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This paper deals with the problem of estimating the finite population mean when some information on two auxiliary attributes are available.
The present book aims to present some improved estimators using auxiliary and attribute information in case of simple random sampling and stratified random sampling and in some cases when non-response is present. This volume is a collection of five papers, written by seven co-authors (listed in the order of the papers): Sachin Malik, Rajesh Singh, Florentin Smarandache, B. B. Khare, P. S. Jha, Usha Srivastava and Habib Ur. Rehman.
A comprehensive expose of basic and advanced sampling techniques along with their applications in the diverse fields of science and technology.
This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.
Ranked Set Sampling is one of the new areas of study in this region of the world and is a growing subject of research. Recently, researchers have paid attention to the development of the types of sampling; though it was not welcome in the beginning, it has numerous advantages over the classical sampling techniques. Ranked Set Sampling is doubly random and can be used in any survey designs. The Pakistan Journal of Statistics had attracted statisticians and samplers around the world to write up aspects of Ranked Set Sampling. All of the essays in this book have been reviewed by many critics. This volume can be used as a reference book for postgraduate students in economics, social sciences, medical and biological sciences, and statistics. The subject is still a hot topic for MPhil and PhD students for their dissertations.
One of the disadvantages of the point estimate in survey sampling is that it fluctuates from sample to sample due to sampling error, as the estimator only provides a point value for the parameter under discussion. The neutrosophic approach, pioneered by Florentin Smarandache, is an excellent tool for estimating the parameters under consideration in sampling theory since it yields interval estimates in which the parameter lies with a very high probability. As a result, the neutrosophic technique, which is a generalization of classical approach, is used to deal with ambiguous, indeterminate, and uncertain data. In this investigation, we suggest a new general family of ratio and exponential ratio type estimators for the elevated estimation of neutrosophic population mean of the primary variable utilizing known neutrosophic auxiliary parameters. For the first degree approximation, the bias and Mean Squared Error (MSE) of the suggested estimators are computed. The neutrosophic optimum values of the characterizing constants are determined, as well as the minimum value of the neutrosophic MSE of the suggested estimator is obtained for these optimum values of the characterizing scalars. Because the minimum MSE of the classical estimators of population mean lies inside the estimated interval of the neutrosophic estimators, the neutrosophic estimators are better than the equivalent classical estimators. The empirical investigation, which used both real and simulated data sets, backs up the theoretical findings. For practical utility in various areas of applications, the estimator with the lowest MSE or highest Percentage Relative Efficiency (PRE) is recommended.
Now available in paperback, this book is organized in a way that emphasizes both the theory and applications of the various variance estimating techniques. Results are often presented in the form of theorems; proofs are deleted when trivial or when a reference is readily available. It applies to large, complex surveys; and to provide an easy reference for the survey researcher who is faced with the problem of estimating variances for real survey data.
The development of estimators of population parameters based on two-phase sampling schemes has seen a dramatic increase in the past decade. Various authors have developed estimators of population using either one or two auxiliary variables. The present volume is a comprehensive collection of estimators available in single and two phase sampling. The book covers estimators which utilize information on single, two and multiple auxiliary variables of both quantitative and qualitative nature. The estimators discussed in the text are based upon different mechanisms of the availability of auxiliary information, termed here as Full, Partial and No Information. Multivariate estimators in survey sampling are also discussed in the book. Two Phase Sampling will prove an invaluable point of reference for researchers working in the field of survey sampling in general and in the field of two-phase sampling in particular.
Basic theory: simple random sampling. Sampling with varying probabilities. Stratified sampling. Ratio method of estimation. Regression method estimation. Choice of sampling unit. Sub-sampling. Systematic sampling. Non-sampling errors.
"Ratio Method of Estimation - This is an ideal textbook for researchers interested in sampling methods, survey methodologists in government organizations, academicians, and graduate students in statistics, mathematics and biostatistics. This textbook makes"