Article | 06-July-2017
The present paper deals with a new median based ratio estimator for the estimation of finite population means in the absence of an auxiliary variable. The bias and mean squared error of the proposed median based ratio estimator are obtained. The performance of the median based ratio estimator is compared with that of the SRSWOR sample mean, ratio estimator and linear regression estimator for certain natural population. It is shown from the numerical comparisons that the proposed median based
J. Subramani
Statistics in Transition New Series, Volume 17 , ISSUE 4, 591–604
Article | 17-July-2017
Abstract
In this paper, the effect of misspecification due to omission of relevant variables on the dominance of the r − (k, d) class estimator proposed by Özkale (2012), over the ordinary least squares (OLS) estimator and some other competing estimators when some of the regressors in the linear regression model are correlated, have been studied with respect to the mean squared error criterion. A simulation study and numerical example have been demostrated to compare the performance of the
Shalini Chandra,
Gargi Tyagi
Statistics in Transition New Series, Volume 18 , ISSUE 1, 27–52
Article | 06-July-2017
This paper proposes a family of estimators of population variance 2 y S of the study variable y in the presence of known population variance 2 x S of the auxiliary variable x. It is identified that in addition to many, the recently proposed classes of estimators due to Sharma and Singh (2014) and Singh and Pal (2016) are members of the proposed family of estimators. Asymptotic expressions of bias and mean squared error (MSE) of the suggested family of
Housila P. Singh,
Surya K. Pal
Statistics in Transition New Series, Volume 17 , ISSUE 4, 605–630
Research paper | 31-October-2017
This paper develops allocation methods for stratified sample surveys in which small area estimation is a priority. We assume stratified sampling with small areas as the strata. Similar to Longford (2006), we seek efficient allocation that minimizes a linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. Unlike Longford, we define mean-squared error in a model-assisted framework, allowing a more natural interpretation of results
W. B. Molefe,
D. K. Shangodoyin,
R. G. Clark
Statistics in Transition New Series, Volume 16 , ISSUE 2, 163–182
Article | 06-July-2017
Construction of small area predictors and estimation of the prediction mean squared error, given different types of auxiliary information are illustrated for a unit level model. Of interest are situations where the mean and variance of an auxiliary variable are subject to estimation error. Fixed and random specifications for the auxiliary variables are considered. The efficiency gains associated with the random specification for the auxiliary variable measured with error are demonstrated. A
Andreea L. Erciulescu,
Wayne A. Fuller
Statistics in Transition New Series, Volume 17 , ISSUE 1, 9–24
Research paper | 31-October-2017
Hilal A. Lone,
Rajesh Tailor
Statistics in Transition New Series, Volume 16 , ISSUE 1, 53–64
Article | 13-December-2019
This paper proposes an improved estimation method for the population coefficient of variation, which uses information on a single auxiliary variable. The authors derived the expressions for the mean squared error of the proposed estimators up to the first order of approximation. It was demonstrated that the estimators proposed by the authors are more efficient than the existing ones. The results of the study were validated by both empirical and simulation studies.
Rajesh Singh,
Madhulika Mishra
Statistics in Transition New Series, Volume 20 , ISSUE 4, 89–111
Sampling Methods | 26-May-2018
Jaishree Prabha Karna,
Dilip Chandra Nath
Statistics in Transition New Series, Volume 19 , ISSUE 1, 25–44
Sampling Methods | 20-November-2017
RANJITA PANDEY,
KALPANA YADAV
Statistics in Transition New Series, Volume 18 , ISSUE 3, 375–392
Article | 22-July-2019
Ashok V. Dorugade
Statistics in Transition New Series, Volume 20 , ISSUE 2, 173–185
Article | 28-May-2019
. Equations for bias and mean squared error are obtained by large sample approximation. Through the numerical and simulation studies it can be easily understood that the proposed method of imputation can outperform their counterparts.
Muhammed Umair Sohail,
Javid Shabbir,
Farinha Sohil
Statistics in Transition New Series, Volume 20 , ISSUE 1, 21–40
Research Communicate | 18-March-2020
This study proposes a new class of exponential-type estimators in simple random sampling for the estimation of the population mean of the study variable using information of the population proportion possessing certain attributes. Theoretically, mean squared error (MSE) equations of the suggested ratio exponential estimators are obtained and compared with the Naik and Gupta (1996) ratio and product estimators, the ratio and product exponential estimator presented in Singh et al. (2007) and the
Tolga Zaman
Statistics in Transition New Series, Volume 21 , ISSUE 1, 159–168
Article | 06-July-2017
Ralf Münnich,
Jan Pablo Burgard,
Siegfried Gabler,
Matthias Ganninger,
Jan-Philipp Kolb
Statistics in Transition New Series, Volume 17 , ISSUE 1, 25–40
Sampling Methods | 20-November-2017
The mean squared error reflects only the average prediction accuracy while the distribution of squared prediction error is positively skewed. Hence, assessing or comparing accuracy based on the MSE (which is the mean of squared errors) is insufficient and even inadequate because we should be interested not only in the average but in the whole distribution of prediction errors. This is the reason why we propose to use different than MSE measures of prediction accuracy in small area estimation
Tomasz Żądło
Statistics in Transition New Series, Volume 18 , ISSUE 3, 413–432
Research Article | 15-February-2020
transform, the Zhao-Atlas-Marks distribution, and the Choi-Williams distribution. Five time-frequency Gaussian atoms and a bat echolocation chirp are used as the testing signals. The Rényi entropy, the ratio of norms, the Stanković measure, and the mean squared error are used as quantitative measures to demonstrate the promising results of the proposed method.
Stanislav Pikula,
Petr Beneš
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–5
Article | 03-July-2017
the analysis of multivariate empirical data, it appears desirable to construct their respective estimators. This paper presents the consistent estimators of the central moments of a random vector, for which essential characteristics have been found, such as a mean vector and a mean squared error. In these formulas, the relevant orders of approximation have been taken into account.
Katarzyna Budny
Statistics in Transition New Series, Volume 18 , ISSUE 1, 1–20
Article | 24-August-2017
In this paper, we proposed an efficient family of ratio-type estimators using one auxiliary variable for the estimation of the current population mean under successive sampling scheme. This family of estimators have been studied by Ray and Sahai (1980) under simple random sampling using one auxiliary variable for estimation of the population mean. Using these estimators in successive sampling, the expression for bias and mean squared error of the proposed estimators are obtained up to the first
Nazeema T. Beevi,
C. Chandran
Statistics in Transition New Series, Volume 18 , ISSUE 2, 227–245
Research paper | 31-October-2017
analysis because different estimators (decisions) are appropriate for different perspectives. An example of planning an intervention in a developing country’s districts with high rate of illiteracy is described. The example exposes the deficiencies of the general concept of efficiency and shows that the criterion for the quality of an estimator has to be formulated specifically for the problem at hand. In the problem, the established small-area estimators perform poorly because the minimum mean squared
Nicholas T. Longford
Statistics in Transition New Series, Volume 16 , ISSUE 1, 65–82
Research Article | 18-March-2020
Rohini Yadav,
Rajesh Tailor
Statistics in Transition New Series, Volume 21 , ISSUE 1, 1–12
Research Article | 08-December-2021
simulation results showed that the increase in parameter values decreases the mean squared error value. Similarly, the mean estimate tends towards the true parameter value as the sam ple sizes increase.
Friday Ikechukwu Agu,
Joseph Thomas Eghwerido
Statistics in Transition New Series, Volume 22 , ISSUE 4, 59–76
Article | 22-January-2018
Kumari Priyanka,
Richa Mittal
Statistics in Transition New Series, Volume 18 , ISSUE 4, 569–587
Article | 27-May-2019
Kumari Priyanka,
Pidugu Trisandhya
Statistics in Transition New Series, Volume 20 , ISSUE 1, 41–65
Article | 11-July-2017
Housila P. Singh,
Vishal Mehta
Statistics in Transition New Series, Volume 18 , ISSUE 1, 53–74
Research Article | 03-March-2021
), AICc (corrected AIC), and the RMSE (Root Mean Squared Error). In result, the following conclusions were reached: the ARFIMA(2,0.3589648,2)-sGARCH(1,1) model and the ARFIMA(2,0.3589648,2)-fGARCH(1,1) model under normal distribution proved to be the best models, demonstrating the smallest values for these criteria. The calculations conducted herein show that the two models are of the same accuracy level in terms of the RMSE value, which equals 0.08808882, and it is this result that distinguishes our
Remal Shaher AlـGounmeein,
Mohd Tahir Ismail
Statistics in Transition New Series, Volume 22 , ISSUE 1, 29–54
Article | 15-September-2020
synthetic populations that resemble the Current Population Survey (CPS) data. To draw samples from these synthetic populations, we consider a simplified sample design that mimics the CPS sample design with the same rotation pattern. Since the number of possible samples that can be drawn from each synthetic population is not large, we compute the exact Σ and the exact mean squared error of all estimators considered to facilitate comparison. To generate the first set of rival estimators, we consider
Daniel Bonnéry,
Yang Cheng,
Partha Lahiri
Statistics in Transition New Series, Volume 21 , ISSUE 4, 166–190