Ray Chambers and Robert Clark
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.001.0001
- Subject:
- Mathematics, Probability / Statistics
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 ...
More
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.Less
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.
Raymond L. Chambers and Robert G. Clark
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.003.0013
- Subject:
- Mathematics, Probability / Statistics
Estimation for multipurpose surveys considers the situation of a survey with many output variables and multiple auxiliary variables. In this context, linear estimation based on a single set of ...
More
Estimation for multipurpose surveys considers the situation of a survey with many output variables and multiple auxiliary variables. In this context, linear estimation based on a single set of multipurpose sample weights represents the dominant approach in sample survey estimation. The development in this chapter includes linear calibrated weighting and ridge weighting, i.e. sample weighting based on a minimum mean squared error ridge regression fit to the survey data, with the latter approach specifically aimed at reducing the incidence of non-positive survey weights caused by sample imbalance and/or model overspecification. The extension to non-parametric regression modelling is also considered, and the important trade-off between sample balance and sample weight variability is discussed.Less
Estimation for multipurpose surveys considers the situation of a survey with many output variables and multiple auxiliary variables. In this context, linear estimation based on a single set of multipurpose sample weights represents the dominant approach in sample survey estimation. The development in this chapter includes linear calibrated weighting and ridge weighting, i.e. sample weighting based on a minimum mean squared error ridge regression fit to the survey data, with the latter approach specifically aimed at reducing the incidence of non-positive survey weights caused by sample imbalance and/or model overspecification. The extension to non-parametric regression modelling is also considered, and the important trade-off between sample balance and sample weight variability is discussed.
Raymond L. Chambers and Robert G. Clark
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780198566625
- eISBN:
- 9780191738449
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566625.003.0014
- Subject:
- Mathematics, Probability / Statistics
Inference for domains considers an important aspect of sample survey inference, where estimates are required not for the population actually surveyed but for subgroups of it typically referred to as ...
More
Inference for domains considers an important aspect of sample survey inference, where estimates are required not for the population actually surveyed but for subgroups of it typically referred to as domains. The main emphasis is on homogeneous domains whose population memberships, and hence sizes, are unknown, since this is the most common case. The case where the domain size is known is also discussed, as is linear domain estimation based on multipurpose sample weights.Less
Inference for domains considers an important aspect of sample survey inference, where estimates are required not for the population actually surveyed but for subgroups of it typically referred to as domains. The main emphasis is on homogeneous domains whose population memberships, and hence sizes, are unknown, since this is the most common case. The case where the domain size is known is also discussed, as is linear domain estimation based on multipurpose sample weights.
Seth J. Schwartz
- Published in print:
- 2022
- Published Online:
- November 2021
- ISBN:
- 9780190095918
- eISBN:
- 9780197612057
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190095918.003.0016
- Subject:
- Psychology, Social Psychology
This chapter addresses work with regionally or nationally representative datasets, which are often used in disciplines like public health, sociology, demography, and political science. Some of these ...
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This chapter addresses work with regionally or nationally representative datasets, which are often used in disciplines like public health, sociology, demography, and political science. Some of these datasets are publicly available, whereas others are proprietary and can be accessed only by developing a formal proposal and paying a fee. The chapter lays out the types of claims and research questions that datasets are best equipped to support or address. Tips for using these datasets are provided, such as understanding the sampling strategy and the labeling of variables in the codebook. Challenges inherent in using public-use and proprietary datasets are also enumerated.Less
This chapter addresses work with regionally or nationally representative datasets, which are often used in disciplines like public health, sociology, demography, and political science. Some of these datasets are publicly available, whereas others are proprietary and can be accessed only by developing a formal proposal and paying a fee. The chapter lays out the types of claims and research questions that datasets are best equipped to support or address. Tips for using these datasets are provided, such as understanding the sampling strategy and the labeling of variables in the codebook. Challenges inherent in using public-use and proprietary datasets are also enumerated.
