An Exponential Estimator over Regression Estimator Using Two Auxiliary variables

2021 ◽  
Vol 10 (3) ◽  
pp. 739-742
Keyword(s):  
Regression Estimator ◽  
Auxiliary Variables

scholarly journals Adapted Exponential Type Estimator in the Presence of Non-response

2021 ◽  
Author(s):  
Ceren Ünal ◽  
Cem Kadilar
Keyword(s):  
Empirical Study ◽  
Exponential Type ◽  
Unbiased Estimator ◽  
Data Sets ◽  
Regression Estimator ◽  
Mean Square ◽  
Order Of Approximation ◽  
Auxiliary Variables ◽  
First Order ◽  
Theoretical Results

In this article, we propose an estimator using the exponential function for the population mean in the case of non-response on both the study and the auxiliary variables. The equations for the Bias and Mean Square Error (MSE) are derived to the first order of approximation and theoretical comparisons are made with existing estimators in literature. Besides, we examine the efficiency of the proposed estimator according to the classical ratio and regression estimator, Hansen-Hurwitz unbiased estimator, and the estimator of Singh et al. (2009). Following theoretical comparisons, we infer that the proposed estimator is more efficient than compared estimators under the obtained conditions in theory. Moreover, these theoretical results are supported numerically by providing an empirical study on five different data sets.

scholarly journals A New Regression Estimator Based on Two Auxiliary Variables

2014 ◽  
Vol 8 (2) ◽  
pp. 251-252
Author(s):  
Saman Shahbaz ◽  
Muhammad Zubair ◽  
Nadeem Shafique Butt ◽  
Muhammad Ismail
Keyword(s):  
Regression Estimator ◽  
Auxiliary Variables

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.      

scholarly journals Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

2019 ◽  
Vol 11 (14) ◽  
pp. 1683 ◽  
Author(s):  
Yangchengsi Zhang ◽  
Long Guo ◽  
Yiyun Chen ◽  
Tiezhu Shi ◽  
Mei Luo ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Carbon ◽  
Time Series Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Series Data ◽  
Landsat 8 ◽  
Auxiliary Variables ◽  
Jianghan Plain ◽  
Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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