Sampling Methods | 22-July-2018
In the present study we have proposed an improved family of estimators for estimation of population mean using the auxiliary information of median, quartile deviation, Gini’s mean difference, Downton’s Method, Probability Weighted Moments and their linear combinations with correlation coefficient and coefficient of variation. The performance of the proposed family of estimators is analysed by mean square error and bias and compared with the existing estimators in the literature. By
Mir Subzar,
Showkat Maqbool,
Tariq Ahmad Raja,
Surya Kant Pal,
Prayas Sharma
Statistics in Transition New Series, Volume 19 , ISSUE 2, 219–238
Article | 13-December-2019
The most dominant problem in the survey sampling is to obtain the better ratio estimators for the estimation of population mean or population variance. Estimation theory is enhanced by using the auxiliary information in order to improve on designs, precision and efficiency of estimators. A modified class of ratio estimator is suggested in this paper to estimate the population mean. Expressions for the bias and the mean square error of the proposed estimators are obtained. Both analytical and
Mir Subzar,
S. Maqbool,
T. A. Raja,
Prayas Sharma
Statistics in Transition New Series, Volume 20 , ISSUE 4, 181–189
Article | 24-August-2017
Nazeema T. Beevi,
C. Chandran
Statistics in Transition New Series, Volume 18 , ISSUE 2, 227–245
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
Article | 24-August-2017
important reasons are weighting for unequal probability of selection, compensation for nonresponse, and post-stratification. Many highly efficient estimation methods in survey sampling require strong information about auxiliary variables, x. The most common estimation methods using auxiliary information in estimation stage are regression and ratio estimator. This paper proposes a sequential data weighting procedure for the estimators of combined ratio mean in complex sample surveys and general variance
Aylin Alkaya,
H. Öztaş Ayhan,
Alptekin Esin
Statistics in Transition New Series, Volume 18 , ISSUE 2, 247–270
Sampling Methods | 20-November-2017
RANJITA PANDEY,
KALPANA YADAV
Statistics in Transition New Series, Volume 18 , ISSUE 3, 375–392
Article | 28-May-2019
The estimation of population parameters might be quite laborious and inefficient, when the sample data have missing values. In comparison follow-up visits, the method of imputation has been found to be a cheaper procedure from a cost point of view. In the present study, we can enhance the performance of imputation procedures by utilizing the raw moments of the auxiliary information rather than their ranks, especially, when the ranking of the auxiliary variable is expensive or difficult to do so
Muhammed Umair Sohail,
Javid Shabbir,
Farinha Sohil
Statistics in Transition New Series, Volume 20 , ISSUE 1, 21–40
Article | 22-January-2018
The key and fundamental purpose of sampling over successive waves lies in the varying nature of study character, it so may happen with ancillary information if the time lag between two successive waves is sufficiently large. Keeping the varying nature of auxiliary information in consideration, modern approaches have been proposed to estimate population mean over two successive waves. Four exponential ratio type estimators have been designed. The properties of proposed estimators have been
Kumari Priyanka,
Richa Mittal
Statistics in Transition New Series, Volume 18 , ISSUE 4, 569–587
Article | 15-March-2019
G. N. Singh,
Amod Kumar,
Gajendra K. Vishwakarma
Statistics in Transition New Series, Volume 19 , ISSUE 4, 575–596
Article | 06-July-2017
includes both area effects and unit level random errors. The population is made up of mutually exclusive domains of different sizes, ranging from a small number of units to a large number of units. We select many independent simple random samples of fixed size from the population and compute various estimates for each sample using the available auxiliary information. The estimates computed for the simulation included the Horvitz-Thompson estimator, the synthetic estimator (indirect estimate
Michael A. Hidiroglou,
Victor M. Estevao
Statistics in Transition New Series, Volume 17 , ISSUE 1, 133–154
Research paper | 31-October-2017
proposed another estimator, which is a linear combination of the means of the matched and unmatched portion of the sample on the second occasion. The bias and mean square error of this combined estimator is also obtained. The optimum mean square error of this combined estimator was compared with (i) the optimum mean square error of the estimator proposed by Singh (2005) (ii) mean per unit estimator and (iii) combined estimator suggested by Cochran (1977) when no auxiliary information is used on any
Zoramthanga Ralte,
Gitasree Das
Statistics in Transition New Series, Volume 16 , ISSUE 2, 183–202
Research Article | 13-December-2018
Optimum stratification is the method of choosing the best boundaries that make the strata internally homogenous. Many authors have attempted to determine the optimum strata boundaries (OSB) when a study variable is itself a stratification variable. However, in many practical situations fetching information regarding the study variable is either difficult or sometimes not available. In such situations we find help in the variable (s) closely related to the study variable. Using auxiliary
Faizan Danish
Statistics in Transition New Series, Volume 19 , ISSUE 3, 507–526