpidemiolThe Global Flourishing Study (GFS) is an international collaboration to develop a publicly accessible data resource to promote global research on human flourishing.
Padgett RN +8 more
europepmc +2 more sources
Developing a representative community health survey sampling frame using open-source remote satellite imagery in Mozambique. [PDF]
Int J Health Geogr, 2018 BackgroundLack of accurate data on the distribution of sub-national populations in low- and middle-income countries impairs planning, monitoring, and evaluation of interventions.
Wagenaar BH +13 more
europepmc +2 more sources
Statistical data integration in survey sampling: a review [PDF]
Japanese Journal of Statistics and Data Science, 2020 Finite population inference is a central goal in survey sampling. Probability sampling is the main statistical approach to finite population inference. Challenges arise due to high cost and increasing non-response rates.
Shu Yang, Jae Kwang Kim
semanticscholar +1 more source
Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey [PDF]
IEEE/CAA Journal of Automatica Sinica, 2021 Graph convolutional networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations.
Xin Liu +5 more
semanticscholar +1 more source
Diffusion Models: A Comprehensive Survey of Methods and Applications [PDF]
ACM Computing Surveys, 2022 Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design.
Ling Yang +8 more
semanticscholar +1 more source
Diffusion Models in Vision: A Survey [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and ...
Florinel-Alin Croitoru +3 more
semanticscholar +1 more source
Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles
Mathematics, 2022 Many surveys are performed using non-probability methods such as web surveys, social networks surveys, or opt-in panels. The estimates made from these data sources are usually biased and must be adjusted to make them representative of the target ...
María del Mar Rueda +2 more
doaj +1 more source
Estimating General Parameters from Non-Probability Surveys Using Propensity Score Adjustment
Mathematics, 2020 This study introduces a general framework on inference for a general parameter using nonprobability survey data when a probability sample with auxiliary variables, common to both samples, is available.
Luis Castro-Martín +2 more
doaj +1 more source