scholarly journals Small area quantile estimation based on distribution function using linear mixed models

2021 ◽  
Vol 7 (2) ◽  
pp. 97-114
Author(s):  
Tomasz Stachurski
Keyword(s):  
Distribution Function ◽  
Simulation Study ◽  
Mixed Models ◽  
Small Area ◽  
Linear Mixed Models ◽  
Auxiliary Information ◽  
Quantile Estimation ◽  
Study Variable ◽  
Small Areas ◽  
Economic Studies

In economic studies researchers are often interested in the estimation of the distribution function or certain functions of the distribution function such as quantiles. This work focuses on the estimation quantiles as inverses of the estimates of the distribution function in the presence of auxiliary information that is correlated with the study variable. In the paper a plug-in estimator of the distribution function is proposed which is used to obtain quantiles in the population and in the small areas. Performance of the proposed method is compared with other estimators of the distribution function and quantiles using the simulation study. The obtained results show that the proposed method usually has smaller relative biases and relative RMSE comparing to other methods of obtaining quantiles based on inverting the distribution function.

Improving the quality of disaggregated SDG indicators with cluster information for small area estimates

2020 ◽  
Vol 36 (4) ◽  
pp. 955-961
Author(s):  
Rizky Zulkarnain ◽  
Dwi Jayanti ◽  
Tri Listianingrum
Keyword(s):  
Small Area ◽  
Research University ◽  
Direct Method ◽  
Random Effect ◽  
Auxiliary Information ◽  
District Level ◽  
Small Areas ◽  
Associate Dean ◽  
Development Goals

The increasing needs for more disaggregated data motivates National Statistical Offices (NSOs) to develop efficient methods for producing official statistics without compromising on quality. In Indonesia, regional autonomy requires that Sustainable Development Goals (SDGs) indicators are available up to the district level. However, several surveys such as the Indonesian Demographic and Health Survey produce estimates up to the provincial level only. This generates gaps in support for district level policies. Small area estimation (SAE) techniques are often considered as alternatives for overcoming this issue. SAE enables more reliable estimation of the small areas by utilizing auxiliary information from other sources. However, the standard SAE approach has limitations in estimating non-sampled areas. This paper introduces an approach to estimating the non-sampled area random effect by utilizing cluster information. This model is demonstrated via the estimation of contraception prevalence rates at district levels in North Sumatera province. The results showed that small area estimates considering cluster information (SAE-cluster) produce more precise estimates than the direct method. The SAE-cluster approach revises the direct estimates upward or downward. This approach has important implications for improving the quality of disaggregated SDGs indicators without increasing cost. The paper was prepared under the kind mentorship of Professor James J. Cochran, Associate Dean for Research, Prof. of Statistics and Operations Research, University of Alabama.

scholarly journals Estimation of a Distribution function under Sampling on Two Occasions

2009 ◽  
Vol 14 (3) ◽  
pp. 315-331
Author(s):  
V. Chadyšas
Keyword(s):  
Distribution Function ◽  
Sampling Design ◽  
Auxiliary Information ◽  
Population Data ◽  
Simple Random Sampling ◽  
Study Variable ◽  
Minimal Variance ◽  
Optimal Estimator ◽  
Real Population ◽  
Under Sampling

Estimation of the distribution function under sampling on two occasions with a simple random sampling design on each occasion is investigated. Composite regression and ratio type estimators are considered, using values of the study variable as auxiliary information obtained on the first occasion. The optimal estimator, in the sense of minimal variance, is also obtained. A simulation study, based on the real population data, is performed and the proposed estimators are compared by a simple estimator for a distribution function.

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