Some Imputation Methods for the Estimation of Domain Mean with an Application to Crop Production Data
33 / 16
Authors
-
Anoop Kumar
Central University of Haryana,
-
Shashi Bhushan
University of Lucknow, Lucknow
-
Rohini Pokhrel
Dr. Shakuntala Misra National Rehabilitation University, Lucknow
Keywords:
Missing data; Imputation; EfficiencyAbstract
Missing data is an unavoidable yet pestering issue in most of the surveys, and if it is not tackled carefully during the survey, the results may exhibit
significant biases in the survey findings. This research proposes mean, ratio, product, and some general classes of imputation methods to solve the
problem of missing data at domain level and proposes a domain mean estimation methods utilizing simple random sampling. The properties of the
suggested imputation methods are determined up to first order approximation, followed by a simulation analysis utilising hypothetically created data
sets. Further, the simulation study is extended with a real data application. Additionally, some appropriate recommendations for practical applications
have been provided to survey experts.
Downloads
References
Ahmed, M.S., Al-Titi, O., Al-Rawi, Z. and Abu-Dayyeh, W.(2006).
Estimation of a population mean using different using different
imputation methods. Statistics in Transition, 7(6), 1247-1264.
Ashutosh (2020). Estimation of domain mean using conventional
synthetic estimator with two auxiliary characters. Annals of Data
Science, 10(1), 153-166.
Bhushan, S. and Pandey, A.P. (2016). Optimal imputation of missing
data for estimation of population mean. Journal of Statistics and
Management System, 19(6),755-769.
Bhushan, S. and Pandey, A.P. (2018). Optimality of ratio type
estimation methods for population mean in presence of missing
data. Communication in Statistics- Theory and Methods, 47(11),
2576-2589.
Bhushan, S. and Pandey, A.P. (2020). Optimal imputation of missing
data using multi auxiliary information. Computational Statistics,
36(1), 449-477.
Bhushan, S. and Pandey, A.P. (2021). Optimality of ratio-type
imputation methods for estimation of population mean using
higher order moment of an auxiliary variable. Journal of Statistical
Theory and Practice, 15, 48.
Bhushan, S., Kumar, A., Pandey, A.P. and Singh, S. (2022). Estimation
of population mean in presence of missing data under simple
random sampling. Communications in Statistics- Simulation and
Computation, 52(12), 6048-6069.
Heitjan, D.F. and Basu S. (1996). Distinguishing ‘Missing at Random’
and ‘Missing Completely at Random’. The American Statistician,
50, 207-213.
Khare, B.B. and Ashutosh (2017). Synthetic estimator for domain mean
using auxiliary character. International Journal of Statistics and
Economics, 18(4), 32-44.
Khare,B.B., Ashutosh and Khare, S.(2018). Modified direct regression
estimator for domain mean using auxiliary character. International
Journal of Applied Mathematics and Statistics, 57(6), 10-20.
Lee, H., Rancourt, E. and Sarndal, C.E.(1994). Experiments with
variance estimation from survey data with imputed values. Journal
of Official Statistics, 10, 231-243.
Pandey, K.K. and Tikkiwal, G.C. (2010). Generalized class of synthetic
estimators for small area under systematic sampling design.
Statistics in Transition-new series, Poland, 11(1), 75-89.
Panse, V.G., Rajagopalan, M. and Pillai, S.S.(1966). Estimation of crop
yields for small areas. Biometrics, 374-384.
Rubin, R.B.(1976). Inference and missing data. Biometrika, 63(3),
581-592.
Prasad, S.(2017). A study on new methods of ratio exponential type
imputation in sample surveys. Hecettepe J Math Stat., 47(5),
1281-1301.
Rueda, M., Gonzalez, S. and Arcos, A. (2005). Indirect methods of
imputation of missing data based on available units. Appl. Math.
Computat., 164, 249-261.
Sharma, S.D., Srivastava, A.K. and Sud, U.C.(2004). Small area crop
estimation for crop yield estimators at Gram Panchayat level.
Journal of Indian Society of Agricultural Statistics, 57, 26-37.
Sharma, M.K. and Sisodia, B.V.S.(2016). Estimation of crop production
for smaller geographical area: An application of discriminant
function analysis. Journal of Statistical Theory and Practice, 10,
444-455.
Singh, H.P. and Horn, S. (1998). An alternative estimator for multi
character surveys. Metrika, 48, 99-107.
Singh, R. and Goel, R.C. (2000). Use of remote sensing satellite data
in crop survey. Technical report, Indian Agricultural Statistics
Research Institute, New Delhi.
Sisodia, B.V.S. and Singh, A. (2001). On small area estimation-An
empirical study. Journal of Indian Society Agricultural Statistics,
54(3), 303-316.
Sisodia, B.V.S. and Chandra, H. (2012). Estimation of crop production
at smaller geographical level in India. Journal of the Indian
Society of Agricultural Statistics, 66(2), 313-319.
Srivastava, A.K., Ahuja, D.L., Bhatia, D.K. and Mathur, D.C.(1999).
Small area estimation technique in agriculture. Souvenir in
Advances in Agricultural Statistics and Computer Application,
Indian Agricultural Statistics Research Institute, New Delhi, 32-45
Singh, S. and Deo, B. (2003). Imputation by power transformation.
Statistical Papers, 44, 555-579.
Singh, S. (2009). A new method of imputation in survey sampling: A
Journal of Theoretical and Applied Statistics, 43(5), 499-511.
Singh, S. and Horn, S. (2000). Compromised imputation in survey
sampling. Metrika, 51, 267-276.
Tikkiwal, B.D. (1993). Modeling through survey data for small
domains. Proceedings of International Scientific Conference on
small Area Statistics and Survey Design (An invited paper), held
in September 1992 at Warsaw, Poland.
Tikkiwal G.C. and Ghiya, A. (2000). A generalized class of synthetic
estimators with application to crop acreage estimation for small
domains. Biometrical Journal, 42, 865-876.
Tikkiwal, G.C., Rai, P.K. and Ghiya, A. (2013). On the performance
of generalized regression estimator for small domains.
Communications in Statistics- Simulation and Computation,
42(4), 891-909.
Toutenburg, H. and Srivastava, V.K. (1998). Estmation of ratio of
population means in survey sampling when some observations are
missing. Metrika, 48, 177-187.
Toutenburg, H. and Srivastava, V.K. and Shalabh (2008). Amputation
versus imputation of missing values through ratio method in
sample surveys. Statistical Papers, 49, 237-247
Downloads
Submitted
Published
Issue
Section
How to Cite
