Comparison of EBLUP and EBLUP Modification in Estimating Small Areas (Study : Percentages of Poverty in Bogor District)

2018 ◽  
pp. 160-168
Author(s):  
Hary Merdeka ◽  
Kusman Sadik ◽  
Indahwati A
Keyword(s):  
Sample Size ◽  
Small Area ◽  
Small Sample Size ◽  
Large Error ◽  
Statistical Technique ◽  
Small Sample ◽  
Parameters Estimation ◽  
Small Areas ◽  
Modification Method ◽  
Size Estimates

A small area of the sample occurs when the sample size is very small. A large error will get if the parameters estimation is done with small the sample. One method to overcome it using a small area estimation (SAE) method. A small area estimator is a statistical technique to estimate the parameters of a sub-population with a small sample size. Estimates in the small area estimator method is based on the model and are indirect estimates. In this study the indirect method used is the EBLUP method and the modification of EBLUP estimator. The results of the alleged percentage of poverty in the Bogor district show that the EBLUP modification method is better compared to the expected method directly. This is based on the average of the RRMSE obtained.

scholarly journals Perbandingan Metode Bootstrap, Jacknife Jiang Dan Area Specific Jacknife Pada Pendugaan Mean Square Error Model Beta-Bernoulli

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yesi Santika ◽  
◽  
Widiarti Widiarti ◽  
Fitriani Fitriani ◽  
Mustofa Usman ◽  
...  
Keyword(s):  
Sample Size ◽  
Mean Square Error ◽  
Small Area ◽  
Empirical Bayes ◽  
Mean Squared Error ◽  
Small Sample Size ◽  
Statistical Technique ◽  
Small Sample ◽  
Mean Square ◽  
The Mean

Small area estimation is defined as a statistical technique for estimating the parameters of a subpopulation with a small sample size. One method of estimating small area parameters is the Empirical Bayes (EB) method. The accuracy of the Empirical Bayes (EB) estimator can be measured by evaluating the Mean Squared Error (MSE). In this study, 3 methods to determine MSE in the EB estimator of the Beta-Bernoulli model will be compared, namely the Bootstrap, Jackknife Jiang and Area-specific Jackknife methods. The study is carried out theoretically and empirically through simulation with R-studio software version 1.2.5033. The simulation results in a number of areas and pairs of prior distribution parameter values, namely Beta, show the effect of sample size and parameter value pairs on the Mean Square Error (MSE) value. The larger the number of areas and the smaller the initial 𝛽, the smaller the MSE value. The area-specific Jackknife method produces the smallest MSE in the number of areas 100 and the Beta parameter value 0.1.

scholarly journals Gender Wise Distribution of Income Using L-Moments Method

2019 ◽  
Vol 2 (1) ◽  
pp. 29
Author(s):  
Muhammad Alam ◽  
Saeed Ullah Jan ◽  
Alam Zeb
Keyword(s):  
Income Distribution ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Parameters Estimation ◽  
Large Sample Size ◽  
Unknown Parameters ◽  
Moments Method ◽  
L Moments ◽  
Distribution Of Income

<em>The main purpose of this work is to explore the income distribution of both male and female in Pakistan over the period of 2010-2011. For this purpose, the lognormal distribution with known parameters is used as a model and its unknown parameters are estimated by three methods that are likelihood, moments and L-moments. The results show that citizens of Pakistan are not equal in income and the probability plot suggested that the income of the male is greater than that of a female in Pakistan. Moreover, for small sample size, the best method of parameters estimation is the L-moments, while, for large sample size the best method is a maximum likelihood. Findings of the study suggest that suitable policy is required to maintain equality in income distribution in the country. It will consequently reduce the gap among rich and poor and will certainly improve social welfare.</em>

scholarly journals HasChlamydia trachomatisprevalence in young women in England, Scotland and Wales changed? Evidence from national probability surveys

2019 ◽  
Vol 147 ◽  
Author(s):  
D. Z. Kounali ◽  
N. J Welton ◽  
K. Soldan ◽  
S. C. Woodhall ◽  
J. Kevin Dunbar ◽  
...  
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Credible Interval ◽  
Small Sample ◽  
Nucleic Acid Amplification ◽  
Absolute Difference ◽  
Chlamydia Screening ◽  
Before And After ◽  
Evidence Source ◽  
Size Estimates

AbstractWe evaluate the utility of the National Surveys of Attitudes and Sexual Lifestyles (Natsal) undertaken in 2000 and 2010, before and after the introduction of the National Chlamydia Screening Programme, as an evidence source for estimating the change in prevalence ofChlamydia trachomatis(CT) in England, Scotland and Wales. Both the 2000 and 2010 surveys tested urine samples for CT by Nucleic Acid Amplification Tests (NAATs). We examined the sources of uncertainty in estimates of CT prevalence change, including sample size and adjustments for test sensitivity and specificity, survey non-response and informative non-response. In 2000, the unadjusted CT prevalence was 4.22% in women aged 18–24 years; in 2010, CT prevalence was 3.92%, a non-significant absolute difference of 0.30 percentage points (95% credible interval −2.8 to 2.0). In addition to uncertainty due to small sample size, estimates were sensitive to specificity, survey non-response or informative non-response, such that plausible changes in any one of these would be enough to either reverse or double any likely change in prevalence. Alternative ways of monitoring changes in CT incidence and prevalence over time are discussed.

scholarly journals Evaluation of Synthetic Small-area Estimators Using Design-based Methods

2019 ◽  
Vol 48 (4) ◽  
pp. 43-57
Author(s):  
Partha Lahiri ◽  
Santanu Pramanik
Keyword(s):  
Sample Size ◽  
Mean Squared Error ◽  
Small Sample Size ◽  
Small Sample ◽  
Error Estimator ◽  
Specific Design ◽  
Small Areas ◽  
Model Free ◽  
Squared Error ◽  
Better Than

The use of area-specific design-based mean squared error (MSE) to measure the uncertainty associated with synthetic and direct estimators is appealing since the same model-free criterion is applied. However, the small sample size is often a difficulty in obtaining a reliable estimator of the area-specific design-based MSE. Moreover, the area-specific design-based mean squared error estimator might yield undesirable negative values under certain circumstances. The existing solution to overcome the problem of small sample size is to consider average design-based MSE, average being taken over the available small areas. This may not solve the other problem of negative MSE. An alternative average design-based mean squared error estimator is proposed which always produces positive estimates. Simulation shows that this estimator performs better than the existing average design-based MSEs as it always produces positive estimates and accounts for the bias component usually present in synthetic estimators.

Application of Ultrasound Elastography for Assessing Intestinal Fibrosis in Inflammatory Bowel Disease: Fiction or Reality?

2020 ◽  
Vol 21 ◽  
Author(s):  
Roberto Gabbiadini ◽  
Eirini Zacharopoulou ◽  
Federica Furfaro ◽  
Vincenzo Craviotto ◽  
Alessandra Zilli ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Sample Size ◽  
Bowel Disease ◽  
Small Sample Size ◽  
Shear Wave Elastography ◽  
Small Sample ◽  
Ultrasound Elastography ◽  
Invasive Technique ◽  
Intestinal Fibrosis ◽  
Inflammatory Bowel

Background: Intestinal fibrosis and subsequent strictures represent an important burden in inflammatory bowel disease (IBD). The detection and evaluation of the degree of fibrosis in stricturing Crohn’s disease (CD) is important to address the best therapeutic strategy (medical anti-inflammatory therapy, endoscopic dilation, surgery). Ultrasound elastography (USE) is a non-invasive technique that has been proposed in the field of IBD for evaluating intestinal stiffness as a biomarker of intestinal fibrosis. Objective: The aim of this review is to discuss the ability and current role of ultrasound elastography in the assessment of intestinal fibrosis. Results and Conclusion: Data on USE in IBD are provided by pilot and proof-of-concept studies with small sample size. The first type of USE investigated was strain elastography, while shear wave elastography has been introduced lately. Despite the heterogeneity of the methods of the studies, USE has been proven to be able to assess intestinal fibrosis in patients with stricturing CD. However, before introducing this technique in current practice, further studies with larger sample size and homogeneous parameters, testing reproducibility, and identification of validated cut-off values are needed.

scholarly journals Small Area Estimation of Smoke-free Workplace Polices and Home Rules in U.S. Counties

2021 ◽  
Author(s):  
Benmei Liu ◽  
Isaac Dompreh ◽  
Anne M Hartman
Keyword(s):  
Tobacco Control ◽  
Small Area ◽  
Small Sample Size ◽  
Health Hazard ◽  
Small Area Estimation ◽  
Small Sample ◽  
County Level ◽  
Area Estimation ◽  
Model Based ◽  
Workplace Policies

Abstract Background The workplace and home are sources of exposure to secondhand smoke (SHS), a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from SHS and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small area estimation techniques are needed to produce such estimates. Methods Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods. Results Model-based estimates of smoke-free workplace policies and home rules were produced for 3,134 (out of 3,143) U.S. counties. In 2014-2015, nearly 80% of U.S. adult workers were covered by smoke-free workplace policies, and more than 85% of U.S. adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules. Conclusions The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention. Implications Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, e.g., to other behavioral, policy, or health related topics.

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