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An Introduction to Model-Based Survey Sampling with Applications
This book is an introduction to the model-based approach to survey sampling. It consists of three parts, with Part I focusing on estimation of population totals. Chapters 1 and 2 introduce survey sampling, and the model-based approach, respectively. Chapter 3 considers the simplest possible model, the homogenous population model, which is then extended to stratified populations in Chapter 4. Chapter 5 discusses simple linear regression models for populations, and Chapter 6 considers clustered populations. The general linear population model is then used to integrate these results in Chapter 7. Part II of this book considers the properties of estimators based on incorrectly specified models. Chapter 8 develops robust sample designs that lead to unbiased predictors under model misspecification, and shows how flexible modelling methods like non-parametric regression can be used in survey sampling. Chapter 9 extends this development to misspecfication robust prediction variance
estimators and Chapter 10 completes Part II of the book with an exploration of outlier robust sample survey estimation. Chapters 11 to 17 constitute Part III of the book and show how model-based methods can be used in a variety of problem areas of modern survey sampling. They cover (in order) prediction of non-linear population quantities, sub-sampling approaches to prediction variance estimation, design and estimation for multipurpose surveys, prediction for domains, small area estimation, efficient prediction of population distribution functions and the use of transformations in survey inference. The book is designed to be accessible to undergraduate and graduate level students with a good grounding in statistics and applied survey statisticians seeking an introduction to model-based survey design and estimation.
eBook
Statistical Models
This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
eBook
Campus Sexual Assault
Sexual assault is a pervasive problem on university and college campuses in the United States that has garnered growing national attention, particularly in the past year. This is the first study to systematically review and synthesize prevalence findings from studies on campus sexual assault (CSA) published since 2000 (n = 34). The range of prevalence findings for specific forms of sexual victimization on college campuses (i.e., forcible rape, unwanted sexual contact, incapacitated rape, sexual coercion, and studies’ broad definitions of CSA/rape) is provided, and methodological strengths and limitations in the empirical body of research on CSA are discussed. Prevalence findings, research design, methodology, sampling techniques, and measures, including the forms of sexual victimization measured, are presented and evaluated across studies. Findings suggest that unwanted sexual contact appears to be most prevalent on college campuses, including sexual coercion, followed by incapacitated rape, and completed or attempted forcible rape. Additionally, several studies measured broad constructs of sexual assault that typically include combined forms of college-based sexual victimization (i.e., forcible completed or attempted rape, unwanted sexual contact, and/or sexual coercion). Extensive variability exists within findings for each type of sexual victimization measured, including those that broadly measure sexual assault, which is largely explained by differences in sampling strategies and overall study designs as well as measures of sexual assault used in studies. Implications for findings and recommendations for future research on the prevalence of college-based sexual victimization are provided.
Journal Article
Handbook of Spatial Statistics
Based on the work of prominent researchers, this handbook provides broad, thorough coverage of this vibrant area, from historical to contemporary topics. It explores the modeling advances, computational approaches, and methodology that have emerged in recent years. The book focuses on continuous and discrete spatial variation, spatial point patterns, and spatio-temporal processes. It also covers multivariate spatial process models, spatial aggregation, spatial misalignment, and spatial gradients in depth. The theory and applications are illustrated with many real-world data examples.
eBook
Stability selection
Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional data. We introduce stability selection. It is based on subsampling in combination with (high dimensional) selection algorithms. As such, the method is extremely general and has a very wide range of applicability. Stability selection provides finite sample control for some error rates of false discoveries and hence a transparent principle to choose a proper amount of regularization for structure estimation. Variable selection and structure estimation improve markedly for a range of selection methods if stability selection is applied. We prove for the randomized lasso that stability selection will be variable selection consistent even if the necessary conditions for consistency of the original lasso method are violated. We demonstrate stability selection for variable selection and Gaussian graphical modelling, using real and simulated data.
Journal Article
Sequential Monte Carlo samplers
We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make parallel Markov chain Monte Carlo algorithms interact to perform global optimization and sequential Bayesian estimation and to compute ratios of normalizing constants. We illustrate these algorithms for various integration tasks arising in the context of Bayesian inference.
Journal Article
Social research 2.0: virtual snowball sampling method using Facebook
Purpose - The aim of this paper is to present a sampling method using virtual networks to study \"hard-to-reach\" populations. In the ambit of social research, the use of new technologies is still questioned because the selection bias is an obstacle to carry on scientific research on the Internet. In this regard, the authors' hypothesis is that the use of social networking sites (Web 2.0) can be effective for the study of \"hard-to-reach\" populations. The main advantages of this technique are that it can expand the geographical scope and facilitates the identification of individuals with barriers to access. Therefore, the use of virtual networks in non-probabilistic samples can increase the sample size and its representativeness.Design methodology approach - To test this hypothesis, a virtual method was designed using Facebook to identify Argentinean immigrant entrepreneurs in Spain (214 cases). A characteristic of this population is that some individuals are administratively invisible in national statistics because they have double nationality (non-EU and EU). The use of virtual sampling was combined with an online questionnaire as a complementary tool for Web 2.0 research in behavioural sciences.Findings - The number of cases detected by Facebook and the virtual response rate is higher than traditional snowball technique. The explanation is that people increase their level of confidence because the researcher shows his personal information (Facebook's profile) and also participates in their groups of interest (Facebook's groups). Moreover, the online questionnaires administration allows the quality of the information to be controlled and avoids duplication of cases.Originality value - The present article is the first that uses Facebook as an instrument to study immigrants. Therefore its adoption represents a great challenge in the social research field because there are many barriers of access and search. It also proposes a novel mix of traditional methodologies updated with the use of new virtual possibilities of studying hard to reach populations, especially in areas of social research where the contributions of these methods are less developed.
Journal Article