scholarly journals Generalised Regression Estimation Given Imperfectly Matched Auxiliary Data

2021 ◽  
Vol 37 (1) ◽  
pp. 239-255
Author(s):  
Li-Chun Zhang
Keyword(s):  
Simulation Study ◽  
Auxiliary Information ◽  
Survey Sampling ◽  
Regression Estimation ◽  
Regression Estimator ◽  
Mean Square ◽  
Auxiliary Data ◽  
Mean Square Errors

Abstract Generalised regression estimation allows one to make use of available auxiliary information in survey sampling. We develop three types of generalised regression estimator when the auxiliary data cannot be matched perfectly to the sample units, so that the standard estimator is inapplicable. The inference remains design-based. Consistency of the proposed estimators is either given by construction or else can be tested given the observed sample and links. Mean square errors can be estimated. A simulation study is used to explore the potentials of the proposed estimators.

scholarly journals Memory Type Ratio and Product Estimators in Stratified Sampling

2020 ◽  
Author(s):  
Irfan Aslam ◽  
Muhammad Noor-ul-Amin ◽  
Uzma Yasmeen ◽  
Muhammad Hanif
Keyword(s):  
Simulation Study ◽  
Moving Average ◽  
Real Life ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Real Life Data ◽  
Memory Type ◽  
Exponential Weighted Moving Average ◽  
Mean Square Errors ◽  
Weighted Moving Average

The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators of the population parameters. In this study, the EWMA statistic is used to estimate the population mean with auxiliary information. The memory type ratio and product estimators are proposed under stratified sampling (StS). Mean square errors (MSE) expressions and relative efficiencies of the proposed estimators are derived. An extensive simulation study is conducted to evaluate the performance of the proposed estimators. An empirical study is presented based on real-life data that supports the findings of the simulation study.

scholarly journals Comparison of Various Methods for Estimating Finite Population Total in Survey Sampling When Study Variable and Auxiliary Variable are Inversely Related

2019 ◽  
Vol 11 (1) ◽  
pp. 15-22
Author(s):  
S. Kumar ◽  
B. V. S. Sisodia
Keyword(s):  
Simulation Study ◽  
Finite Population ◽  
Auxiliary Variable ◽  
Survey Sampling ◽  
The Other ◽  
Regression Estimator ◽  
Study Variable ◽  
Population Total ◽  
Model Based ◽  
Proposed Model

In the present paper, a model based calibration estimator of population total has been developed when study variable y and auxiliary variable x are inversely related. The relative performance of the proposed model based calibration estimator in comparison to model based estimator, the usual regression estimator and calibration based regression estimator have been examined by conducting a limited simulation study. In view of the results of the simulation study, it has been found that model based calibration estimator has outperformed the other estimators. However, calibration based regression estimator was found to be close to the model based calibration estimator.  

scholarly journals Overcoming the problems caused by collinearity in mixed-effects logistic model: determining the contribution of various types of violence on depression in pregnant women

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanaz Khalili ◽  
Javad Faradmal ◽  
Hossein Mahjub ◽  
Babak Moeini ◽  
Khadijeh Ezzati-Rastegar
Keyword(s):  
Pregnant Women ◽  
Simulation Study ◽  
Fixed Effects ◽  
Logistic Model ◽  
Mixed Effects ◽  
Mean Square ◽  
Empirical Power ◽  
Binary Responses ◽  
Correlated Binary Responses ◽  
Mean Square Errors

Abstract Background Collinearity is a common and problematic phenomenon in studies on public health. It leads to inflation in variance of estimator and reduces test power. This phenomenon can occur in any model. In this study, a new ridge mixed-effects logistic model (RMELM) is proposed to overcome consequences of collinearity in correlated binary responses. Methods Parameters were estimated through penalized log-likelihood with combining expectation maximization (EM) algorithm, gradient ascent, and Fisher-scoring methods. A simulation study was performed to compare new model with mixed-effects logistic model(MELM). Mean square error, relative bias, empirical power, and variance of random effects were used to evaluate RMELM. Also, contribution of various types of violence, and intervention on depression among pregnant women experiencing intimate partner violence(IPV) were analyzed by new and previous models. Results Simulation study showed that mean square errors of fixed effects were decreased for RMELM than MELM and empirical power were increased. Inflation in variance of estimators due to collinearity was clearly shown in the MELM in data on IPV and RMELM adjusted the variances. Conclusions According to simulation results and analyzing IPV data, this new estimator is appropriate to deal with collinearity problems in the modelling of correlated binary responses.

scholarly journals The Performance of Some Restricted Estimators In Restricted Linear Regression Model

2021 ◽  
Vol 26 (2) ◽  
Author(s):  
Bader Aboud ◽  
Mustafa Ismaeel Naif
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Simulation Study ◽  
Linear Regression Model ◽  
Regression Estimator ◽  
Mean Square ◽  
Ridge Regression Estimator ◽  
The Mean ◽  
Biased Estimators ◽  
Better Than

In the linear regression model, the restricted biased estimation as one of important  methods to addressing the high variance and the  multicollinearity problems. In this paper, we make the simulation study of the some restricted biased estimators. The mean square error (MME) criteria are used to make a comparison  among them. According to the simulation study we observe that, the performance of the restricted modified unbiased  ridge regression estimator (RMUR) was proposed by  Bader and Alheety (2020)  is better than  of these estimators. Numerical example have been considered to illustrate the performance of the estimators.

scholarly journals On Estimating Quantiles Using Auxiliary Information

2015 ◽  
Vol 31 (1) ◽  
pp. 101-119 ◽  
Author(s):  
Yves G. Berger ◽  
Juan F. Munoz
Keyword(s):  
Simulation Study ◽  
Auxiliary Information ◽  
Regression Estimator ◽  
Acceptable Accuracy ◽  
Good Compromise ◽  
Computationally Intensive ◽  
Multistage Sampling ◽  
Selection Probabilities

Abstract We propose a transformation-based approach for estimating quantiles using auxiliary information. The proposed estimators can be easily implemented using a regression estimator. We show that the proposed estimators are consistent and asymptotically unbiased. The main advantage of the proposed estimators is their simplicity. Despite the fact the proposed estimators are not necessarily more efficient than their competitors, they offer a good compromise between accuracy and simplicity. They can be used under single and multistage sampling designs with unequal selection probabilities. A simulation study supports our finding and shows that the proposed estimators are robust and of an acceptable accuracy compared to alternative estimators, which can be more computationally intensive.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
D. Bhattacharyya ◽  
G.N. Singh ◽  
Taghreed M. Jawa ◽  
Neveen Sayed-Ahmed ◽  
Awadhesh K. Pandey
Keyword(s):  
Missing Data ◽  
Simulation Study ◽  
Real Life ◽  
Mean Square ◽  
Extensive Simulation ◽  
Population Mean ◽  
Gamma Distributions ◽  
Point Estimator ◽  
Using Data ◽  
Mean Square Errors

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 study using data generated from normal, Poisson, and Gamma distributions has been conducted to evaluate how the proposed estimator performs in comparison to several contemporary estimators. The results have been summarized, and discussion regarding real-life applications of the estimator follows.

scholarly journals Dual Use of Auxiliary Information for Estimating the Finite Population Mean under the Stratified Random Sampling Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sohaib Ahmad ◽  
Sardar Hussain ◽  
Muhammad Aamir ◽  
Uzma Yasmeen ◽  
Javid Shabbir ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Simulation Study ◽  
Random Sampling ◽  
Finite Population ◽  
Stratified Random Sampling ◽  
Data Sets ◽  
Mean Square ◽  
Sample Mean ◽  
Population Mean ◽  
Mean Square Errors

In this paper, we proposed an improved family of estimators for finite population mean under stratified random sampling, which needed a helping variable on the sample mean and rank of the auxiliary variable. The expression of the bias and mean square error of the proposed and existing estimators are computed up to the first-order approximation. The estimators proposed in different situations were investigated and provided a minimum mean square error relative to all other estimators considered. Four actual data sets and simulation studies are carried out to observe the performance of the estimators. For simulation study, R software is used. The mean square errors of all four data sets are minimum and percent relative efficiencies are more than a hundred percent higher than the other existing estimators, which indicated the importance of the newly proposed family of estimators. From the simulation study, it is concluded that the suggested family of estimators achieved better results. We demonstrate theoretically and numerically that the proposed estimator produces efficient results compared to all other contend estimators in entire situations. Overall, we conclude that the performance of the family of suggested estimators is better than all existing estimators.

scholarly journals Some Classes of Logarithmic-Type Imputation Techniques for Handling Missing Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Awadhesh K. Pandey ◽  
G. N. Singh ◽  
D. Bhattacharyya ◽  
Abdulrazzaq Q. Ali ◽  
Samah Al-Thubaiti ◽  
...  
Keyword(s):  
Missing Data ◽  
Simulation Study ◽  
Real Life ◽  
Mean Square ◽  
Extensive Simulation ◽  
Gamma Distributions ◽  
Using Data ◽  
Point Estimators ◽  
Mean Square Errors ◽  
Logarithmic Type

In this manuscript, three new classes of log-type imputation techniques have been proposed to handle missing data when conducting surveys. The corresponding classes of point estimators have been derived for estimating the population mean. Their properties (Mean Square Errors and bias) have been studied. An extensive simulation study using data generated from normal, Poisson, and Gamma distributions, as well as real dataset, has been conducted to evaluate how the proposed estimator performs in comparison to several contemporary estimators. The results have been summarized, and discussion regarding real-life applications of the estimator follows.

scholarly journals A MODIFIED GENERALIZED CHAIN RATIO IN REGRESSION ESTIMATOR

2021 ◽  
Vol 19 (1) ◽  
pp. 1-7
Author(s):  
F. S. APANTAKU ◽  
O. M. OLAYIWOLA ◽  
A. O. AJAYI ◽  
O. S. JAIYEOLA
Keyword(s):  
Local Government ◽  
Relative Efficiency ◽  
Data Sets ◽  
Regression Estimator ◽  
Mean Square ◽  
Auxiliary Variables ◽  
Ogun State ◽  
Tobacco Production ◽  
The Mean ◽  
Mean Square Errors

Generalized Chain ratio in regression type estimator is efficient for estimating the population mean. Many authors have derived a Generalized Chain ratio in regression type estimator. However, the computation of its Mean Square Error (MSE) is cumbersome based on the fact that several iterations have to be done, hence the need for a modified generalized chain ratio in regression estimator with lower MSE. This study proposed a modified generalized chain ratio in regression estimator which is less cumbersome in its computation. Two data sets were used in this study. The first data were on tobacco production by tobacco producing countries with yield of tobacco (variable of interest), area of land and production in metric tonnes as the auxiliary variables. The second data were the number of graduating pupils (variable of interest) in Ado-Odo/Ota local government, Ogun state with the number of enrolled pupils in primaries one and five as the auxiliary variables. The mean square errors in the existing and proposed estimators for various values of alpha were derived and relative efficiency was determined. The MSE for the existing estimator of tobacco production gave six values 0.0080, 0.0079, 0.0080, 0.0082, 0.0087 and 0.0093 with 0.0079 as the minimum while the proposed estimator gave 0.0054. The MSEs for the existing estimator for the graduating pupils were 20.73, 11.08, 7.49, 9.96, 18.50 and 33.10 with 7.49 as the minimum while the proposed was 6.52. The results of this study showed that the proposed estimator gave lower MSE for the two data sets, hence it is more efficient.      

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