Results 71 to 80 of about 762,527 (191)

Semiparametric inference of competing risks data with additive hazards and missing cause of failure under MCAR or MAR assumptions [PDF]

, 2014
International audienceIn this paper, we consider a semiparametric model for lifetime data with competing risks and missing causes of death. We assume that an additive hazards model holds for each cause-specific hazard rate function and that a random ...
Bordes, Laurent, Dauxois, Jean-Yves, Joly, Pierre   +2 more
core   +3 more sources

Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.

PLoS ONE, 2022
A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data.
Adil Aslam Mir, Kimberlee Jane Kearfott, Fatih Vehbi Çelebi, Muhammad Rafique   +3 more
doaj   +1 more source

Asymptotically efficient product-limit estimators with censoring indicators missing at random [PDF]

, 2008
In this paper, we develop methods for estimating a survival function with censoring indicators missing at random. The resulting methods lead to the use of imputation and inverse probability weighting.
Ng, KW, Wang, Q
core  

Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches

Health and Quality of Life Outcomes, 2009
Background Missing data is classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Knowing the mechanism is useful in identifying the most appropriate analysis.
Fayers Peter M, Fielding Shona, Ramsay Craig R   +2 more
doaj   +1 more source

Univariate and multivariate imputation methods evaluation for reconstructing climate time series data: A case study of Mosul station-Iraq

Journal of Agrometeorology
Comprehensive climate time series data is indispensable for monitoring the impacts of climate change. However, observational datasets often suffer from data gaps within their time series, necessitating imputation to ensure dataset integrity for further ...
KHALID QARAGHULI, M. F. MURSHED, M. AZLIN M. SAID, ALI MOKHTAR, IMAN ROUSTA   +4 more
doaj   +1 more source

Application of Multiple Imputation in Dealing with Missing Data in Agricultural Surveys: The Case of BMP Adoption

Journal of Agricultural and Resource Economics, 2018
Missing-data problems are common in farmer surveys but are often ignored in the literature. Conventional methods to address missing data, such as deletion and mean replacement, assume that data are missing completely at random, which rarely holds.
Hua Zhong, Wuyang Hu, Jerrod M. Penn
doaj   +1 more source

Adaptive Imputation of Irregular Truncated Signals with Machine Learning

Applied Sciences
In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent.
Tyler Ward, Kouroush Jenab, Jorge Ortega-Moody   +2 more
doaj   +1 more source

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

Epidemiology, Biostatistics and Public Health, 2022
Background The 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.
Sander MJ van Kuijk , Wolfgang Viechtbauer, Louis L Peeters, Luc Smits   +3 more
doaj  

Non-Response in Dynamic Panel Data Models [PDF]

This paper stresses the links that exist between concepts that are used in the theory of model reduction and concepts that arise in the missing data literature.
Cheti Nicoletti
core  

Spatially Gated Mixture of Experts for Missing Data Imputation in Pavement Management Systems

Systems
Accurate imputation of missing pavement-condition data is critical for proactive infrastructure management, yet it is complicated by spatial non-stationarity—deterioration patterns and data quality vary markedly across regions.
Bongjun Ji, Seungyeon Han, Mun-Sup Lee
doaj   +1 more source