scholarly journals Estimation of parameters of finite population L-statistics

2013 ◽  
Vol 18 (3) ◽  
pp. 327-343 ◽  
Author(s):  
Dalius Pumputis ◽  
Andrius Čiginas
Keyword(s):  
Order Statistics ◽  
Linear Combination ◽  
Simulation Study ◽  
Finite Population ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Estimation Accuracy ◽  
Estimation Of Parameters ◽  
Edgeworth Expansions

We consider the estimation of important parameters of a linear combination of order statistics (L-statistic) in a finite population, emphasizing the influence of auxiliary information on the estimation accuracy. Assuming that values of an auxiliary variable are available for all population units, we construct calibrated estimators for the variance of L-statistics and for the parameters, which define one-term Edgeworth expansions of distributions of L-statistics. The gain of the new estimators is demonstrated by the simulation study.

scholarly journals Estimation of finite population distribution function with dual use of auxiliary information under non-response

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243584
Author(s):  
Sardar Hussain ◽  
Sohaib Ahmad ◽  
Sohail Akhtar ◽  
Amara Javed ◽  
Uzma Yasmeen
Keyword(s):  
Distribution Function ◽  
Finite Population ◽  
Population Distribution ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Distribution Functions ◽  
Auxiliary Variables ◽  
Sample Mean ◽  
Sample Distribution

In this paper, we propose two new families of estimators for estimating the finite population distribution function in the presence of non-response under simple random sampling. The proposed estimators require information on the sample distribution functions of the study and auxiliary variables, and additional information on either sample mean or ranks of the auxiliary variable. We considered two situations of non-response (i) non-response on both study and auxiliary variables, (ii) non-response occurs only on the study variable. The performance of the proposed estimators are compared with the existing estimators available in the literature, both theoretically and numerically. It is also observed that proposed estimators are more precise than the adapted distribution function estimators in terms of the percentage relative efficiency.

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 Using Auxiliary Information More Efficiently in Population Variance Estimation - A New Family of Estimators

2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Kalim Ullah ◽  
Zawar Hussain ◽  
Salman Arif Cheema
Keyword(s):  
Finite Population ◽  
Variance Estimation ◽  
Theoretical Study ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
First Order ◽  
New Family ◽  
Squared Error ◽  
Estimation Of Variance

In this article, we have suggested estimation of variance in finite population by using known values of parameter related to auxiliary information such as rank and second raw moment of auxiliary variable in stratified random sampling. The expression for the bias and mean squared error (MSE) of the suggested estimator are obtained up to first order of approximation. The proposed estimator is efficient comparatively various other estimators. A numerical and theoretical study are performed to support the suggested estimator.

scholarly journals Efficient Class of Estimators for Finite Population Mean using Auxiliary Information in Two-Occasion Successive Sampling

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
G. N. Singh ◽  
Mohd Khalid
Keyword(s):  
Linear Combination ◽  
Finite Population ◽  
Auxiliary Information ◽  
Successive Sampling ◽  
Sample Mean ◽  
Population Means ◽  
Population Mean ◽  
Practical Applications ◽  
Optimum Estimator ◽  
Class Of Estimators

In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion, was empirically compared with the proposed class of estimators. Mutual comparisons of the proposed estimator were carried out. Suitable recommendations are made to the survey statistician for practical applications.

scholarly journals Finite Population Distribution Function Estimation Using Auxiliary Information Under Simple Random Sampling

2021 ◽  
Vol 3 (1) ◽  
pp. 29-38
Author(s):  
Sohaib Ahmad ◽  
Sardar Hussain ◽  
Sohail Ahmad
Keyword(s):  
Distribution Function ◽  
Random Sampling ◽  
Finite Population ◽  
Population Distribution ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Supplementary Information ◽  
Function Estimation ◽  
Distribution Function Estimation

In this paper, a new estimator for estimating the finite population distribution function(DF) are propose using supplementary information on the DF of the auxiliary variable under simple random sampling. A comparative study is conducted to compare, theoretically and numerically, the adapted distribution function estimators of Cochran (1940), Murthy (1967), Bahl and Tuteja (1991), Rao (1991), Singh et al. (2009) and Grover and Kaur (2014) with the proposed estimators. It is found that the proposed estimators always perform better than the adapted estimators in terms of MSE and percentage relative efficiency.

scholarly journals An Alternative Estimator for Estimating the Finite Population Mean Using Auxiliary Information in Sample Surveys

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ramkrishna S. Solanki ◽  
Housila P. Singh ◽  
Anjana Rathour
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Population Mean ◽  
Asymptotic Expressions ◽  
Variance Formula ◽  
Optimum Estimator ◽  
Class Of Estimators ◽  
Alternative Estimator ◽  
Usual Product

This paper suggests a class of estimators for estimating the finite population mean of the study variable using known population mean of the auxiliary variable . Asymptotic expressions of bias and variance of the suggested class of estimators have been obtained. Asymptotic optimum estimator (AOE) in the class is identified along with its variance formula. It has been shown that the proposed class of estimators is more efficient than usual unbiased, usual ratio, usual product, Bahl and Tuteja (1991), and Kadilar and Cingi (2003) estimators under some realistic conditions. An empirical study is carried out to judge the merits of suggested estimator over other competitors practically.

Tuning design weights to deal with non-response in successive sampling

2021 ◽  
Vol 16 (2) ◽  
pp. 97-108
Author(s):  
Kumari Priyanka
Keyword(s):  
Natural Population ◽  
Simulation Study ◽  
Finite Population ◽  
Auxiliary Information ◽  
Population Level ◽  
Successive Sampling ◽  
Population Mean ◽  
Design Weights

The estimation of finite population mean at current occasion in two occasion successive sampling in presence of non-response is investigated using tuned jackknife estimators. Based on the availability of auxiliary information at population level (Info U) and sample level (Info s) and using tuned jackknife technique, estimators have been proposed. Estimator of variance of proposed estimators have also been discussed. Different cases of occurance of non-response have been explored. The estimators are mutually compared. The properties of these estimators are studied via simulation study using natural population.

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

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