Known Coefficient Of Variation Research Articles

Shrinkage estimation for square of location parameter of the exponential distribution with known coefficient of variation

Shrinkage estimation has become a basic tool in the computational analysis of high-dimensional data. Historically and conceptually a key development toward this was the discovery of the inadmissibility of the usual estimator of a multivariate normal mean. This study explores the problem of estimating the square of location parameter of an exponential distribution when the coefficient of variation is known without error. Several estimators have been proposed with their properties. The best unbiased estimator as well least minimum mean square error (MMSE) estimator has been identified among several estimators. Numerical illustrations are given in the support of the present study.

Reliability estimation for mean of normal population with known coefficient of variation

An attempt has been made to estimate reliability for mean of normal population with known CV. The reliability function is derived and curves have been shown for this reliability function. It is seen that effect of skewness is considerable but the effect of kurtosis is not much.

Estimating Simultaneous Confidence Intervals for Multiple Contrasts of Means of Normal Distribution with Known Coefficients of Variation

This study investigated the performance of simultaneous confidence intervals (SCIs) to differentiate the means of multiple normal population distributions with known coefficients of variation (CVs). The researchers aim to find the means of several normal distributions with known coefficients of variation, SCIMOVER, SCIs, and SCIk, which are extended to k populations. The authors constructed SCIs for the difference between multiple normal means with known coefficients of variation. There are three approaches: the method of variance estimates recovery approach (MOVER), and two central limit theorem approaches (CLT). A Monte Carlo simulation was used to evaluate the performance of the coverage probabilities and expected lengths of the methods. The simulation results indicate that the MOVER approach is more desirable than the CLT approaches in terms of the coverage probability. The performance of the proposed approaches is also compared using an example with real data. Moreover, the coverage probability results for SCIMOVER were over the nominal level of 0.95, indicating that it is more stable than SCIs and SCIk and was thus more appropriate for use in this scenario. Finally, the researchers suggest using the MOVER approach for constructing the SCIs to determine the variation to achieve the best solution in related fields in the near future. Doi: 10.28991/ESJ-2022-06-04-04 Full Text: PDF

Sequential Estimation of an Inverse Gaussian Mean with Known Coefficient of Variation

Sequential Estimation of an Inverse Gaussian Mean with Known Coefficient of Variation

Optimal estimation of population variance in the presence of random non-response using simulation approach

This paper introduces some new ratio- and difference-type estimators of the population variance in the presence of random non-response based on Searls (The utilization of a known coefficient of variation in the estimation procedure. J Am Stat Asso. 1964;59:1225–1226) philosophy. The proposed ratio- and difference-type estimators remain better than the estimators obtained by Ahmeda et al. (Estimation of finite population variance in presence of random non-response using auxiliary variables. Infr Mang Sci. 2005;16(2):73–82) in the presence of random non-response using auxiliary variables. The properties (bias and mean square error) of the proposed estimators presented were derived up to the first-order approximation using the Taylor series approach. Conditions for which the new estimators more efficient than other estimators considered in the study were also established. Numerical examples were conducted, and the results revealed that the proposed class of estimators is more efficient than existing estimators.

Linear Regression Estimation Methods for Inferring Standard Values of Snow Load in Small Sample Situations

The aim of this paper is to establish a new method for inferring standard values of snow load in small sample situations. Due to the incomplete meteorological data in some areas, it is often necessary to infer the standard values of snow load in the conditions of small samples in engineering, but the point estimation methods of classical statistics adopted till now do not take into account the influences of statistical uncertainty, and the inference results are always aggressive. In order to overcome the above shortcomings, according to the basic principle of optimal linear unbiased estimation and invariant estimation of the minimum type I distribution parameters and the tantile, using the least square method, the linear regression estimation methods for inferring standard values of snow load in small sample situations are proposed, which can take into account two cases such as parameter-free and known coefficient of variation, and the predicted formulas of snow load standard values are given, respectively. Through numerical integration and Monte Carlo numerical simulation, the numerical table of correlation coefficients is established, which is more convenient for the direct application of inferential formulas. According to the results of theoretical analysis and examples, when using the indirect point estimation methods to infer the standard values of snow load in the conditions of small samples, the inference results are always small. The linear regression estimation method is suitable for inferring standard values of snow load in the conditions of small samples, which can give more reasonable results. When using the linear regression estimation to infer standard values of snow load in practical application, even if the coefficient of variation is unknown, it can set the upper limit value of the coefficient of variation according to the experience; meanwhile, according to the parameter-free and known coefficient of variation, the estimation is carried out, respectively, and the smaller value of the two is taken as the final estimate. The method can be extended to the statistical inference of variable load standard values such as wind load and floor load.

Estimation of Parameters in Erlang Distribution using Prior Information

This paper focuses on the estimation of parameters of an Erlang density using known Coefficients of variation and Kurtosis of the population basing on past experience and a simple random sample of size from the population. Estimators using Searle D.T approach are proposed and their bias and mean squared error are calculated. The relative efficiency of over conventional estimator is also tabulated for various sample sizes and various C.V values, Kurtosis values. The proposed estimators are observed to be more efficient than under the conditions established. Subject Classification: AMS 62J13

Use of coefficient of variation in calibration estimation of population mean in stratified sampling

Calibration approach is becoming more important in survey sampling. Calibration estimation helps in improving the estimates of population parameters by making use of auxiliary information. This paper proposes a new calibration estimator for estimating the population mean in the stratified random sampling with a set of new calibration constraints using known coefficient of variation of the auxiliary variable. The result has also been extended to the case of double sampling in stratified sampling. The proposed estimator has been compared with the estimator developed by Tracy et al. (2003) with the help of simulation study on real datasets.

Ratio- and product-based estimators using known coefficient of variation of the auxiliary variable via modified maximum likelihood

In this paper, we improve ratio- and product-based estimators for population mean using known coefficient of variation of the auxiliary variable through modified maximum likelihood. The expressions for biases and mean square errors of the proposed estimators have been obtained theoretically. We obtain some conditions under which the proposed estimators have minimum mean square errors than other existing estimators. For the support of the theoretical outcomes, simulations studies have been made under several super-population models. One real life application for the support of the properties of the proposed estimator has also been given. We also study the robustness properties of the proposed estimators. We show that the use of modified maximum likelihood estimates in estimating finite population mean results to robust estimates under non-normality and outliers. Confidence intervals show that the proposed estimator has shorter confidence intervals and more coverage of estimates than those of the existing estimators.

Improved two phase sampling exponential ratio and product type estimators for population mean of study character in the presence of non response

Improved two phase sampling exponential ratio and product type estimators for population mean using known coefficient of variation of study character in the presence of non response have been proposed and their properties are studied under large sample approximation. The proposed estimators are compared with the other existing estimators by using the MSE criterion and the conditions under which the proposed estimators perform better are obtained. An empirical study is also given to judge the performance of the proposed estimators. At the end, simulation studies have been carried out to verify the superiority to the proposed estimators.

Modified estimator for population mean in systematic sampling using known coefficient of variation

This paper focuses on modified estimator for population mean in case of systematic sampling procedure in view of minimum mean squared error and sacrificing unbiasedness property of estimator. This estimator is constructed by using known coefficient of variation of the population under study. The underlying procedure of construction is that of Searle.D.T.(1964).The intra- correlation coefficient of strata is observed to play key role in this construction. The relative efficiencies(REF) of constructed estimator over existing conventional estimator of systematic sampling are obtained for various values of known coefficient of variation and hypothetical values of intra class correlation coefficient. The numerical results are tabulated, and it is established that constructed estimator is more efficient than the conventional estimator.

Effect of AR (2) Model on the Economic Design Mean Chart with Known Coefficient of Variation Under Non Normal Population

Effect of AR (2) Model on the Economic Design Mean Chart with Known Coefficient of Variation Under Non Normal Population

The Effect of Measurement Error on the Power of X Charts with Known Coefficient of variation

The Effect of Measurement Error on the Power of X Charts with Known Coefficient of variation

Single Sampling Plan for Variable Indexed by AQL and AOQL with Known Coefficient of Variation

Single Sampling Plan for Variable Indexed by AQL and AOQL with Known Coefficient of Variation

An Efficient General Family of Estimators for Population Mean in the Presence of Non-Response

This paper presents a new general family of estimators to estimate the population mean of study variable y in the presence of non-response when utilizing a known coefficient of variation of study variable y. The expressions for bias, mean squared error (MSE), and minimum mean squared error (MMSE) of the proposed family of estimators are derived up to the first degree of approximation. In addition, a numerical example and a simulation study are presented to explain the performance of the proposed estimators. It was shown that the proposed estimators perform better compared to all other relevant estimators.

Confidence Intervals for the Reciprocal of a Normal Mean with a Known Coefficient of Variation and a Restricted Parameter Space

The natural parameter space is known to be restricted in many real applications such as engineering, sciences and social sciences. The confidence interval derived from the classical Neyman procedure is unsatisfactory in the case of a restricted parameter space. New confidence intervals for the reciprocal of a normal mean with a known coefficient of variation and a restricted parameter space are proposed in this paper. A simulation study has been conducted to compare the performance of the proposed confidence intervals.

Use of scrambled response model in estimating the finite population mean in presence of non response when coefficient of variation is known

ABSTRACTWe propose an estimator for the finite population mean utilizing known coefficient of variation of the study character in case of quantitative sensitive variable considering a randomization mechanism on the second call that provides privacy protection to the respondents to get truthful information. We also propose generalized ratio- and regression-type estimators under two-phase sampling scheme. The conditions under which the proposed estimators are more efficient than the relevant estimators under scrambled response model have been obtained. An empirical study is carried out to evaluate performances of the estimators.

Estimation of population variance using known coefficient of variation of an auxiliary variable in sample surveys

This paper addresses the problem of estimating the population variance of the study variable y using information on coefficient of variation Cx of an auxiliary variable x. We have suggested a class of estimators for the population variance. Expressions of bias and mean squared error of the proposed class of estimators are obtained under large sample approximation. It is identified that the usual unbiased estimator and Das and Tripathi’s (1978) estimators are members of the suggested class of estimators. It has been shown that proposed class of estimators is more efficient than usual unbiased estimator and Das and Tripathi’s (1978) estimators. An empirical study is given in support of the present study.

An Improved Estimator of Population Variance using known Coefficient of Variation

An Improved Estimator of Population Variance using known Coefficient of Variation

Use of Apriori Information in Estimating Mean of a Normal Population– Second Order Relative Efficiencies

Background/Objective: Modified estimator for population mean using known coefficient of variation was proposed by1. Such an estimator has minimum mean squared error but it is unbiased. In2 has proposed an estimator for population mean using an estimator for coefficient of variation. Motivated by the distribution of the sample root mean square s given in3 an estimator was constructed for mean of Normal population by4 and the relative efficiency of the proposed estimator over the conventional unbiased estimator was derived up to first order approximation. The present paper is focused on constructing two estimators for the mean of Normal population using known coefficient of variation. The relative efficiencies are derived upto second order and the numerical results are tabulated. Methods: Basing on Searle method two estimators are constructed for mean μ of Normal population. Former is a linear combination whereas the latter being a convex combination of X and s. Results: The Relative efficiencies of the proposed estimators over the conventional unbiased estimator X are obtained. Conclusions/Applications: It is established that the first estimator is more efficient than that of X where as the second estimator is equally efficient to that of X under first order approximation. Under the second order approximation it is observed that both first and second estimators are more efficient than X and the first estimator is more efficient than that of second estimator.