An Efficient Class of Estimators for the Mean of a Finite Population in Two-Phase Sampling Using Multi-Auxiliary Variates

This paper presents an efficient class of estimators for estimating the population mean of the variate under study in two-phase sampling using information on several auxiliary variates.The expressions for bias and mean square error(MSE)of the proposed class have been obtained using Taylor series method.In addition,the minimum attainableMSE of the proposed class is obtained to the first order of approximation.The proposed class encompasses a wide range of estimators of the sampling literature.Efficiency comparison has been made for demonstrating the performance of the proposed class.An attempt has been made to find optimum sample sizes under a known fixed cost function.Numerical illustrations are given in support of theoretical findings.

Time-course data prediction for repeatedly measured gene expression

Variability in time course gene expression data is a natural phenomenon. The intention of this work is to predict the future time point data through observed sample data point. The Bayesian inference is carried to serve the objective. A total of 6 replicates 3 time point's data of 218 genes expression is adopted to illustrate the method. The estimates are found consistent with HPD interval to predict the future time point gene expression value. This proposed method can be adopted in other gene expression data setup to predict the future time course data.

Estimation of Mean in Double Sampling Using Exponential Technique on Multi-auxiliary Variates

This paper presents exponential-type ratio and product estimators for a finite population mean in double sampling using information on several auxiliary variates.The proposed estimators can be viewed as a generalization over the estimators suggested by Singh and Vishwakarma(Austrian J Stat 36(3):217–225,2007).The expressions for biases and mean square errors(MSEs)of the proposed estimators have been derived to the first degree of approximation.In addition,the expressions for minimum attainable MSEs are also investigated using the criterion for optimality of the weights.An empirical study is carried out in the support of the present study.Both theoretical andempirical findings are encouraging and support thesoundness that the proposed procedures for mean estimation perform better than the usual unbiased estimators and other well-known estimators under some realistic conditions.

Estimation of optimum sample size allocation:An illustration with body mass index for evaluating the effect of a dietetic supplement

In many medical researches,it is needed to determine the optimal sample size allocation in a heterogeneous population.This paper proposes the algorithm for optimal sample size allocation.We consider the optimal allocation problem as an optimization problem and the solution is obtained by using Bisection,Secant,Regula-Falsi and other numerical methods.The performance of the algorithm for different numerical methods are analyzed and evaluated in terms of computing time,number of iterations and gain in accuracy using stratification.The efficacy of algorithm is evaluated for the response in terms of body mass index(BMI)to the dietetic supplement with diabetes mellitus,HIV/AIDS and cancer post-operatory recovery patients.