EFFICIENCY OF RANKED SET SAMPLING IN HORTICULTURAL SURVEYS

<p>DOI: 10.12957/cadest.2015.19114</p> <p>Abstract</p> <p>In this paper, we explore the feasibility of using RSS (Ranked Set Sampling) in improving the estimates of the population mean in comparison  to SRS (Simple Random Sampling) in Horticultural research. We use an experience developed with a survey of apples in India. The numerical results suggest that RSS procedure results in a substantial reduction of standard errors, and  thus provides more efficient estimates than SRS, in the  specific Horticultural Survey studied, using the same sample size. Then it is recommended as an easy-to-use accurate method to management of this Horticulture problem.</p> <p>Key-words: Ranked Set Sampling, Simple Random Sampling, Standard Error, Accuracy.</p><p><em> </em></p><p><em> </em></p><p><em> </em></p><p><em> </em></p><p> </p

Rank Set Sampling in Improving the Estimates of Simple Regression Model

<p>In this paper Rank set sampling (RSS) is introduced with a view of increasing the efficiency of estimates of Simple regression model. Regression model is considered with respect to samples taken from sampling techniques like Simple random sampling (SRS), Systematic sampling (SYS) and Rank set sampling (RSS). It is found that R² and Adj R²obtained from regression model based on Rank set sample is higher than rest of two sampling schemes. Similarly Root mean square error, p-values, coefficient of variation are much lower in Rank set based regression model, also under validation technique (Jackknifing) there is consistency in the measure of R², Adj R² and RMSE in case of RSS as compared to SRS and SYS. Results are supported with an empirical study involving a real data set generated of <em>Pinus Wallichiana</em> taken from block Langate of district Kupwara. </p

Obtaining Strata Boundaries under Proportional Allocation with Varying Cost of Every Unit

One of the main reasons for stratifying the population is to produce a gain in precision of the estimates, in the sample surveys. For achieving this, one of the problem is determination of optimum strata boundaries. The strata boundaries should be obtained in such a way, so that it can reasonably expect to reduce the cost of the survey as much as possible without sacrificing the accuracy or alternatively, reducing the margin of error to the greatest possible extent for the same expected cost. In this paper, we have discussed the way of obtaining optimum strata boundaries when the cost of every unit varies in the whole strata. The problem is formulated as non-linear programming problem which is solved by using Bellman’s principle of optimality. For numerical illustration an example is presented for uniformly distributed study variable

New Exponential Ratio Estimator in Ranked Set Sampling

In this study, we adapted the families of estimators from Ünal and Kadilar (2021) using the exponential function for the population mean in case of non-response for simple random sampling for the estimation of the mean of the population with the RSS (ranked set sampling) method. The equations for the MSE (mean square error) and the bias of the adapted estimators are obtained for&nbsp; RSS&nbsp; and it, in theory, shows that the proposed estimator is additional efficient than the present RSS mean estimators in the literature.&nbsp; In addition, we support these theoretical results with real COVID-19 real data and conjointly the simulation studies with different distributions and parameters. As a result of the study, it was observed that the efficiency of the proposed estimator was better than the other estimators

ISSN: 2454-132X Impact factor: 4.295 A Statistical study on Network Service Providers in Kashmir

Abstract-Survey methodology studies the sampling of individual from a population and data collection with a view towards making statistical inferences about the population represented by the sample and the constructs represented by the measures. In this paper, data has been collected from different colleges related to network service providers and statistical analysis has been made in MINITAB with many conclusions like the students belonging to rural are more using cell phones rather than urban students. Not only this but also, majority of the students are of the opinion that mobile has a large bad effect on children. The graphical representation has been given with respective contents included in the paper

A New-Fangled Ratio-Type Exponential Estimator for Population Variance using Auxiliary Information

This manuscript provides new exponential ratio type estimator in simple random sampling for estimating the population variance using auxiliary information. The key purpose of this paper is to propose a new estimator and to increase the efficiency of the estimator for the population variance. The proposed exponential product-type estimator’s bias and mean square error expressions have been derived. The optimum value of the characterizing scalar has been found, which minimizes the MSE of the proposed estimator. The proposed estimator was theoretically compared to competing estimators. It is shown that the proposed estimator outperforms its competitors. To demonstrate the practical use of different estimation formulae and empirically demonstrate the efficiency of the constructed estimators, a numerical analysis is conducted using real data sets

Modified Regression Estimators for Improving Mean Estimation -Poisson Regression Approach

In this article, a class of Poisson-regression based estimators has been proposed for estimating the finite population mean in simple random sampling without replacement (SRSWOR). The Poisson-regression model is the most common method used to model count responses in many studies. The expression for bias and mean square error (MSE) of proposed class of estimators are obtained up to first order of approximation. The proposed estimators have been compared theoretically with the existing estimators, and the condition under which the proposed class of estimators perform better than existing estimators have been obtained. Two real data sets are considered to assess the performance of the proposed estimators. Numerical findings confirms that the proposed estimators dominate over the existing estimators such as Koc (2021) and Usman et al. (2021) in terms of mean squared error

Evaluation of Ranked Set Sampling Methods under Skewed and Unskewed Distributions

The use of ranked set sampling (RSS), a unique and cost-effective sampling technique, is appropriate when determining units is simple and straightforward. The estimates based on RSS are more effective than the conventional unconstrained sampling approaches, and in recent years, their modified versions have gained popularity because of their remarkably increased effectiveness in research projects on forest management. In light of this, the current study was conducted on skewed and unskewed distributions to assess the efficacy of the various modified versions of RSS. Simulated data was produced in R Studio (version 4.1.2 – 2021) using skewed and unskewed probability distributions like the gamma, exponential, uniform, and normal distributions to fulfil the specified goals. In accordance with this, a ranked set sample of sizes 150, 300, 450, 600, 750, 900, 1050, and 1200 with a set size of 3, 6, 9, 12, 15, 18, and 24 using a constant cycle (r) of 50 was drawn from the simulated data using various modified RSS techniques, including: Extreme ranked set sampling (ERSS), Median ranked set sampling (MRSS), Percentile rank set sampling (PRSS), Balanced grouped ranked set sampling (BGRSS), Balanced grouped ranked set sampling (BGRSS), and Truncation based rank set sampling (TBRSS)by means of library( RS Sampling) of R Studio. Since these samples are based on modified versions of RSS that are more regularly spaced and induce stratification at the sample level, which involves a gain in efficiency, it can be seen from the results that modified versions of RSS performed better in unskewed distributions in comparison to skewed distributions in terms of efficiency. Additionally, it was discovered that the effectiveness of every modified RSS algorithm rises as the sample size does. According to empirical research using simulated data, truncation-based rank set sampling (TBRSS), among the modified RSS methods, outperformed its competitors in terms of efficiency. The value of AIC & BIC reduced as the set size across the modified RSS methods increased, according to the goodness of fit results, showing that less information is lost as set size increases. Finally, it can be said that RSS has practical ramifications, and that R packages greatly aid in the implementation of modified RSS methods, which are quite informative and suitable to sample surveys