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- Articles and reports: 11-536-X200900110803Description:
"Classical GREG estimator" is used here to refer to the generalized regression estimator extensively discussed for example in Särndal, Swensson and Wretman (1992). This paper summarize some recent extensions of the classical GREG estimator when applied to the estimation of totals for population subgroups or domains. GREG estimation was introduced for domain estimation in Särndal (1981, 1984), Hidiroglou and Särndal (1985) and Särndal and Hidiroglou (1989), and was developed further in Estevao, Hidiroglou and Särndal (1995). For the classical GREG estimator, fixed-effects linear model serves as the underlying working or assisting model, and aggregate-level auxiliary totals are incorporated in the estimation procedure. In some recent developments, an access to unit-level auxiliary data is assumed for GREG estimation for domains. Obviously, an access to micro-merged register and survey data involves much flexibility for domain estimation. This view has been adopted for GREG estimation for example in Lehtonen and Veijanen (1998), Lehtonen, Särndal and Veijanen (2003, 2005), and Lehtonen, Myrskylä, Särndal and Veijanen (2007). These extensions cover the cases of continuous and binary or polytomous response variables, use of generalized linear mixed models as assisting models, and unequal probability sampling designs. Relative merits and challenges of the various GREG estimators will be discussed.
Release date: 2009-08-11 - 2. Comparison of design-based and model-based methods in analyzing complex health survey data: A case study ArchivedArticles and reports: 11-522-X20020016708Description:
In this paper, we discuss the analysis of complex health survey data by using multivariate modelling techniques. Main interests are in various design-based and model-based methods that aim at accounting for the design complexities, including clustering, stratification and weighting. Methods covered include generalized linear modelling based on pseudo-likelihood and generalized estimating equations, linear mixed models estimated by restricted maximum likelihood, and hierarchical Bayes techniques using Markov Chain Monte Carlo (MCMC) methods. The methods will be compared empirically, using data from an extensive health interview and examination survey conducted in Finland in 2000 (Health 2000 Study).
The data of the Health 2000 Study were collected using personal interviews, questionnaires and clinical examinations. A stratified two-stage cluster sampling design was used in the survey. The sampling design involved positive intra-cluster correlation for many study variables. For a closer investigation, we selected a small number of study variables from the health interview and health examination phases. In many cases, the different methods produced similar numerical results and supported similar statistical conclusions. Methods that failed to account for the design complexities sometimes led to conflicting conclusions. We also discuss the application of the methods in this paper by using standard statistical software products.
Release date: 2004-09-13 - Articles and reports: 12-001-X20030016605Description:
In this paper, we examine the effects of model choice on different types of estimators for totals of domains (including small domains or small areas) for a sampled finite population. The paper asks how different estimator types compare for a common underlying model statement. We argue that estimator type - synthetic, generalized regression (GREG), composite, empirical best linear unbiased predicition (EBLUP), hierarchical Bayes, and so on - is one important aspect of domain estimation, and that the choice of the model, including its parameters and effects, is a second aspect, conceptually different from the first. Earlier work has not always made this distinction clear. For a given estimator type, one can derive different estimators, depending on the choice of model. In recent literature, a number of estimator types have been proposed, but there is relatively little impartial comparisons made among them. In this paper, we discuss three types: synthetic, GREG, and, to a limited extent, composite. We show that model improvement - the transition from a weaker to a stronger model - has very different effects on the different estimator types. We also show that the difference in accuracy between the different estimator types depends on the choice of model. For a well-specified model, the difference in accuracy between synthetic and GREG is negligible, but it can be substantial if the model is mis-specified. The synthetic type then tends to be highly inaccurate. We rely partly on theoretical results (for simple random sampling only) and partly on empirical results. The empirical results are based on simulations with repeated samples drawn from two finite populations, one artificially constructed, the other constructed from the real data of the Finnish Labour Force Survey.
Release date: 2003-07-31 - 4. Logistic generalized regression estimators ArchivedArticles and reports: 12-001-X19980013909Description:
In this paper we study the model-assisted estimation of class frequencies of a discrete response variable by a new survey estimation method, which is closely related to generalized regression estimation. In generalized regression estimation the available auxiliary data are incorporated in the estimation procedure by a linear model fit. Instead of using a linear model for the class indicators, we describe the joint distribution of the class indicators by a multinomial logistic model. Logistic generalized regression estimators are introduced for class frequencies in a population and domains. Monte Carlo experiments were carried out for simulated data and for real data taken from the Labour Force Survey conducted monthly by Statistics Finland. The logistic generalized regression estimation yielded better results than the ordinary regression estimation for small domains and particularly for small class frequencies.
Release date: 1998-07-31
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Articles and reports (4)
Articles and reports (4) ((4 results))
- Articles and reports: 11-536-X200900110803Description:
"Classical GREG estimator" is used here to refer to the generalized regression estimator extensively discussed for example in Särndal, Swensson and Wretman (1992). This paper summarize some recent extensions of the classical GREG estimator when applied to the estimation of totals for population subgroups or domains. GREG estimation was introduced for domain estimation in Särndal (1981, 1984), Hidiroglou and Särndal (1985) and Särndal and Hidiroglou (1989), and was developed further in Estevao, Hidiroglou and Särndal (1995). For the classical GREG estimator, fixed-effects linear model serves as the underlying working or assisting model, and aggregate-level auxiliary totals are incorporated in the estimation procedure. In some recent developments, an access to unit-level auxiliary data is assumed for GREG estimation for domains. Obviously, an access to micro-merged register and survey data involves much flexibility for domain estimation. This view has been adopted for GREG estimation for example in Lehtonen and Veijanen (1998), Lehtonen, Särndal and Veijanen (2003, 2005), and Lehtonen, Myrskylä, Särndal and Veijanen (2007). These extensions cover the cases of continuous and binary or polytomous response variables, use of generalized linear mixed models as assisting models, and unequal probability sampling designs. Relative merits and challenges of the various GREG estimators will be discussed.
Release date: 2009-08-11 - 2. Comparison of design-based and model-based methods in analyzing complex health survey data: A case study ArchivedArticles and reports: 11-522-X20020016708Description:
In this paper, we discuss the analysis of complex health survey data by using multivariate modelling techniques. Main interests are in various design-based and model-based methods that aim at accounting for the design complexities, including clustering, stratification and weighting. Methods covered include generalized linear modelling based on pseudo-likelihood and generalized estimating equations, linear mixed models estimated by restricted maximum likelihood, and hierarchical Bayes techniques using Markov Chain Monte Carlo (MCMC) methods. The methods will be compared empirically, using data from an extensive health interview and examination survey conducted in Finland in 2000 (Health 2000 Study).
The data of the Health 2000 Study were collected using personal interviews, questionnaires and clinical examinations. A stratified two-stage cluster sampling design was used in the survey. The sampling design involved positive intra-cluster correlation for many study variables. For a closer investigation, we selected a small number of study variables from the health interview and health examination phases. In many cases, the different methods produced similar numerical results and supported similar statistical conclusions. Methods that failed to account for the design complexities sometimes led to conflicting conclusions. We also discuss the application of the methods in this paper by using standard statistical software products.
Release date: 2004-09-13 - Articles and reports: 12-001-X20030016605Description:
In this paper, we examine the effects of model choice on different types of estimators for totals of domains (including small domains or small areas) for a sampled finite population. The paper asks how different estimator types compare for a common underlying model statement. We argue that estimator type - synthetic, generalized regression (GREG), composite, empirical best linear unbiased predicition (EBLUP), hierarchical Bayes, and so on - is one important aspect of domain estimation, and that the choice of the model, including its parameters and effects, is a second aspect, conceptually different from the first. Earlier work has not always made this distinction clear. For a given estimator type, one can derive different estimators, depending on the choice of model. In recent literature, a number of estimator types have been proposed, but there is relatively little impartial comparisons made among them. In this paper, we discuss three types: synthetic, GREG, and, to a limited extent, composite. We show that model improvement - the transition from a weaker to a stronger model - has very different effects on the different estimator types. We also show that the difference in accuracy between the different estimator types depends on the choice of model. For a well-specified model, the difference in accuracy between synthetic and GREG is negligible, but it can be substantial if the model is mis-specified. The synthetic type then tends to be highly inaccurate. We rely partly on theoretical results (for simple random sampling only) and partly on empirical results. The empirical results are based on simulations with repeated samples drawn from two finite populations, one artificially constructed, the other constructed from the real data of the Finnish Labour Force Survey.
Release date: 2003-07-31 - 4. Logistic generalized regression estimators ArchivedArticles and reports: 12-001-X19980013909Description:
In this paper we study the model-assisted estimation of class frequencies of a discrete response variable by a new survey estimation method, which is closely related to generalized regression estimation. In generalized regression estimation the available auxiliary data are incorporated in the estimation procedure by a linear model fit. Instead of using a linear model for the class indicators, we describe the joint distribution of the class indicators by a multinomial logistic model. Logistic generalized regression estimators are introduced for class frequencies in a population and domains. Monte Carlo experiments were carried out for simulated data and for real data taken from the Labour Force Survey conducted monthly by Statistics Finland. The logistic generalized regression estimation yielded better results than the ordinary regression estimation for small domains and particularly for small class frequencies.
Release date: 1998-07-31
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