On use of Ranked Set Sampling for estimating Super-population Total: Gamma Population Model

Ahmed, Shakeel; Shabbir, Javid

doi:10.24200/sci.2019.50976.1946

Cited by 5 publications

(14 citation statements)

References 44 publications

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“…For real data, results are obtained for HPM (M = 1), LPM (M = 2), the quadratic model (M = 3) and the higher order polynomial (cubic) model (M = 4). The estimated error variances ofτ (y) are obtained from Equation (35) based on different estimators of σ 2 . Note that all results given in Tables 1-4 are provided for ridge regression estimator with certain choices of v as the variance estimator given in n Equation ( 34) is a special case of variance estimator in Equation (35) with v = 0.…”

Section: Simulationsmentioning

confidence: 99%

“…The estimated error variances ofτ (y) are obtained from Equation (35) based on different estimators of σ 2 . Note that all results given in Tables 1-4 are provided for ridge regression estimator with certain choices of v as the variance estimator given in n Equation ( 34) is a special case of variance estimator in Equation (35) with v = 0. The ESPE for different combinations of M , v and n is enlisted in third column of Tables 1-2.…”

Section: Simulationsmentioning

confidence: 99%

“…Therefore, add this application and the following sentence here: Note that the LLR technique can also be used for evaluating entrepreneurial opportunities, see [30] for more information about this topic. The researches documented in literature consist a wide variety of model-based estimators constructed under different forms of the relationship between the outcome and the predictors, see [31], [32], [33], [34], [35], [36] [37] and [38]. Although a wide variety of restricted sampling methods have been in [8] some of them are based on linear regression model, some on polynomial models and some on proportional and stratified population models.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Basis Function Approach to Finite Population Parameter Estimation

Ahmed

Shabbir

2021

Scientia Iranica

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Modeling non-linear data is a common practice in data science and machine learning (ML). It is aberrant to get a natural process whose outcome varies linearly with the values of input variable(s). A robust and easy methodology is needed for accurately and quickly fitting a sampled data set with a set of covariates assuming that the sampled data could be a complicated non-linear function. A novel approach for estimation of finite population parameter τ , a linear combination of the population values is considered, in this article, under superpopulation setting with known basis functions regression (BFR) models. The problems of subsets selection with single predictor under an automatic matrix approach, and ill-conditioned regression models are discussed. Prediction error variance of the proposed estimator is estimated under widely used feature selection criteria in ML. Finally, the expected squared prediction error (ESPE) of the proposed estimator and the expectation of estimated error variance under bootstrapping as well as simulation study with different regularizers are obtained to observe the long-run behavior of the proposed estimator.

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Section: Simulationsmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Basis Function Approach to Finite Population Parameter Estimation

Ahmed

Shabbir

2021

Scientia Iranica

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“…Some other useful references are, Irfan et al [17], Abid et al. [18], Abid et al [19], Javed et al [20], Naz et al [21], Younis and Shabbir [22], Ahmed and Shabbir [23] and Nazir et al [24].…”

Section: Introductionmentioning

confidence: 99%

On use of subsampling of the non-respondents for estimation of distribution function

Shabbir

Gupta

2021

Scientia Iranica

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In this study, we propose a general class of estimators of the finite population distribution function (DF) using two auxiliary variables under subsampling of non-respondents.We use the Hansen and Hurwitz pioneered model in our subsampling technique. Layout of response and non-response classes are discussed in various tables in detail. Expressions for the biases and mean square errors (MSEs) of the estimators are obtained up to first order of approximation. We also obtain the conditions by comparing the proposed estimator with existing estimators. Three real data sets are used to support the theoretical findings. In our findings, it is observed that the proposed class of estimators is more efficient as compared to all other existing estimators including the usual mean estimator, ratio estimator, exponential-ratio estimator, traditional difference estimator, and many well-known difference type estimators by using the criterion of MSE.

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“…The derivation of the likelihood function for parameter estimation based on double-ranked set sampling (DRSS) designs were used by [22] for the estimation of the parameters of the power-generalized Weibull distribution. The authors of [23] derived the estimate of the finite population total under Ranked Set Sampling Without Replacement (RSSWOR), employing the model relationship, especially Gamma Population Model (GPM), between the study and auxiliary variables. The authors of [24] considered the estimation of the scale parameter of Levy distribution using a ranked set sample; they derived the best linear unbiased estimator and its variance based on a ranked set sample.…”

mentioning

confidence: 99%

Parameter Estimation of the Exponentiated Pareto Distribution Using Ranked Set Sampling and Simple Random Sampling

Khamnei

Meidutė‐Kavaliauskienė

Fathi

et al. 2022

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In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no presence or possibility of a closed-form at the hands of estimators or any other intellectual. The numerical approach is a well-suited one for this study as there has been struggles in achieving it with any other technique. In order to compare the different sampling methods, simulation studies are performed as the main technique. As for the illustrative purposes, analysis of a simulated dataset is desired for the objective of the presentation. The conclusion that we can reach based on these is that the estimators based on the ranked set sample have far better efficiency than the simple random sample at the same sample size.

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On use of Ranked Set Sampling for estimating Super-population Total: Gamma Population Model

Cited by 5 publications

References 44 publications

A Novel Basis Function Approach to Finite Population Parameter Estimation

A Novel Basis Function Approach to Finite Population Parameter Estimation

On use of subsampling of the non-respondents for estimation of distribution function

Parameter Estimation of the Exponentiated Pareto Distribution Using Ranked Set Sampling and Simple Random Sampling

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