Cargando…

An Exponential-Cum-Sine-Type Hybrid Imputation Technique for Missing Data

In this study, a new exponential-cum-sine-type hybrid imputation technique has been proposed to handle missing data when conducting surveys. The properties of the corresponding point estimator for population mean have been examined in terms of bias and mean square errors. An extensive simulation stu...

Descripción completa

Detalles Bibliográficos
Autores principales:	Bhattacharyya, D., Singh, G. N., Jawa, Taghreed M., Sayed-Ahmed, Neveen, Pandey, Awadhesh K.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Hindawi 2021
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8664504/ https://www.ncbi.nlm.nih.gov/pubmed/34899894 http://dx.doi.org/10.1155/2021/4845569

Ejemplares similares

Some Classes of Logarithmic-Type Imputation Techniques for Handling Missing Data
por: Pandey, Awadhesh K., et al.
Publicado: (2021)

SICE: an improved missing data imputation technique
por: Khan, Shahidul Islam, et al.
Publicado: (2020)

Tables of sine, cosine and exponential integrals
por: National Bureau of Standards. Washington, DC
Publicado: (1940)

Assessing the Lifetime Performance Index with Digital Inferences of Power Hazard Function Distribution Using Progressive Type-II Censoring Scheme
por: EL-Sagheer, Rashad M., et al.
Publicado: (2022)

Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate
por: Panwar, M. S., et al.
Publicado: (2022)

Flexible imputation of missing data
por: van Buuren, Stef
Publicado: (2018)

Missing Data and Imputation Methods
por: Schober, Patrick, et al.
Publicado: (2020)

An Improved Estimation for Heterogeneous Datasets with Lower Detection Limits regarding Environmental Health
por: Feroze, Navid, et al.
Publicado: (2022)

Missing data imputation techniques for wireless continuous vital signs monitoring
por: van Rossum, Mathilde C., et al.
Publicado: (2023)

The new discrete distribution with application to COVID-19 Data
por: Almetwally, Ehab M., et al.
Publicado: (2022)

A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers
por: Turrado, Concepción Crespo, et al.
Publicado: (2016)

The Frailty Index, Missing Data, and Imputation
por: Pridham, Glen, et al.
Publicado: (2020)

A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset
por: Gan, Qihong, et al.
Publicado: (2023)

Inferences for Stress-Strength Reliability Model in the Presence of Partially Accelerated Life Test to Its Strength Variable
por: El-Sagheer, Rashad M., et al.
Publicado: (2022)

Optimal Periods of Conducting Preventive Maintenance to Reduce Expected Downtime and Its Impact on Improving Reliability
por: Al-Duais, Fuad S., et al.
Publicado: (2022)

Residual and Past Discrete Tsallis and Renyi Extropy with an Application to Softmax Function
por: Jawa, Taghreed M., et al.
Publicado: (2022)

Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
por: Liao, Serena G, et al.
Publicado: (2014)

Imputing missing genotypes: effects of methods and patterns of missing data
por: Ogut, Funda, et al.
Publicado: (2011)

Imputation of missing genotypes: an empirical evaluation of IMPUTE
por: Zhao, Zhenming, et al.
Publicado: (2008)

A hybrid imputation approach for microarray missing value estimation
por: Li, Huihui, et al.
Publicado: (2015)

Multiple imputation of missing data using SAS
por: Berglund, Patricia A, et al.
Publicado: (2014)

Imputation methods for missing data for polygenic models
por: Fridley, Brooke, et al.
Publicado: (2003)

Imputation of missing information in worldwide patent data
por: de Rassenfosse, Gaétan, et al.
Publicado: (2020)

Impute the missing data using retrieved dropouts
por: Wang, Shuai, et al.
Publicado: (2022)

Imputing Missing Data in Hourly Traffic Counts
por: Shafique, Muhammad Awais
Publicado: (2022)

A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies
por: Muhammad, Mustapha, et al.
Publicado: (2021)

A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction
por: Hu, Zhiyong, et al.
Publicado: (2020)

Efficient technique of microarray missing data imputation using clustering and weighted nearest neighbour
por: Dubey, Aditya, et al.
Publicado: (2021)

Artificial neural networks and statistical models for optimization studying COVID-19
por: Elhag, Azhari A., et al.
Publicado: (2021)

Evaluating the state of the art in missing data imputation for clinical data
por: Luo, Yuan
Publicado: (2021)

Robust imputation method for missing values in microarray data
por: Yoon, Dankyu, et al.
Publicado: (2007)

Comparison of imputation methods for missing laboratory data in medicine
por: Waljee, Akbar K, et al.
Publicado: (2013)

Multiple Imputation of Missing Composite Outcomes in Longitudinal Data
por: O’Keeffe, Aidan G., et al.
Publicado: (2016)

The Sin of Missing Data: Is All Forgiven by Way of Imputation?
por: Dettori, Joseph R., et al.
Publicado: (2018)

Missing data and multiple imputation in clinical epidemiological research
por: Pedersen, Alma B, et al.
Publicado: (2017)

Imputing missing data of function and disease activity in rheumatoid arthritis registers: what is the best technique?
por: Mongin, Denis, et al.
Publicado: (2019)

Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques
por: Wangwongchai, Angkool, et al.
Publicado: (2023)

Missing value imputation using least squares techniques in contaminated matrices
por: Garcia-Peña, Marisol, et al.
Publicado: (2022)

Multiple imputation of missing data under missing at random: including a collider as an auxiliary variable in the imputation model can induce bias
por: Curnow, Elinor, et al.
Publicado: (2023)

Efficiency of multiple imputation to test for association in the presence of missing data
por: Croiseau, Pascal, et al.
Publicado: (2007)

Cannot write session to /tmp/vufind_sessions/sess_3viie152o0v9jehr877gb1h5em