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1
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Efficient two-parameter estimator in linear regression model

100%
Dorugade A. V.
Statistics in Transition new series
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2019
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vol. 20
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issue 2
173-185
EN
In this article, two-parameter estimators in linear model with multicollinearity are considered. An alternative efficient two-parameter estimator is proposed and its properties are examined. Furthermore, this was compared with the ordinary least squares (OLS) estimator and ordinary ridge regression (ORR) estimators. Also, using the mean squares error criterion the proposed estimator performs more efficiently than OLS estimator, ORR estimator and other reviewed two-parameter estimators. A numerical example and simulation study are finally conducted to illustrate the superiority of the proposed estimator.
2
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A Ratio-Cum-Product Estimator of Finite Population Mean in Systematic Sampling

100%
Tailor R., Jatwa N. K., Singh H. P.
Statistics in Transition new series
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2014
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vol. 15
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issue 3
391-398
EN
In this paper we consider the problem of estimation of population mean using information on two auxiliary variables in systematic sampling. We have extended Singh (1967) estimator for estimation of population mean in systematic sampling. We have derived the expressions for the bias and mean squared error of the suggested estimator up to the first degree of approximation. We have compared the suggested estimator with existing estimators and obtained the conditions under which the suggested estimator is more efficient. An empirical study has been carried out to demonstrate the performance of the suggested estimator.
3
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Almost Unbiased Ratio and Product Type Exponential Estimators

100%
Yadav R., Upadhyaya L. N., Singh H. P., Chatterjee S.
Statistics in Transition new series
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2012
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vol. 13
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issue 3
537-550
EN
This paper considers the problem of estimating the population mean Y of the study variate y using information on auxiliary variate x. We have suggested a generalized version of Bahl and Tuteja (1991) estimator and its properties are studied. It is found that asymptotic optimum estimator (AOE) in the proposed generalized version of Bahl and Tuteja (1991) estimator is biased. In some applications, biasedness of an estimator is disadvantageous. So applying the procedure of Singh and Singh (1993) we derived an almost unbiased version of AOE. A numerical illustration is given in the support of the present study.
4
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Estimation of Finite Population Mean Using Deciles of an Auxiliary Variable

100%
Subramani J., Kumarapandiyan G.
Statistics in Transition new series
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2013
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vol. 14
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issue 1
75-88
EN
The present paper deals with a class of modified ratio estimators for estimation of population mean of the study variable when the population deciles of the auxiliary variable are known. The biases and the mean squared errors of the proposed estimators are derived and compared with that of existing modified ratio estimators for certain known populations. Further, we have also derived the conditions for which the proposed estimators perform better than the existing modified ratio estimators. From the numerical study it is also observed that the proposed modified ratio estimators perform better than the existing modified ratio estimators.
5
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Improved separate ratio and product exponential type estimators in the case of post-stratification

100%
Lone H. A., Tailor R.
Statistics in Transition new series
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2015
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vol. 16
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issue 1
53–64
EN
This paper addressed the problem of estimation of finite population mean in the case of post-stratification. Improved separate ratio and product exponential type estimators in the case of post-stratification are suggested. The biases and mean squared errors of the suggested estimators are obtained up to the first degree of approximation. Theoretical and empirical studies have been done to demonstrate better efficiencies of the suggested estimators than other considered estimators.
6
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Noise and bias - some controversies raised by the book 'Noise: A Flaw in Human Judgment', written by Daniel Kahneman, Olivier Sibony, Cass R. Sunstein

100%
Szreder M.
Przegląd Statystyczny
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2022
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vol. 69
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issue 1
39-49
EN
The paper reviews and discusses the statistical aspects of the phenomenon called 'noise' which Daniel Kahneman, the Nobel Prize winning psychologist, and his colleagues present in their new book entitled 'Noise: A Flaw in Human Judgment'. Noise is understood by the authors as an unexpected and undesirable variation present in people's judgments. The variability of judgments influences decisions which are made on the basis of those judgments and, consequently, may have a negative impact on the operations of various institutions. This is the main concern presented and analyzed in this book. The objective of this paper is to look at the relationship between bias and noise - the two major components of the mean squared error (MSE) - from a different perspective which is absent in the book. Although the author agrees that each of the two components contributes equally to MSE, he claims that in some circumstances a reduction of noise can make accurate inference not less, but more difficult. It is justified that the actual impact of noise cannot be accurately determined without considering both bias and noise simultaneously.
7
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Efficient estimation of population mean in the presence of non-response and measurement error

88%
Tiwari K. K., Sharma V.
Statistics in Transition new series
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2023
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vol. 24
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issue 3
95-116
EN
In real-world surveys, non-response and measurement errors are common, therefore studying them together seems rational. Some population mean estimators are modified and studied in the presence of non-response and measurement errors. Bias and mean squared error expressions are derived under different cases. For all estimators, a theoretical comparison is made with the sample mean per unit estimator. The Monte-Carlo simulation is used to present a detailed picture of all estimators' performance.
8
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Optimal allocation for equal probability two-stage design

75%
Molefe W.
Statistics in Transition new series
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2022
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vol. 23
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issue 4
129-148
EN
This paper develops optimal designs when it is not feasible for every cluster to be represented in a sample as in stratified design, by assuming equal probability two-stage sampling where clusters are small areas. The paper develops allocation methods for two-stage sample surveys where small-area estimates are a priority. We seek efficient allocations where the aim is to minimize the linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. We suggest some alternative allocations with a view to minimizing the same objective. Several alternatives, including the area-only stratified design, are found to perform nearly as well as the optimal allocation but with better practical properties. Designs are evaluated numerically using Switzerland canton data as well as Botswana administrative districts data.
9
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An Approximation To The Optimal Subsample Allocation For Small Areas

75%
Molefe W. B., Shangodoyin D. K., Clark R. G.
Statistics in Transition new series
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2015
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vol. 16
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issue 2
163-182
EN
This paper develops allocation methods for stratified sample surveys in which small area estimation is a priority. We assume stratified sampling with small areas as the strata. Similar to Longford (2006), we seek efficient allocation that minimizes a linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. Unlike Longford, we define mean-squared error in a model-assisted framework, allowing a more natural interpretation of results using an intra-class correlation parameter. This allocation has an analytical form for a special case, and has the unappealing property that some strata may be allocated no sample. We derive a Taylor approximation to the stratum sample sizes for small area estimation using composite estimation giving priority to both small area and national estimation.
10
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Power ratio cum median-based ratio estimator of finite population mean with known population median

75%
Abdullahi U. K., Ugwuowo F. I., Lawson N.
Statistics in Transition new series
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2023
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vol. 24
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issue 5
35-44
EN
The search for an efficient estimator of the finite population mean has been a critical problem to the sample survey research community. This study is motivated by the fact that the conducted literature review showed that no research has developed such an average ratio estimator of the population mean that would utilize both the population and the sample medians of study variable, as well as the Srivastava (1967) estimator at a time. In this paper we proposed the power ratio cum median-based ratio estimator of the finite population mean, which is a function of two ratio estimators in the form of an average. The estimator assumes the population to be homogeneous and skewed. The properties (i.e. the Bias and the Mean Squared Error - MSE) of the proposed estimator were derived alongside its asymptotically optimum MSE. We demonstrated the efficiency of the proposed estimator jointly with its efficiency conditions by comparing it to selected estimators described in the literature. Empirically, a real-life dataset from the literature and a simulation study from two skewed distributions (Gamma and Weibull) were used to examine the efficiency gain. The empirical analysis and simulation study demonstrated that the efficiency gain is significant. Hence, the practical application of the proposed estimator is recommended, especially in socio-economic surveys.
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