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Journal articles on the topic 'Ratio Type Estimators'

Author: Grafiati
Published: 8 September 2023

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1

Ijaz, Muhammad, Syed Muhammad Asim, Atta ullah, and Ibrahim Mahariq. "Flexible Robust Regression-Ratio Type Estimators and Its Applications." Mathematical Problems in Engineering 2022 (September 28, 2022): 1–6. http://dx.doi.org/10.1155/2022/8977392.

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In real-world situations, the data set under examination may contain uncommon noisy measurements that unreasonably affect the data’s outcome and produce incorrect model estimates. Practitioners employed robust-type estimators to reduce the weight of the noisy measurements in a data set in such a scenario. Using auxiliary information that will produce reliable estimates, we have looked at a few flexible robust-type estimators in this study. In order to estimate the population mean, this study presents unique flexible robust regression type ratio estimators that take into account the data from the midrange and interdecile range of the auxiliary variables. Up to the first order of approximate computation, the bias and mean square were calculated. In order to compare the flexibility of the proposed estimator to those of the existing estimators, theoretical conditions were also obtained. We took into account data sets containing outliers for empirical computation, and it was found that the suggested estimators produce results with higher precision than the existing estimators.
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2

Kumari, Chandni, and Ratan Kumar Thakur. "Improved Ratio Type Estimators Using Auxiliary Attribute for Population Variance." International Journal of Science and Research (IJSR) 9, no. 4 (April 5, 2020): 1491–98. http://dx.doi.org/10.21275/sr20426180318.

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3

Raghav, Yashpal Singh. "Neutrosophic Generalized Exponential Robust Ratio Type Estimators." International Journal of Analysis and Applications 21 (May 1, 2023): 41. http://dx.doi.org/10.28924/2291-8639-21-2023-41.

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Estimators proposed under classical statistics fail if data are vague or indeterminate. Neutrosophic Statistics are the only alternative because its deal with indeterminacy. Extensive reserch has been conducted in this field because of its wide applicability. This study aimed to further develop the theory of neutosophic simple random sampling without replacement. In this study, a generalized neutrosophic exponential robust ratio-type estimator was proposed, and five of its member neutrosophic estimators were developed. Derivations of the bias and Mean Square Error were provided up to the first-order approximation. To demonstrate the high efficiency of the proposed neutrosophic estimators an empirical study on the stock price of Moderna and four simulation studies have been conducted, and the results show that the proposed neutrosophic estimators are more efficient than similar existing ratio type estimators discussed in this paper in neutrosophic as well as classical forms.
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4

Bahl, Shashi, and Meenu Nain. "Difference-cum-ratio type estimators." Journal of Statistics and Management Systems 2, no. 1 (March 1999): 1–8. http://dx.doi.org/10.1080/09720510.1999.10700986.

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5

Tankou, Victoire, and Sudhakar Dharmadhikari. "Improvement of Ratio-Type Estimators." Biometrical Journal 31, no. 7 (1989): 795–802. http://dx.doi.org/10.1002/bimj.4710310705.

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6

Malik, Sachin, and Rajesh Singh. "Some Improved Multivariate-Ratio-Type Estimators Using Geometric and Harmonic Means in Stratified Random Sampling." ISRN Probability and Statistics 2012 (August 26, 2012): 1–7. http://dx.doi.org/10.5402/2012/509186.

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Auxiliary variable is commonly used in survey sampling to improve the precision of estimates. Whenever there is auxiliary information available, we want to utilize it in the method of estimation to obtain the most efficient estimator. In this paper using multiauxiliary information we have proposed estimators based on geometric and harmonic mean. It was also shown that estimators based on harmonic mean and geometric mean are less biased than Olkin (1958) and Singh (1967) estimators under certain conditions. However, the MSE of Olkin (1958) estimator and geometric and harmonic estimators are same up to the first order of approximations.
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7

Subramani, J., and G. Kumarapandiyan. "A Class of Modified Ratio Estimators for Estimation of Population Variance." Journal of Applied Mathematics, Statistics and Informatics 11, no. 1 (May 1, 2015): 91–114. http://dx.doi.org/10.1515/jamsi-2015-0006.

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Abstract In this paper we have proposed a class of modified ratio type variance estimators for estimation of population variance of the study variable using the known parameters of the auxiliary variable. The bias and mean squared error of the proposed estimators are obtained and also derived the conditions for which the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators. Further we have compared the proposed estimators with that of the traditional ratio type variance estimator and existing modified ratio type variance estimators for certain natural populations.
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8

Rather, Khalid Ul Islam, Eda Gizem Koçyiğit, Ronald Onyango, and Cem Kadilar. "Improved regression in ratio type estimators based on robust M-estimation." PLOS ONE 17, no. 12 (December 12, 2022): e0278868. http://dx.doi.org/10.1371/journal.pone.0278868.

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In this article, a new robust ratio type estimator using the Uk’s redescending M-estimator is proposed for the estimation of the finite population mean in the simple random sampling (SRS) when there are outliers in the dataset. The mean square error (MSE) equation of the proposed estimator is obtained using the first order of approximation and it has been compared with the traditional ratio-type estimators in the literature, robust regression estimators, and other existing redescending M-estimators. A real-life data and simulation study are used to justify the efficiency of the proposed estimators. It has been shown that the proposed estimator is more efficient than other estimators in the literature on both simulation and real data studies.
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9

Agbebia, C. S., and E. I. Enang. "Calibration Approach Ratio Estimators of Population Median in Stratified Random Sampling Design." Asian Journal of Probability and Statistics 21, no. 4 (March 29, 2023): 64–77. http://dx.doi.org/10.9734/ajpas/2023/v21i4472.

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Median as a measure of location gives a more robust estimate than the mean when dealing with heavy tailed or skewed distributions. It can also be used in cases of qualitative variables and open end intervals. Calibration, an approach that adjusts the original design weight by incorporating auxiliary information is employed using the chi square distance measure on a ratio median estimator under stratified random sampling to propose some estimators of population median. These proposed estimators are: the regression and ratio-type calibrated estimators with one constraint and the regression and ratio-type calibrated estimators with two constraints. The estimators of variance of these proposed estimators are also obtained. Empirical investigations on the performance of these estimators are carried out using R software simulated data set under underlying distributional assumptions of Cauchy and Lognormal, for sample sizes of 10%, 20% and 25%. The results showed that the proposed regression and ratio-type calibrated estimators with one constraint and the regression-type calibrated estimator with two constraints were more efficient than the existing ratio estimator and the proposed ratio-type calibrated estimator under two constraints for both Cauchy and the lognormal distributions.
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10

IJAZ, MUHAMMAD, ATTA ULLAH, and TOLGA ZAMAN. "ON THE DEVELOPMENT OF RATIO TYPE ESTIMATORS USING AUXILIARY INFORMATION." Journal of Science and Arts 21, no. 1 (March 30, 2021): 163–70. http://dx.doi.org/10.46939/j.sci.arts-21.1-a14.

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The paper produces some new modified forms of the ratio estimators using the auxiliary information. The large sample properties, that is, the bias and mean squared error up to the first order of approximation are determined. The comparison is made with other existing estimators by using an applied data. It has been observed that the proposed estimators have a fewer mean squared error and leads to the efficient results as compared to the classical ratio estimator, Sisodia and Dwivedi, Singh and Kakran, Upadhyaya and Singh estimators.
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11

Shabbir, Javid, Shakeel Ahmed, Aamir Sanaullah, and Ronald Onyango. "Measuring Performance of Ratio-Exponential-Log Type General Class of Estimators Using Two Auxiliary Variables." Mathematical Problems in Engineering 2021 (October 1, 2021): 1–12. http://dx.doi.org/10.1155/2021/5245621.

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In this paper, a ratio-exponential-log type general class of estimators is proposed in estimating the finite population mean using two auxiliary variables when population parameters of the auxiliary variables are known. From the proposed estimator, some special estimators are identified as members of the proposed general class of estimators. The mean square error (MSE) expressions are obtained up to the first order of approximation. This study finds that the proposed general class of estimators outperforms as compared to the conventional mean estimator, usual ratio estimators, exponential-ratio estimators, log-ratio type estimators, and many other competitor regression type estimators. Four real-life applications are used for efficiency comparison.
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12

Izunobi, Chinyeaka Hostensia, and Aloysius Chijioke Onyeka. "Logarithmic Ratio and Product-type Estimators of Population Mean." International Journal of Advanced Statistics and Probability 7, no. 2 (November 21, 2019): 47. http://dx.doi.org/10.14419/ijasp.v7i2.26943.

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Based on the natural logarithm of known population mean of an auxiliaryvariable, x, the study introduces logarithmic ratio and product-type estimatorsof the population mean of the study variable, y, in simple random samplingwithout replacement (SRSWOR) scheme. Part of the eciency conditions forthe proposed logarithmic estimators to be more ecient than the existing ex-ponential ratio and product-type estimators, as well as the customary ratio andproduct-type estimators, is that the natural logarithm of the known populationmean of the auxiliary variable, x, must be greater than 2. Generally, there is ahigh tendency for the proposed logarithmic estimators to be more ecient thanexisting customary and exponential ratio and product-type estimators whenthe natural logarithm of the auxiliary variable population mean is greater than2. The theoretical results are illustrated and conrmed using some numericaldatasets.
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13

Bahl, Shashi, and R. K. Tuteja. "Ratio and Product Type Exponential Estimators." Journal of Information and Optimization Sciences 12, no. 1 (January 1991): 159–64. http://dx.doi.org/10.1080/02522667.1991.10699058.

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14

Sisodia, B. V. S., and Sunil Kumar. "Comparison of some ratio-type estimators." Microelectronics Reliability 30, no. 5 (January 1990): 873–76. http://dx.doi.org/10.1016/0026-2714(90)90555-2.

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15

Dharmadhikari, S., and V. Tankou. "Improvement of Ratio-Type Estimators-II." Biometrical Journal 33, no. 6 (1991): 705–10. http://dx.doi.org/10.1002/bimj.4710330611.

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16

Shaheen, Nazia, Muhammad Nouman Qureshi, Osama Abdulaziz Alamri, and Muhammad Hanif. "Optimized inferences of finite population mean using robust parameters in systematic sampling." PLOS ONE 18, no. 1 (January 23, 2023): e0278619. http://dx.doi.org/10.1371/journal.pone.0278619.

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In this article, we have proposed a generalized estimator for mean estimation by combining the ratio and regression methods of estimation in the presence of auxiliary information using systematic sampling. We incorporated some robust parameters of the auxiliary variable to obtain precise estimates of the proposed estimator. The mathematical expressions for bias and mean square error of proposed the estimator are derived under large sample approximation. Many other generalized ratio and product-type estimators are obtained from the proposed estimator using different choices of scalar constants. Some special cases are also discussed in which the proposed generalized estimator reduces to the usual mean, classical ratio, product, and regression type estimators. Mathematical conditions are obtained for which the proposed estimator will perform more precisely than the challenging estimators mentioned in this article. The efficiency of the proposed estimator is evaluated using four populations. Results showed that the proposed estimator is efficient and useful for survey sampling in comparison to the other existing estimators.
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17

Yunusa, Mojeed Abiodun, Jamiu Olasunkanmi Muili, Ahmed Audu, and Ran Vijay Kumar Singh. "A Sine Type Median Based Estimator for the Estimation of Population Mean." Oriental Journal of Physical Sciences 8, no. 1 (July 15, 2023): 21–26. http://dx.doi.org/10.13005/ojps08.01.05.

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In the literature, there are numerous estimators for estimating population means when auxiliary information is provided. Subramani suggested ratio median based estimator when the median of the study variable is available and the regression estimator was shown to be significantly less efficient than the estimator. In this research, we suggested an estimator for the population mean of the studied variable based on a sine type median. Using Taylor series expansion, the bias and mean square error of the estimator were obtained up to the first order of approximation. The condition under which the proposed estimator is more efficient than the existing estimators was established. An empirical investigation was done to compare the suggested estimator's efficiency to that of the existing estimators, and the numerical findings showed that the proposed estimator is more efficient.
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18

Khan, Mursala, and Rajesh Singh. "Estimation of Population Mean in Chain Ratio-Type Estimator under Systematic Sampling." Journal of Probability and Statistics 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/248374.

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A chain ratio-type estimator is proposed for the estimation of finite population mean under systematic sampling scheme using two auxiliary variables. The mean square error of the proposed estimator is derived up to the first order of approximation and is compared with other relevant existing estimators. To illustrate the performances of the different estimators in comparison with the usual simple estimator, we have taken a real data set from the literature of survey sampling.
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19

Bhushan, Shashi, Anoop Kumar, Amer Ibrahim Al-Omari, and Ghadah A. Alomani. "Mean Estimation for Time-Based Surveys Using Memory-Type Logarithmic Estimators." Mathematics 11, no. 9 (April 30, 2023): 2125. http://dx.doi.org/10.3390/math11092125.

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This article examines the issue of population mean estimation utilizing past and present data in the form of an exponentially weighted moving average (EWMA) statistic under simple random sampling (SRS). We suggest memory-type logarithmic estimators and derive their properties, such as mean-square error (MSE) and bias up to a first-order approximation. Using the EWMA statistic, the conventional and novel memory-type estimators are compared. Real and artificial populations are used as examples to illustrate the theoretical findings. According to the empirical findings, memory-type logarithmic estimators are superior to the conventional mean estimator, ratio estimator, product estimator, logarithmic-type estimator, and memory-type ratio and product estimators.
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20

Rabiu, Adam, Abubakar Yahaya, and Muhammad Abdulkarim. "MODIFICATION OF SEPARATE RATIO TYPE EXPONENTIAL ESTIMATOR: A POST-STRATIFICATION APPROACH." FUDMA JOURNAL OF SCIENCES 5, no. 2 (July 10, 2021): 404–12. http://dx.doi.org/10.33003/fjs-2021-0502-629.

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In this research, modification of separate ratio type exponential estimator introduced in an earlier study is proposed. Expressions for the bias and mean square error (MSE) of the proposed estimator up to first degree of approximation are derived. The optimum value of the constant which minimize the MSE of the suggested estimator is also obtained. In the same vein, efficiency comparisons between the proposed estimator and some related existing ones under the case of post-stratification is conducted. Empirical studies have been conducted to demonstrate the efficiencies of the suggested estimators over other considered estimators. The proposed MSE and Percentage Relative Efficiency (PRE) were used to evaluate the achievement of the modified estimator.
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21

Mittal, Alisha, and Manoj Kumar. "Two new estimators of finite population variance under random non-response: An application." Model Assisted Statistics and Applications 18, no. 1 (March 24, 2023): 73–84. http://dx.doi.org/10.3233/mas-221361.

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In this paper, an attempt has been made to study the estimation of finite population variance in simple random sampling without replacement (SRSWOR) in presence of non-response by proposing two estimators. The two estimators (dual to ratio and ratio cum dual to ratio type) have been proposed on the basis of availability and non-availability of auxiliary information. The properties such as bias and mean square error of the proposed estimators have also been studied up to first order of approximation. The proposed estimators are compared with some existing estimators and are also mutually compared. An empirical study, based on both vegetable crop data and simulated data, has been performed to find out the best estimator. It has been seen that out of the two proposed estimators, the ratio cum dual to ratio type estimator performed better than dual to ratio estimator.
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22

Zaman, Tolga, Emre Dünder, Ahmed Audu, David Anekeya Alilah, Usman Shahzad, and Muhammad Hanif. "Robust Regression-Ratio-Type Estimators of the Mean Utilizing Two Auxiliary Variables: A Simulation Study." Mathematical Problems in Engineering 2021 (September 3, 2021): 1–9. http://dx.doi.org/10.1155/2021/6383927.

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Many authors defined the modified version of the mean estimator by using two auxiliary variables. These proposed estimators highly depend on the calculated regression coefficients. In the presence of outliers, these estimators do not give satisfactory results. In this study, we improve the suggested estimators using several robust regression techniques while obtaining the regression coefficients. We compared the efficiencies between the suggested estimators and the estimators presented in the literature. We used two numerical examples and a simulation study to support these theoretical results. Empirical results show that the modified ratio estimator performs well in the presence of outliers when adopting robust regression techniques.
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23

Upadhyaya, Lakshmi N., Housila P. Singh, S. Chatterjee, and Rohini Yadav. "Improved Ratio and Product Exponential Type Estimators." Journal of Statistical Theory and Practice 5, no. 2 (June 2011): 285–302. http://dx.doi.org/10.1080/15598608.2011.10412029.

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24

Kamal, Asifa, Nimra Amir, and Huma Dastagir. "Some Exponential Type Predictive Estimators of Finite Population Mean in Two-Phase Sampling." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 2, no. 1 (June 30, 2020): 51–57. http://dx.doi.org/10.52700/scir.v2i1.10.

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This study is designed for predictive estimation of finite population mean in two phase sampling using two auxiliary variables. Two phase exponential ratio type estimator and exponential chain ratio type estimator are proposed under predictive approach suggested by Bahl & Tuteja (1991). The expressions of bias and mean square error of both suggested estimators have been carried out for theoretical comparison. The numerical study on real life data sets as well as simulation study has been conducted to examine the performance of suggested estimators. Finally, it is demonstrated that the suggested exponential ratio estimators are more efficient than competitive estimators in support of numerical and simulation study as well.
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25

Onyango, Ronald, Brian Oduor, and Francis Odundo. "Mean Estimation of a Sensitive Variable under Nonresponse Using Three-Stage RRT Model in Stratified Two-Phase Sampling." Journal of Probability and Statistics 2022 (April 22, 2022): 1–14. http://dx.doi.org/10.1155/2022/4530120.

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The present study addresses the problems of mean estimation and nonresponse under the three-stage RRT model. Auxiliary information on an attribute and variable is used to propose a generalized class of exponential ratio-type estimators. Expressions for the bias, mean squared error, and minimum mean squared error for the proposed estimator are derived up to the first degree of approximation. The efficiency of the proposed estimator is studied theoretically and numerically using two real datasets. From the numerical analysis, the proposed generalized class of exponential ratio-type estimators outperforms ordinary mean estimators, usual ratio estimators, and exponential ratio-type estimators. Furthermore, the efficiencies of the mean estimators are observed to decrease with an increase in the sensitivity level of the survey question. As the inverse sampling rate and nonresponse rate go up, so does the efficiency of the mean estimators, which makes them more accurate.
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26

Dar, Amber Yousaf, Nadia Saeed, Moustafa Omar Ahmed Abu-Shawiesh, Saman Hanif Shahbaz, and Muhammad Qaiser Shahbaz. "A new class of ratio type estimators in single- and two-phase sampling." AIMS Mathematics 7, no. 8 (2022): 14208–26. http://dx.doi.org/10.3934/math.2022783.

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<abstract> <p>The estimation of a certain population characteristics is required for several situations. The estimates are built so that the error of estimation is minimized. In several situations estimation of the population mean is required. Different estimators for the mean are available but, there is still room for improvement. In this paper, a new class of ratio-type estimators is proposed for the estimation of the population mean. The estimators are proposed for single- and two-phase sampling schemes. The expressions for bias and mean square error are obtained for single-phase and two-phase sampling estimators. Mathematical comparison of the proposed estimators has been achieved by using some existing single-phase and two-phase sampling estimators. Extensive simulations have been conducted to compare the proposed estimators with some available single- and two-phase sampling estimators. It has been observed that the proposed estimators are better than the existing estimators. Consequently, the proposed ratio estimators are recommended for use by the practitioners in various fields of industry, engineering and medical and physical sciences.</p> </abstract>
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27

Yunusa, Mojeed Abiodun, Ahmed Audu, and Awwal Adejumobi. "Logarithmic-Product-Cum-Ratio Type Estimator for Estimating Finite Population Coefficient of Variation." Oriental Journal of Physical Sciences 7, no. 2 (January 10, 2023): 82–87. http://dx.doi.org/10.13005/ojps07.02.05.

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Estimation of population parameters have been a challenging aspect in sample survey for sometimes and many efforts have been made to enhance the exactness of the parameters of these estimators. We suggested the logarithmic-product-cum-ratio type estimator. Expression of the MSE of the intended estimator originated using the Taylor series technique. A numerical illustration was conducted and the results revealed that the modified work is ameliorated than sample means with other estimators observed.
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28

Rather, K. Ul Islam, M. Iqbal Jeelani, M. Younis Shah, S. E. H. Rizvi, and M. Sharma. "A new ratio type estimator for computation of population mean under post-stratification." Journal of Applied Mathematics, Statistics and Informatics 18, no. 1 (May 1, 2022): 29–42. http://dx.doi.org/10.2478/jamsi-2022-0003.

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Abstract In this study, the difficulty of estimating the population mean in the situation of post-stratification is discussed. The case of post-stratification is presented for ratio-type exponential estimators of finite population mean. Mean-squared error of the proposed estimator is obtained up to the first degree of approximation. In the instance of post-stratification, the proposed estimator was compared with the existing estimators. An empirical study by using some real data and further, simulation study has been carried out to demonstrate the performance of the proposed estimator.
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29

Muhammad, Isah, Yahaya Zakari, and Ahmed Audu. "An Alternative Class of Ratio-Regression-Type Estimator under Two-Phase Sampling Scheme." Central Bank of Nigeria Journal of Applied Statistics 12, no. 2 (March 30, 2022): 1–26. http://dx.doi.org/10.33429/cjas.12221.1/5.

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In this study, a new exponential ratio-regression estimator is developed using an auxiliary variable for estimating the finite population mean under a two-phase sampling system. The Bias and Mean Square Error (MSE) of the proposed estimator are derived and compared with some of the estimators in extant literature. Thus, the conditions under which the proposed estimator is better than some existing estimators are provided. Empirically, using four real datasets and simulation study, the proposed estimator performs better than the classical ratio, classical regression, exponential ratio, and exponential regression cum ratio estimator when compared using the criteria of bias, mean square error and percentage relative efficiency. The proposed estimator can be used to estimate the averages of economic variables such as inflation, exchange rate, and standard of living for policy formulation.
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Muhammad, Isah, Yahaya Zakari, and Ahmed Audu. "An Alternative Class of Ratio-Regression-Type Estimator under Two-Phase Sampling Scheme." Central Bank of Nigeria Journal of Applied Statistics 12, no. 2 (March 30, 2022): 1–26. http://dx.doi.org/10.33429/cjas.12221.1/5.

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In this study, a new exponential ratio-regression estimator is developed using an auxiliary variable for estimating the finite population mean under a two-phase sampling system. The Bias and Mean Square Error (MSE) of the proposed estimator are derived and compared with some of the estimators in extant literature. Thus, the conditions under which the proposed estimator is better than some existing estimators are provided. Empirically, using four real datasets and simulation study, the proposed estimator performs better than the classical ratio, classical regression, exponential ratio, and exponential regression cum ratio estimator when compared using the criteria of bias, mean square error and percentage relative efficiency. The proposed estimator can be used to estimate the averages of economic variables such as inflation, exchange rate, and standard of living for policy formulation.
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31

Muhammad, Isah, Yahaya Zakari, and Ahmed Audu. "An Alternative Class of Ratio-Regression-Type Estimator under Two-Phase Sampling Scheme." Central Bank of Nigeria Journal of Applied Statistics 12, no. 2 (March 30, 2022): 1–26. http://dx.doi.org/10.33429/cjas.12221.1/5.

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In this study, a new exponential ratio-regression estimator is developed using an auxiliary variable for estimating the finite population mean under a two-phase sampling system. The Bias and Mean Square Error (MSE) of the proposed estimator are derived and compared with some of the estimators in extant literature. Thus, the conditions under which the proposed estimator is better than some existing estimators are provided. Empirically, using four real datasets and simulation study, the proposed estimator performs better than the classical ratio, classical regression, exponential ratio, and exponential regression cum ratio estimator when compared using the criteria of bias, mean square error and percentage relative efficiency. The proposed estimator can be used to estimate the averages of economic variables such as inflation, exchange rate, and standard of living for policy formulation.
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32

Oral, Evrim, and Cem Kadilar. "Robust ratio-type estimators in simple random sampling." Journal of the Korean Statistical Society 40, no. 4 (December 2011): 457–67. http://dx.doi.org/10.1016/j.jkss.2011.04.001.

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33

Olasunkanmi Muili, Jamiu, and A. Audu. "Estimation of Finite Population Mean of Median Based Using Power Transformation." Oriental Journal of Physical Sciences 6, no. 1-2 (February 28, 2022): 26–31. http://dx.doi.org/10.13005/ojps06.01-02.05.

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This paper deals with the assessment of finite population mean. An estimator is suggested for estimation of finite population mean of study variable. The purpose of this study is to evolve a ratio-type estimator to enhance the proficiency of the existing estimators considered in the study in sample random sampling without replacement using information of auxiliary variable. Expressions of the bias and mean square error (MSE) of the proposed estimator was derived by Taylor series method. The efficiency conditions under which the proposed ratio-type estimator is better than sample mean, ratio estimator, and other estimators considered in this study have been established. Theoretical and empirical findings are incentive and brace the robustness of the proposed estimator for mean estimation. The empirical results shown that the suggested estimator is more efficient than the sample mean, ratio estimator and other estimators.
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34

Ali, Muhammad, Muhammad Khalil, Muhammad Hanif, Nasir Jamal, and Usman Shahzad. "An Improved Ratio-Type Variance Estimator by Using Linear Combination of Diferent Measures of Location." Oriental Journal of Physical Sciences 3, no. 1 (June 25, 2018): 24–32. http://dx.doi.org/10.13005/ojps03.01.05.

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In this research study, modified family of estimators is proposed to estimate the population variance of the study variable when the population variance, quartiles, median and the coefficient of correlation of auxiliary variable are known. The expression of bias and mean squared error (MSE) of the proposed estimator are derived. Comparisons of the proposed estimator with the other existing are conducted estimators. The results obtained were illustrated numerically by using primary data sets. Theoretical and numerical justification of the proposed estimator was done to show its dominance.
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35

Iftikhar, Soofia, Alamgir Khalil, and Amjad Ali. "A Novel and Improved Logarithmic Ratio-Product Type Estimator of Mean in Stratified Random Sampling." Mathematical Problems in Engineering 2022 (June 8, 2022): 1–20. http://dx.doi.org/10.1155/2022/6179625.

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Population mean is an important characteristic from many perspectives in statistical methodology. In this connection, many separate ratio type estimators of population mean have been developed by the researchers to gain more precision and efficiency available in the literature. This study suggests a new class of logarithmic separate ratio-product type estimators of a finite population mean in stratified random sampling using one auxiliary variable to achieve enhanced precision and efficiency. The mean square error expression for the proposed class of estimators and some selected estimates are derived till first order of approximation. Three real data sets and a simulation study reveal that the proposed estimators outperform other estimators used in this analysis.
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36

Mittal, Alisha, and Manoj Kumar. "GENERALISED EXPONENTIAL RATIO-TYPE ESTIMATOR FOR FINITE POPULATION VARIANCE UNDER RANDOM NON-RESPONSE." International Journal of Advanced Research 9, no. 01 (January 31, 2021): 589–96. http://dx.doi.org/10.21474/ijar01/12332.

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In this research paper an effort has been made for the estimation of population variance of the study variable by using information on certain known parameters of the auxiliary variable under non-response for scheme I and II given by Singh and Joarder (1998). Generalized exponential ratio-type estimator has been proposed and their properties have been studied under non response techniques and conditions were found when the family of proposed estimators identified by using different choices for (P, Q) performed better than the usual unbiased estimator. It was also observed that for different values of α ∈ (0.0, 1.0), the estimators and were found to be best under numerical illustration.
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37

Naz, Farah, Tahir Nawaz, Tianxiao Pang, and Muhammad Abid. "Use of Nonconventional Dispersion Measures to Improve the Efficiency of Ratio-Type Estimators of Variance in the Presence of Outliers." Symmetry 12, no. 1 (December 19, 2019): 16. http://dx.doi.org/10.3390/sym12010016.

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The use of auxiliary information in survey sampling to enhance the efficiency of the estimators of population parameters is a common phenomenon. Generally, the ratio and regression estimators are developed by using the known information on conventional parameters of the auxiliary variables, such as variance, coefficient of variation, coefficient of skewness, coefficient of kurtosis, or correlation between the study and auxiliary variable. The efficiency of these estimators is dubious in the presence of outliers in the data and a nonsymmetrical population. This study presents improved variance estimators under simple random sampling without replacement with the assumption that the information on some nonconventional dispersion measures of the auxiliary variable is readily available. These auxiliary variables can be the inter-decile range, sample inter-quartile range, probability-weighted moment estimator, Gini mean difference estimator, Downton’s estimator, median absolute deviation from the median, and so forth. The algebraic expressions for the bias and mean square error of the proposed estimators are obtained and the efficiency conditions are derived to compare with the existing estimators. The percentage relative efficiencies are used to numerically compare the results of the proposed estimators with the existing estimators by using real datasets, indicating the supremacy of the suggested estimators.
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38

Koç, Tuba, and Haydar Koç. "A New Class of Quantile Regression Ratio-Type Estimators for Finite Population Mean in Stratified Random Sampling." Axioms 12, no. 7 (July 22, 2023): 713. http://dx.doi.org/10.3390/axioms12070713.

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Quantile regression is one of the alternative regression techniques used when the assumptions of classical regression analysis are not met, and it estimates the values of the study variable in various quantiles of the distribution. This study proposes ratio-type estimators of a population mean using the information on quantile regression for stratified random sampling. The proposed ratio-type estimators are investigated with the help of the mean square error equations. Efficiency comparisons between the proposed estimators and classical estimators are presented in certain conditions. Under these obtained conditions, it is seen that the proposed estimators outperform the classical estimators. In addition, the theoretical results are supported by a real data application.
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39

Suleiman, Sikiru A., and Amos A. Adewara. "Improved Modified Ratio Estimation of Population Mean Using Information on Size of the Sample." Tanzania Journal of Science 47, no. 5 (December 29, 2021): 1753–65. http://dx.doi.org/10.4314/tjs.v47i5.22.

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In sample surveys, auxiliary information is used for estimation to improve the efficiency of estimators. Increased precision can be obtained when the variable under study is highly correlated with auxiliary information. In this study, the sample size has been used as information for improved estimation of population mean of the main variable under study. A new modified generalized ratio type estimator of population mean has been proposed and the efficiency was examined using Murthy (1967) and Mukhopadhyay (2009) dataset. The large sample properties, the bias and the mean squared error of the newly proposed modified ratio estimator were obtained up to first order of approximation. The optimum value of the characterizing scalar which minimizes the mean squared error was obtained and the minimum value of the mean squared error of the proposed modified ratio estimator for this optimum value was also obtained. A theoretical comparison of the proposed modified ratio estimators was made with the other existing related estimators of population mean using auxiliary information. The conditions under which the proposed modified ratio estimators perform better than the other existing estimators of population mean are given. A numerical study was also carried out to see the performances of the proposed modified ratio estimators and some existing related ratio estimators of population mean and verify the conditions under which the proposed modified ratio estimators are better than some other existing related ratio estimators considered. It was shown that the proposed modified ratio estimators perform better than some existing related ratio estimators as they are having lower mean squared errors. Keywords: Ratio Estimator, Sample size, Bias, Mean Squared Error, Efficiency
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40

Olufadi, Yunusa, and Cem Kadilar. "A Study on the Chain Ratio-Type Estimator of Finite Population Variance." Journal of Probability and Statistics 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/723982.

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We suggest an estimator using two auxiliary variables for the estimation of the unknown population variance. The bias and the mean square error of the proposed estimator are obtained to the first order of approximations. In addition, the problem is extended to two-phase sampling scheme. After theoretical comparisons, as an illustration, a numerical comparison is carried out to examine the performance of the suggested estimator with several estimators.
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41

Younis, Faryal, and Javid Shabbir. "Generalized ratio-type and ratio-exponential-type estimators for population mean under modified Horvitz-Thompson estimator in adaptive cluster sampling." Journal of Statistical Computation and Simulation 89, no. 8 (March 4, 2019): 1505–15. http://dx.doi.org/10.1080/00949655.2019.1586905.

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42

RATHER, KHALID UL ISLAM, M. IQBAL JEELANI, M. YOUNIS SHAH, AFSHAN TABASSUM, and S. E. H. RIZVI. "DIFFERENCE-CUM-EXPONENTIAL EFFICIENT ESTIMATOR OF POPULATION VARIANCE." Journal of Science and Arts 22, no. 2 (June 30, 2022): 367–74. http://dx.doi.org/10.46939/j.sci.arts-22.2-a10.

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In the current investigation, we have suggested a Difference Cum-Exponential Type Efficient Estimator of Population variance of the study variable using information on the auxiliary variable. Up to the first order of approximation, the proposed estimator's bias and mean square error (MSE) expressions are derived and suggested optimum estimator is also found, with its optimal qualities are investigated. The suggested estimator is proven to be more competent than sample variance, classic ratio estimators based on Isaki , Singh et al. and Kadilar and Cingi estimators in [1-3]. Numerical study is also carried out by using real data sets.
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43

Magnussen, Steen, and Thomas Nord-Larsen. "A Jackknife Estimator of Variance for a Random Tessellated Stratified Sampling Design." Forest Science 65, no. 5 (March 12, 2019): 543–47. http://dx.doi.org/10.1093/forsci/fxy070.

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Abstract Semisystematic sampling designs—in which a population area frame is tessellated into cells, and a randomly located sample is taken from each cell—affords random tessellated stratified (RTS) Horvitz–Thompson-type estimators. Forest inventory applications with RTS estimators are rare, possibly because of computational complexities with the estimation of variance. To reduce this challenge, we propose a jackknife estimator of variance for RTS designs. We demonstrate an application with a model-assisted ratio of totals estimator and data from the Danish National Forest Inventory. RTS estimators of standard error were, as a rule, smaller than comparable estimates obtained under the assumption of simple random sampling. The proposed jackknife estimator performed well.
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44

Swain, A. K. P. C., and Priyaranjan Dash. "On a Class of Almost Unbiased Ratio Type Estimators." Journal of Statistical Theory and Applications 19, no. 1 (2020): 28. http://dx.doi.org/10.2991/jsta.d.200224.007.

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45

Jhajj, H. S., Harpreet Kaur, and Gurjeet Walia. "Efficient family of ratio-product type estimators of median." Model Assisted Statistics and Applications 9, no. 4 (October 8, 2014): 277–82. http://dx.doi.org/10.3233/mas-140299.

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46

Tahir, Muhammad, Bu Yude, Saima Bashir, Sardar Hussain, and Tahir Munir. "A new improved estimator for the population mean using twofold auxiliary information under simple random sampling." Management Science Letters 13, no. 4 (2023): 265–76. http://dx.doi.org/10.5267/j.msl.2023.5.001.

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In this manuscript, the mean of the study and the auxiliary variable, as well as the rank of the auxiliary variable, were needed to develop a new, improved ratio-in-regression type estimator for population mean. Up to the first order of approximation, expressions for the bias and mean square error of the existing and proposed estimators are computed. The effectiveness and stability of our new, enhanced estimator are evaluated using simulation and two actual data sets. The suggested estimator's superior performance to all other considered estimators is shown both conceptually and numerically. The mean square error is the lowest, and PREs out-performs other known estimators by a factor of more than one hundred. Overall, we draw the conclusion that the suggested new improved estimator outperforms all its predecessors.
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47

Ahmad, Sohaib, Sardar Hussain, Uzma Yasmeen, Muhammad Aamir, Javid Shabbir, M. El-Morshedy, Afrah Al-Bossly, and Zubair Ahmad. "A simulation study: Using dual ancillary variable to estimate population mean under stratified random sampling." PLOS ONE 17, no. 11 (November 28, 2022): e0275875. http://dx.doi.org/10.1371/journal.pone.0275875.

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In this paper, we propose an improved ratio-in-regression type estimator for the finite population mean under stratified random sampling, by using the ancillary varaible as well as rank of the ancillary varaible. Expressions of the bias and mean square error of the estimators are derived up to the first order of approximation. The present work focused on proper use of the ancillary variable, and it was discussed how ancillary variable can improve the precision of the estimates. Two real data sets as well as simulation study are carried out to observe the performances of the estimators. We demonstrate theoretically and numerically that proposed estimator performs well as compared to all existing estimators.
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48

Muili, Jamiu Olasunkanmi, Ran Vijay Kumar Singh, Gerald Ikechukwu Onwuka, and Ahmed Audu. "Calibration of Stratified Random Sampling with Combined Ratio Estimators." Oriental Journal of Physical Sciences 7, no. 1 (August 30, 2022): 30–36. http://dx.doi.org/10.13005/ojps07.01.05.

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This study considered modification of combined ratio type calibration estimators in stratified random sampling using calibration estimation approaches. The estimators of population mean in stratified random sampling depends on the strata estimated sample means. However, the means are sensitive to the extreme values or outliers in the sample observations of the study variables and strata sizes respectively. A new sets of calibration weights and property of the suggested combined calibration estimators of population mean in stratified sampling were derived. Empirical study through simulation was conducted to investigate the efficiency of the modified combined ratio-type calibration estimators of population mean obtained and the results revealed that the suggested estimators of population mean performed better than some existing estimators considered in the study.
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49

Pandey, R., K. Yadav, and N. S. Thakur. "Adapted Factor-Type Imputation Strategies." Journal of Scientific Research 8, no. 3 (September 1, 2016): 321–39. http://dx.doi.org/10.3329/jsr.v8i3.27804.

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The present paper provides alternative improved Factor-Type (F-T) estimators of population mean in presence of item non-response for the practitioners. The proposed estimators have been shown to be more efficient than the four existing estimators which are more efficient than the usual ratio and the mean estimators. Optimum conditions for minimum mean squared error are obtained for the new estimators. Empirical comparisons based on three different data sets establish that the proposed estimators record least mean squared error and hence a substantial gain in Percentage Relative Efficiency (P.R.E.), over these five contemporary estimators.
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50

Mandowara, V. L., and Nitu Mehta. "Efficient Generalized Ratio-Product Type Estimators for Finite Population Mean with Ranked Set Sampling." Austrian Journal of Statistics 42, no. 3 (February 24, 2016): 137–48. http://dx.doi.org/10.17713/ajs.v42i3.147.

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In this paper we suggest two modified estimators of the population mean using the power transformation based on ranked set sampling (RSS). The first order approximation of the bias and of the mean squared error of the proposed estimators are obtained. A generalized version of the suggested estimators by applying the power transformation is also presented. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in simple random sampling (SRS). A numerical illustration is also carried out to demonstrate the merits of the proposed estimators using RSS over the usual estimators in SRS.
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