New Entropy Estimators with Smaller Root Mean Squared Error

New estimators of entropy of continuous random variable are suggested. The proposed estimators are investigated under simple random sampling (SRS), ranked set sampling (RSS), and double ranked set sampling (DRSS) methods. The estimators are compared with Vasicek (1976) and Al-Omari (2014) entropy estimators theoretically and by simulation in terms of the root mean squared error (RMSE) and bias values. The results indicate that the suggested estimators have less RMSE and bias values than their competing estimators introduced by Vasicek (1976) and Al-Omari (2014)

Acceptance Sampling Plans Based on Truncated Life Tests for Sushila Distribution

An acceptance sampling plan problem based on truncated life tests when the lifetime following a Sushila distribution is considered in this paper. For various acceptance numbers, confidence levels and values of the ratio between fixed experiment time and particular mean lifetime, the minimum sample sizes required to ascertain a specified mean life were found. The operating characteristic function values of the suggested sampling plans and the producer's risk are presented. Some tables are provided and the results are illustrated by an example of a real data set

Bayesian Inference on the Variance of Normal Distribution Using Moving Extremes Ranked Set Sampling

Bayesian inference of the variance of the normal distribution is considered using moving extremes ranked set sampling (MERSS) and is compared with the simple random sampling (SRS) method. Generalized maximum likelihood estimators (GMLE), confidence intervals (CI), and different testing hypotheses are considered using simple hypothesis versus simple hypothesis, simple hypothesis versus composite alternative, and composite hypothesis versus composite alternative based on MERSS and compared with SRS. It is shown that modified inferences using MERSS are more efficient than their counterparts based on SRS

On Maximum Likelihood Estimators of the Parameters of a Modified Weibull Distribution Using Extreme Ranked Set Sampling

Extreme ranked set sampling (ERSS) is considered to estimate the three parameters and population mean of the modified Weibull distribution (MWD). The maximum likelihood estimator (MLE) is investigated and compared to the corresponding one based on simple random sampling (SRS). It is found that, the MLE based on ERSS is more efficient than MLE using SRS for estimating the three parameters of the MWD. The ERSS estimator of the population mean of the MWD is also found to be more efficient than the SRS based on the same number of measured units

EXTREME RANKED REPETITIVE SAMPLING CONTROL CHARTS

ABSTRACT In this paper, we proposed a new ranked data control chart using repetitive sampling criterion to increase the performance of detecting any shift in mean process. For the comparisons target, the average run length (ARL) of the proposed control chart based on repetitive extreme ranked set sampling computed using exact and estimated parameters. The results showed that the ARL affected negatively by the parameter estimation. Moreover, the performances of the proposed control chart is evaluated and compared with similar control chart that obtained by using different sampling schemes such as the simple random sampling, ranked set sampling, extreme ranked set sampling and repetitive ranked set sampling.. The results showed that the ranked data based control chart outperform the classical control chart in terms of the ARL

Economic Design of Acceptance Sampling Plans for Truncated Life Tests Using Three-Parameter Lindley Distribution

A single acceptance sampling plan for the three-parameter Lindley distribution under a truncated life test is developed. For various consumer’s confidence levels, acceptance numbers, and values of the ratio of the experimental time to the specified average lifetime, the minimum sample size important to assert a certain average lifetime are calculated. The operating characteristic (OC) function values as well as the associated producer’s risks are also provided. A numerical example is presented to illustrate the suggested acceptance sampling plans

Bayes Estimation of the Mean of Normal Distribution Using Moving Extreme Ranked Set Sampling

<p class="MsoNormal" style="text-justify: kashida; text-align: justify; text-kashida: 0%; margin: 0cm 0cm 0pt; unicode-bidi: embed; direction: ltr; tab-stops: 0cm;"><span style="font-family: Times New Roman;"><span style="font-size: small;">Moving extreme ranked set sampling (MERSS) is one of useful modifications of ranked set</span><span style="font-size: 11pt;"> </span><span style="font-size: small;">sampling (RSS). The method has been investigated by many authors and proved to be a good competitor to simple random sampling (SRS). In this paper, Bayes estimation of the mean of normal distribution based on MERSS is considered and compared with SRS counterpart. The suggested estimators are found to be more efficient than that from SRS.</span></span></p

Double acceptance sampling plan based on truncated life tests for two-parameter Xgamma distribution

In this paper, a double acceptance sampling plan (DASP) in terms oftruncated life tests is oered assuming that the lifetime of a product followsthe two-parameter Xgamma (TPXG) distribution. The mean of the TPXGdistribution is considered as the quality parameter. For a certain values ofthe consumer's condence level, the minimum sample sizes of the rst andsecond samples required to assert the identied mean life are achieved. Thecorresponding operating characteristic (OC) values for to the various qualitylevels are attained as well as the minimum ratios of the mean life to theindicated life are obtained. Numerical results and examples are presentedfor illustration

Inference of Adaptive methods for Multi-Stage skew-t Simulated Data

Multilevel models can be used to account for clustering in data from multi-stage surveys. In some cases, the intra-cluster correlation may be close to zero, so that it may seem reasonable to ignore clustering and fit a single level model. This article proposes several adaptive strategies for allowing for clustering in regression analysis of multi-stage survey data. The approach is based on testing whether the cluster-level variance component is zero. If this hypothesis is retained, then variance estimates are calculated ignoring clustering; otherwise, clustering is reflected in variance estimation. A simple simulation study is used to evaluate the various procedures

New double stage ranked set sampling for estimating the population mean

In environmental and many other areas, the main focus of survey is to measureelements using an efficient and cost-effective sampling technique. One way to reach that isby using Ranked set sampling (RSS). RSS is an alternative sampling technique that canimprove the efficiency of estimators when measuring the variable of interest is eithercostly or time-consuming but ranking its elements in a small set is easy. The purpose of thisarticle is to introduce a new modification of RSS to estimate the mean of the targetpopulation. This proposed technique is a double-stage approach that combines median RSS(MRSS) and MiniMax RSS (MMRSS). The performance of the empirical mean and varianceestimators based on the proposed technique are compared with their counterparts inMMRSS, RSS and simple random sampling (SRS) via Monte Carlo simulation. Simulationresults revealed that this new modification is always more efficient than their counterpartsusing MMRSS and SRS, while it is more efficient than RSS is many cases especially when thedistribution is asymmetric