For two reasons, artificial or pseudo-populations are a basic concept in statistical surveys: At first, this unified framework may substantially improve the users’ intuitive understanding of various aspects in the sampling theory. This is shown in the book for the probability and non-probability sampling schemes, different estimation strategies, small area estimation, the repair methods for occurred nonresponse, data imputation and weighting adjustment, as well as for randomized response questioning designs that are aimed to avoid nonresponse at all. At second, in some cases pseudo-populations actually have to be generated physically: Examples are simulation studies in the field of survey sampling and a certain type of the finite population bootstrap.
A state-of-the-art presentation of optimum spatio-temporal sampling design - bridging classic ideas with modern statistical modeling concepts and the latest computational methods. Spatio-temporal Design presents a comprehensive state-of-the-art presentation combining both classical and modern treatments of network design and planning for spatial and spatio-temporal data acquisition. A common problem set is interwoven throughout the chapters, providing various perspectives to illustrate a complete insight to the problem at hand. Motivated by the high demand for statistical analysis of data that takes spatial and spatio-temporal information into account, this book incorporates ideas from the areas of time series, spatial statistics and stochastic processes, and combines them to discuss optimum spatio-temporal sampling design.
An Approach Using Finite Mixtures of Markov Chain Models
MATLAB Package bayesf
The new Version 2.0 of the Matlab-Package bayesf to analyze some of the finite mixture and Markov switching models discussed in the book is now available.