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All (10) ((10 results))

  • Articles and reports: 12-001-X201900200006
    Description:

    This paper presents a new algorithm to solve the one-dimensional optimal stratification problem, which reduces to just determining stratum boundaries. When the number of strata H and the total sample size n are fixed, the stratum boundaries are obtained by minimizing the variance of the estimator of a total for the stratification variable. This algorithm uses the Biased Random Key Genetic Algorithm (BRKGA) metaheuristic to search for the optimal solution. This metaheuristic has been shown to produce good quality solutions for many optimization problems in modest computing times. The algorithm is implemented in the R package stratbr available from CRAN (de Moura Brito, do Nascimento Silva and da Veiga, 2017a). Numerical results are provided for a set of 27 populations, enabling comparison of the new algorithm with some competing approaches available in the literature. The algorithm outperforms simpler approximation-based approaches as well as a couple of other optimization-based approaches. It also matches the performance of the best available optimization-based approach due to Kozak (2004). Its main advantage over Kozak’s approach is the coupling of the optimal stratification with the optimal allocation proposed by de Moura Brito, do Nascimento Silva, Silva Semaan and Maculan (2015), thus ensuring that if the stratification bounds obtained achieve the global optimal, then the overall solution will be the global optimum for the stratification bounds and sample allocation.

    Release date: 2019-06-27

  • Articles and reports: 12-001-X201500214249
    Description:

    The problem of optimal allocation of samples in surveys using a stratified sampling plan was first discussed by Neyman in 1934. Since then, many researchers have studied the problem of the sample allocation in multivariate surveys and several methods have been proposed. Basically, these methods are divided into two classes: The first class comprises methods that seek an allocation which minimizes survey costs while keeping the coefficients of variation of estimators of totals below specified thresholds for all survey variables of interest. The second aims to minimize a weighted average of the relative variances of the estimators of totals given a maximum overall sample size or a maximum cost. This paper proposes a new optimization approach for the sample allocation problem in multivariate surveys. This approach is based on a binary integer programming formulation. Several numerical experiments showed that the proposed approach provides efficient solutions to this problem, which improve upon a ‘textbook algorithm’ and can be more efficient than the algorithm by Bethel (1985, 1989).

    Release date: 2015-12-17

  • Articles and reports: 11-522-X200800010988
    Description:

    Online data collection emerged in 1995 as an alternative approach for conducting certain types of consumer research studies and has grown in 2008. This growth has been primarily in studies where non-probability sampling methods are used. While online sampling has gained acceptance for some research applications, serious questions remain concerning online samples' suitability for research requiring precise volumetric measurement of the behavior of the U.S. population, particularly their travel behavior. This paper reviews literature and compares results from studies using probability samples and online samples to understand whether results differ from the two sampling approaches. The paper also demonstrates that online samples underestimate critical types of travel even after demographic and geographic weighting.

    Release date: 2009-12-03

  • Articles and reports: 12-001-X200800210761
    Description:

    Optimum stratification is the method of choosing the best boundaries that make strata internally homogeneous, given some sample allocation. In order to make the strata internally homogenous, the strata should be constructed in such a way that the strata variances for the characteristic under study be as small as possible. This could be achieved effectively by having the distribution of the main study variable known and create strata by cutting the range of the distribution at suitable points. If the frequency distribution of the study variable is unknown, it may be approximated from the past experience or some prior knowledge obtained at a recent study. In this paper the problem of finding Optimum Strata Boundaries (OSB) is considered as the problem of determining Optimum Strata Widths (OSW). The problem is formulated as a Mathematical Programming Problem (MPP), which minimizes the variance of the estimated population parameter under Neyman allocation subject to the restriction that sum of the widths of all the strata is equal to the total range of the distribution. The distributions of the study variable are considered as continuous with Triangular and Standard Normal density functions. The formulated MPPs, which turn out to be multistage decision problems, can then be solved using dynamic programming technique proposed by Bühler and Deutler (1975). Numerical examples are presented to illustrate the computational details. The results obtained are also compared with the method of Dalenius and Hodges (1959) with an example of normal distribution.

    Release date: 2008-12-23

  • Articles and reports: 12-001-X200800110611
    Description:

    In finite population sampling prior information is often available in the form of partial knowledge about an auxiliary variable, for example its mean may be known. In such cases, the ratio estimator and the regression estimator are often used for estimating the population mean of the characteristic of interest. The Polya posterior has been developed as a noninformative Bayesian approach to survey sampling. It is appropriate when little or no prior information about the population is available. Here we show that it can be extended to incorporate types of partial prior information about auxiliary variables. We will see that it typically yields procedures with good frequentist properties even in some problems where standard frequentist methods are difficult to apply.

    Release date: 2008-06-26

  • Articles and reports: 12-001-X20070019856
    Description:

    The concept of 'nearest proportional to size sampling designs' originated by Gabler (1987) is used to obtain an optimal controlled sampling design, ensuring zero selection probabilities to non-preferred samples. Variance estimation for the proposed optimal controlled sampling design using the Yates-Grundy form of the Horvitz-Thompson estimator is discussed. The true sampling variance of the proposed procedure is compared with that of the existing optimal controlled and uncontrolled high entropy selection procedures. The utility of the proposed procedure is demonstrated with the help of examples.

    Release date: 2007-06-28

  • Articles and reports: 11-522-X20040018752
    Description:

    This paper outlines some possible applications of the permanent sample of households ready to respond with respect to surveying difficult-to-reach population groups.

    Release date: 2005-10-27

  • Articles and reports: 11-522-X20040018753
    Description:

    For the estimation of low-income households, a supplementary sample is selected within a limited number of geographic areas. This paper presents the dual sample design used, along with scenarios considered and some findings that led to the choices made.

    Release date: 2005-10-27

  • Articles and reports: 11-522-X20030017729
    Description:

    This paper describes the design of the samples and analyses factors that affect the scope of the direct data collection for the first Integrated Census (IC) experiment.

    Release date: 2005-01-26

  • Articles and reports: 12-001-X19970023615
    Description:

    This paper demonstrates the utility of a multi-stage survey design that obtains a total count of health facilities and of the potential client population in an area. The design has been used for a state-level survey conducted in mid-1995 in Uttar Pradesh, India. The design involves a multi-stage, areal cluster sample, wherein the primary sampling unit is either an urban block or rural village. All health service delivery points, either self-standing facilities or distribution agents, in or formally assigned to the primary sampling unit are mapped, listed, and selected. A systematic sample of households is selected, and all resident females meeting predetermined eligibility criteria are interviewed. Sample weights for facilities and individuals are applied. For facilities, the weights are adjusted for survey response levels. The survey estimate of the total number of government facilities compares well against the total published counts. Similarly the female client population estimated in the survey compares well with the total enumerated in the 1991 census.

    Release date: 1998-03-12
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Analysis (10)

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  • Articles and reports: 12-001-X201900200006
    Description:

    This paper presents a new algorithm to solve the one-dimensional optimal stratification problem, which reduces to just determining stratum boundaries. When the number of strata H and the total sample size n are fixed, the stratum boundaries are obtained by minimizing the variance of the estimator of a total for the stratification variable. This algorithm uses the Biased Random Key Genetic Algorithm (BRKGA) metaheuristic to search for the optimal solution. This metaheuristic has been shown to produce good quality solutions for many optimization problems in modest computing times. The algorithm is implemented in the R package stratbr available from CRAN (de Moura Brito, do Nascimento Silva and da Veiga, 2017a). Numerical results are provided for a set of 27 populations, enabling comparison of the new algorithm with some competing approaches available in the literature. The algorithm outperforms simpler approximation-based approaches as well as a couple of other optimization-based approaches. It also matches the performance of the best available optimization-based approach due to Kozak (2004). Its main advantage over Kozak’s approach is the coupling of the optimal stratification with the optimal allocation proposed by de Moura Brito, do Nascimento Silva, Silva Semaan and Maculan (2015), thus ensuring that if the stratification bounds obtained achieve the global optimal, then the overall solution will be the global optimum for the stratification bounds and sample allocation.

    Release date: 2019-06-27

  • Articles and reports: 12-001-X201500214249
    Description:

    The problem of optimal allocation of samples in surveys using a stratified sampling plan was first discussed by Neyman in 1934. Since then, many researchers have studied the problem of the sample allocation in multivariate surveys and several methods have been proposed. Basically, these methods are divided into two classes: The first class comprises methods that seek an allocation which minimizes survey costs while keeping the coefficients of variation of estimators of totals below specified thresholds for all survey variables of interest. The second aims to minimize a weighted average of the relative variances of the estimators of totals given a maximum overall sample size or a maximum cost. This paper proposes a new optimization approach for the sample allocation problem in multivariate surveys. This approach is based on a binary integer programming formulation. Several numerical experiments showed that the proposed approach provides efficient solutions to this problem, which improve upon a ‘textbook algorithm’ and can be more efficient than the algorithm by Bethel (1985, 1989).

    Release date: 2015-12-17

  • Articles and reports: 11-522-X200800010988
    Description:

    Online data collection emerged in 1995 as an alternative approach for conducting certain types of consumer research studies and has grown in 2008. This growth has been primarily in studies where non-probability sampling methods are used. While online sampling has gained acceptance for some research applications, serious questions remain concerning online samples' suitability for research requiring precise volumetric measurement of the behavior of the U.S. population, particularly their travel behavior. This paper reviews literature and compares results from studies using probability samples and online samples to understand whether results differ from the two sampling approaches. The paper also demonstrates that online samples underestimate critical types of travel even after demographic and geographic weighting.

    Release date: 2009-12-03

  • Articles and reports: 12-001-X200800210761
    Description:

    Optimum stratification is the method of choosing the best boundaries that make strata internally homogeneous, given some sample allocation. In order to make the strata internally homogenous, the strata should be constructed in such a way that the strata variances for the characteristic under study be as small as possible. This could be achieved effectively by having the distribution of the main study variable known and create strata by cutting the range of the distribution at suitable points. If the frequency distribution of the study variable is unknown, it may be approximated from the past experience or some prior knowledge obtained at a recent study. In this paper the problem of finding Optimum Strata Boundaries (OSB) is considered as the problem of determining Optimum Strata Widths (OSW). The problem is formulated as a Mathematical Programming Problem (MPP), which minimizes the variance of the estimated population parameter under Neyman allocation subject to the restriction that sum of the widths of all the strata is equal to the total range of the distribution. The distributions of the study variable are considered as continuous with Triangular and Standard Normal density functions. The formulated MPPs, which turn out to be multistage decision problems, can then be solved using dynamic programming technique proposed by Bühler and Deutler (1975). Numerical examples are presented to illustrate the computational details. The results obtained are also compared with the method of Dalenius and Hodges (1959) with an example of normal distribution.

    Release date: 2008-12-23

  • Articles and reports: 12-001-X200800110611
    Description:

    In finite population sampling prior information is often available in the form of partial knowledge about an auxiliary variable, for example its mean may be known. In such cases, the ratio estimator and the regression estimator are often used for estimating the population mean of the characteristic of interest. The Polya posterior has been developed as a noninformative Bayesian approach to survey sampling. It is appropriate when little or no prior information about the population is available. Here we show that it can be extended to incorporate types of partial prior information about auxiliary variables. We will see that it typically yields procedures with good frequentist properties even in some problems where standard frequentist methods are difficult to apply.

    Release date: 2008-06-26

  • Articles and reports: 12-001-X20070019856
    Description:

    The concept of 'nearest proportional to size sampling designs' originated by Gabler (1987) is used to obtain an optimal controlled sampling design, ensuring zero selection probabilities to non-preferred samples. Variance estimation for the proposed optimal controlled sampling design using the Yates-Grundy form of the Horvitz-Thompson estimator is discussed. The true sampling variance of the proposed procedure is compared with that of the existing optimal controlled and uncontrolled high entropy selection procedures. The utility of the proposed procedure is demonstrated with the help of examples.

    Release date: 2007-06-28

  • Articles and reports: 11-522-X20040018752
    Description:

    This paper outlines some possible applications of the permanent sample of households ready to respond with respect to surveying difficult-to-reach population groups.

    Release date: 2005-10-27

  • Articles and reports: 11-522-X20040018753
    Description:

    For the estimation of low-income households, a supplementary sample is selected within a limited number of geographic areas. This paper presents the dual sample design used, along with scenarios considered and some findings that led to the choices made.

    Release date: 2005-10-27

  • Articles and reports: 11-522-X20030017729
    Description:

    This paper describes the design of the samples and analyses factors that affect the scope of the direct data collection for the first Integrated Census (IC) experiment.

    Release date: 2005-01-26

  • Articles and reports: 12-001-X19970023615
    Description:

    This paper demonstrates the utility of a multi-stage survey design that obtains a total count of health facilities and of the potential client population in an area. The design has been used for a state-level survey conducted in mid-1995 in Uttar Pradesh, India. The design involves a multi-stage, areal cluster sample, wherein the primary sampling unit is either an urban block or rural village. All health service delivery points, either self-standing facilities or distribution agents, in or formally assigned to the primary sampling unit are mapped, listed, and selected. A systematic sample of households is selected, and all resident females meeting predetermined eligibility criteria are interviewed. Sample weights for facilities and individuals are applied. For facilities, the weights are adjusted for survey response levels. The survey estimate of the total number of government facilities compares well against the total published counts. Similarly the female client population estimated in the survey compares well with the total enumerated in the 1991 census.

    Release date: 1998-03-12
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