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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.
An introduction to the essentially mathematical principles of survey sampling as they are applied in practice. Intended for survey sampling theorists and practitioners, as a guide for those who may have to design and conduct a survey, and for those commissioning, organizing, and overseeing survey op
The present paper presents the detail discussion on estimation of population mean in simple random sampling in the presence of non-response. Motivated by Gupta and Shabbir (2008), we have suggested the class of estimators of population mean using an auxiliary variable under non-response. A theoretical study is carried out using two-phase sampling scheme when the population mean of auxiliary variable is not known. An empirical study has also been done in the support of theoretical results.
Reviews sampling methods used in surveys: simple random sampling, systematic sampling, stratification, cluster and multi-stage sampling, sampling with probability proportional to size, two-phase sampling, replicated sampling, panel designs, and non-probability sampling. Kalton discusses issues of practical implementation, including frame problems and non-response, and gives examples of sample designs for a national face-to-face interview survey and for a telephone survey. He also treats the use of weights in survey analysis, the computation of sampling errors with complex sampling designs, and the determination of sample size.
This book presents statistical knowledge, and methodology of sampling and data analysis specifically for spatial inventory and monitoring of local natural resources. The text shows how statistical methodology can be embedded in real-life spatial inventory and monitoring projects. The book functions as a design guide for efficient sampling schemes and monitoring systems can be designed, consistent with the aims and constraints of the project.
This book presents the state-of-the-art of forest resources assessments and monitoring. It provides links to practical applications of forest and natural resource assessment programs. It offers an overview of current forest inventory systems and discusses forest mensuration, sampling techniques, remote sensing applications, geographic and forest information systems, and multi-resource forest inventory. Attention is also given to the quantification of non-wood goods and services.
Now available in paperback, this book provides a comprehensive account of survey sampling theory and methodology suitable for students and researchers across a variety of disciplines. It shows how statistical modeling is a vital component of the sampling process and in the choice of estimation technique. The first textbook that systematically extends traditional sampling theory with the aid of a modern model assisted outlook. Covers classical topics as well as areas where significant new developments have taken place.
Modern statistics consists of methods which help in drawing inferences about the population under consideration. These populations may actually exist, or could be generated by repeated· experimentation. The medium of drawing inferences about the population is the sample, which is a subset of measurements selected from the population. Each measurement in the sample is used for making inferences about the population. The populations and also the methods of sample selection differ from one field of science to the other. Social scientists use surveys tocollectthe sample information, whereas the physical scientists employ the method of experimentation for obtaining this information. This is because in social sciences the factors that cause variation in the measurements on the study variable for the population units can not be controlled, whereas in physical sciences these factors can be controlled, at least to some extent, through proper experimental design. Several excellent books on sampling theory are available in the market. These books discuss the theory of sample surveys in great depth and detail, and are suited to the postgraduate students majoring in statistics. Research workers in the field of sampling methodology can also make use of these books. However, not many suitable books are available, which can be used by the students and researchers in the fields of economics, social sciences, extension education, agriculture, medical sciences, business management, etc. These students and workers usually conduct sample surveys during their research projects.
Sampling Theory and Methods presents in detail several sampling schemes like simple random sampling, unequal probability sampling methods, systematic, stratified, cluster and multistage sampling. In addition to sampling schemes a number of estimating methods which include ratio and regression estimators are also discussed. The use of superpopulation models is covered in detail along with recent developments including estimation of distribution functions, adaptive sampling schemes etc. New to the Second Edition: *Contents reorganized to establish a coherent link between various concepts *Several numerical examples associated with real life solutions for bringing out the relevance of theory in real life context
"The level is appropriate for an upper-level undergraduate or graduate-level statistics major. Sampling: Design and Analysis (SDA) will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor of survey statistics will gain even more from the content that SDA offers. The updates to SDA have potential to enrich traditional survey sampling classes at both the undergraduate and graduate levels. The new discussions of low response rates, non-probability surveys, and internet as a data collection mode hold particular value, as these statistical issues have become increasingly important in survey practice in recent years... I would eagerly adopt the new edition of SDA as the required textbook." (Emily Berg, Iowa State University) What is the unemployment rate? What is the total area of land planted with soybeans? How many persons have antibodies to the virus causing COVID-19? Sampling: Design and Analysis, Third Edition shows you how to design and analyze surveys to answer these and other questions. This authoritative text, used as a standard reference by numerous survey organizations, teaches the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology. Readers should be familiar with concepts from an introductory statistics class including probability and linear regression; optional sections contain statistical theory for readers familiar with mathematical statistics. The third edition, thoroughly revised to incorporate recent research and applications, includes a new chapter on nonprobability samples—when to use them and how to evaluate their quality. More than 200 new examples and exercises have been added to the already extensive sets in the second edition. SDA’s companion website contains data sets, computer code, and links to two free downloadable supplementary books (also available in paperback) that provide step-by-step guides—with code, annotated output, and helpful tips—for working through the SDA examples. Instructors can use either R or SAS® software. SAS® Software Companion for Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (2022, CRC Press) R Companion for Sampling: Design and Analysis, Third Edition by Yan Lu and Sharon L. Lohr (2022, CRC Press)