Search results for "IMPUTATION"

Bayesian models for data missing not at random in health examination surveys

2018

In epidemiological surveys, data missing not at random (MNAR) due to survey nonresponse may potentially lead to a bias in the risk factor estimates. We propose an approach based on Bayesian data augmentation and survival modelling to reduce the nonresponse bias. The approach requires additional information based on follow-up data. We present a case study of smoking prevalence using FINRISK data collected between 1972 and 2007 with a follow-up to the end of 2012 and compare it to other commonly applied missing at random (MAR) imputation approaches. A simulation experiment is carried out to study the validity of the approaches. Our approach appears to reduce the nonresponse bias substantially…

Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved

2019

International audience; Millions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, k…

Systematic handling of missing data in complex study designs : experiences from the Health 2000 and 2011 Surveys

2016

We present a systematic approach to the practical and comprehensive handling of missing data motivated by our experiences of analyzing longitudinal survey data. We consider the Health 2000 and 2011 Surveys (BRIF8901) where increased non-response and non-participation from 2000 to 2011 was a major issue. The model assumptions involved in the complex sampling design, repeated measurements design, non-participation mechanisms and associations are presented graphically using methodology previously defined as a causal model with design, i.e. a functional causal model extended with the study design. This tool forces the statistician to make the study design and the missing-data mechanism explicit…

Structure and determinants of production in Textile-Clothing-Leather-Skins (TCLS) craft industry in Benin: a study based on investigations of the val…

2014

Lieu et date de la conférence modifiées. Prétoria 31Juillet au 2 Août 2014 (Au lieu de Kigali -Rwanda); International audience; Sustainable economic growth in Benin requires a better understand of the informalsector, which contributes to two-thirds of GDP. Particularly, craft industry and TCLSsubsector is one of informal activity sector to be handled. The objectives of this work wereto identify the structure and factors that determine the production in TCLS craft industry.This study was based on a survey carried out in February 2011 on value creationdata in craft industry of TCLS in Benin. Lack of data induced by informal activities wascircumvented using an imputation method. A Multiple Cor…

Analysis and evaluation of cell imputation

2008

CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS

2018

Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-for…

Factors Influencing Teachers’ Use of ICT in Class: Evidence from a Multilevel Logistic Model

2022

Information and Communication Technologies (ICTs) have become a key factor in the educational context, especially in the aftermath of the COVID-19 pandemic, and, correctly implemented, can help to improve academic performance. The aim of this research was to analyse the factors that influence teachers’ decisions to use ICT more- or less frequently to carry out tasks and exercises in their classes. To this end, we estimated a multilevel logistic model with census data from the individualized evaluation of students of the Community of Madrid (Spain) carried out at the end of the 2018–2019 academic year in primary and secondary education. Additionally, we applied multiple imputation techniques…