Log-extended exponential-geometric parameters estimation using simple random sampling and moving extremes ranked set sampling

2020 ◽  
pp. 1-15
Author(s):  
Rui Yang ◽  
Wangxue Chen ◽  
Yanfei Dong
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Parameters Estimation ◽  
Geometric Parameters

Stratified Ranked Set Sampling With Missing Observations for Estimating the Difference

2022 ◽  
pp. 209-232
Author(s):  
Carlos N. Bouza-Herrera
Keyword(s):  
Random Sampling ◽  
Real Data ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Missing Observations ◽  
Seminal Paper ◽  
Sample Error ◽  
The Difference ◽  
Stratified Ranked Set Sampling ◽  
Stratified Model

The authors develop the estimation of the difference of means of a pair of variables X and Y when we deal with missing observations. A seminal paper in this line is due to Bouza and Prabhu-Ajgaonkar when the sample and the subsamples are selected using simple random sampling. In this this chapter, the authors consider the use of ranked set-sampling for estimating the difference when we deal with a stratified population. The sample error is deduced. Numerical comparisons with the classic stratified model are developed using simulated and real data.

Ratio-Type Estimation Using Scrabled Auxiliary Variables in Stratification Under Simple Random Sampling and Ranked Set Sampling

2022 ◽  
pp. 62-85
Author(s):  
Carlos N. Bouza-Herrera ◽  
Jose M. Sautto ◽  
Khalid Ul Islam Rather
Keyword(s):  
Random Sampling ◽  
Mean Squared Error ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Auxiliary Variables ◽  
Mild Conditions ◽  
Squared Error ◽  
The Mean ◽  
Better Than

This chapter introduced basic elements on stratified simple random sampling (SSRS) on ranked set sampling (RSS). The chapter extends Singh et al. results to sampling a stratified population. The mean squared error (MSE) is derived. SRS is used independently for selecting the samples from the strata. The chapter extends Singh et al. results under the RSS design. They are used for developing the estimation in a stratified population. RSS is used for drawing the samples independently from the strata. The bias and mean squared error (MSE) of the developed estimators are derived. A comparison between the biases and MSEs obtained for the sampling designs SRS and RSS is made. Under mild conditions the comparisons sustained that each RSS model is better than its SRS alternative.

scholarly journals Estimating Bark Eating Caterpillars Indarbela quadrinotata (walker) in Populus deltoides Using Ranked Set Sampling

2020 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Arvind Kumar ◽  
Girish Chandra ◽  
Sanjay Kumar
Keyword(s):  
Random Sampling ◽  
Populus Deltoides ◽  
Insect Pest ◽  
Uttar Pradesh ◽  
Simple Random Sampling ◽  
Forest Tree ◽  
Ranked Set Sampling ◽  
Actual Measurement ◽  
Mean And Variance ◽  
Cost Efficient

<p>The problem of bark eating caterpillar, <em>Indarbela quadrinotata</em> infestation has been observed from variety of horticulture and forest tree species in India. The estimation of infestation of this caterpillar using conventional sampling methods was found difficult because counting the number of caterpillar in each tree is practically not feasible. Ranked set sampling (RSS) is a cost efficient method which provides improved estimators of mean and variance when actual measurement of the observations is difficult to obtain but a reasonable ranking of the units in the sample is relatively easy. In the present study, poplar, <em>Populus deltoides</em> plantation of Western Uttar Pradesh and Uttarakhand was taken for the assessment of <em>Indarbela quadrinotata</em> infestation. The RSS estimator of population mean and variance have been discussed and compared with the corresponding estimators from simple random sampling (SRS). The relative precision (RP) of RSS procedure with respect to the SRS for four different set sizes of <em>k </em>= 3, 5, 7, and 10 has been deliberated. It was seen that RP increase with the increment in <em>k</em>. The method of RSS was found suitable for the assessment of insect pest infestation.</p><p><strong>Keywords</strong><strong>: </strong><em>Indarbela quadrinotata</em>, <em>Populus deltoides</em>, simple random sampling, ranked set sample, order statistics.</p>

scholarly journals Maximum likelihood estimation in location-scale families using varied L ranked set sampling

2020 ◽  
Author(s):  
Amer Al-Omari
Keyword(s):  
Maximum Likelihood ◽  
Random Sampling ◽  
Maximum Likelihood Estimators ◽  
Likelihood Estimation ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Sampling Schemes ◽  
Scale Parameters ◽  
Insight Into ◽  
Family Of Distributions

Recently, a generalized ranked set sampling (RSS) scheme has been introduced which encompasses several existing RSS schemes, namely varied L RSS (VLRSS), and it provides more precise estimators of the population mean than the estimators with the traditional simple random sampling (SRS) and RSS schemes. In this paper, we extend the work and consider the maximum likelihood estimators (MLEs) of the location and scale parameters when sampling from a location-scale family of distributions. In order to give more insight into the performance of VLRSS with respect to SRS and RSS schemes, the asymptotic relative precisions of the MLEs using VLRSS relative to that using SRS and RSS are compared for some usual location-scale distributions. It turns out that the MLEs with VLRSS are more precise than those with the existing sampling schemes.

scholarly journals Robust Distribution-Free Hybrid Exponentially Weighted Moving Average Schemes Based on Simple Random Sampling and Ranked Set Sampling Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Jean-Claude Malela-Majika ◽  
Sandile C. Shongwe ◽  
Muhammad Aslam ◽  
Saddam A. Abbasi
Keyword(s):  
Random Sampling ◽  
Control Charts ◽  
Moving Average ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Exponentially Weighted Moving Average ◽  
Runs Rules ◽  
Formula Type ◽  
Weighted Moving Average ◽  
Exponentially Weighted

This paper proposes new nonparametric hybrid exponentially weighted moving average (HEWMA) control charts based on simple random sampling (SRS) and ranked set sampling (RSS) techniques using the Wilcoxon rank-sum W statistic. The in-control robustness and out-of-control (OOC) performances are thoroughly investigated using extensive simulations. The HEWMA W chart is shown to be superior to the basic exponentially weighted moving average (EWMA) and double EWMA W charts in many cases under normal and nonnormal distributions. Moreover, the OOC sensitivities of the new HEWMA W -type control charts are further improved by using supplementary 2-of-2 and 2-of-3 standard and improved runs-rules approaches. It is found that the proposed HEWMA W -type charts with runs-rules perform better than the basic HEWMA W SRS and RSS charts. Real-life data based on the impurity of iron ore are used to illustrate the design and implementation of the new control charts.

scholarly journals On Regression Estimators Using Extreme Ranked Set Samples

2004 ◽  
Vol 9 ◽  
pp. 67
Author(s):  
Hani M. Samawi ◽  
Ahmed Y.A. Al-Samarraie ◽  
Obaid M. Al-Saidy
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Population Mean ◽  
Concomitant Variable ◽  
Regression Estimators ◽  
Monte Carlo Evaluation ◽  
Extreme Ranked Set Sampling

Regression is used to estimate the population mean of the response variable, , in the two cases where the population mean of the concomitant (auxiliary) variable, , is known and where it is unknown. In the latter case, a double sampling method is used to estimate the population mean of the concomitant variable. We invesitagate the performance of the two methods using extreme ranked set sampling (ERSS), as discussed by Samawi et al. (1996). Theoretical and Monte Carlo evaluation results as well as an illustration using actual data are presented. The results show that if the underlying joint distribution of and  is symmetric, then using ERSS to obtain regression estimates is more efficient than using ranked set sampling (RSS) or  simple random sampling (SRS).  

Sign in / Sign up

Export Citation Format

Share Document