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COVID-19 A data perspective

COVID-19: A data perspective: Explore key economic trends and social challenges that arise as the COVID-19 situation evolves.

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All (4) ((4 results))

  • Articles and reports: 12-001-X199400214425
    Description:

    We present a formal model based sampling solution to the problem of estimating list frame size based on capture-recapture sampling which has been widely used for animal populations and for adjusting the US census. For two incomplete lists it is easy to estimate total frame size using the Lincoln-Petersen estimator. This estimator is model based with a key assumption being independence of the two lists. Once an estimator of the population (frame) size has been obtained it is possible to obtain an estimator of a population total for some characteristic if a sample of units has that characteristic measured. A discussion of the properties of this estimator will be presented. An example where the establishments are fishing boats taking part in an ocean fishery off the Atlantic Coast of the United States is presented. Estimation of frame size and then population totals using a capture-recapture model is likely to have broad application in establishment surveys due to practicality and cost savings but possible biases due to assumption violations need to be considered.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400114428
    Description:

    Recently, much effort has been directed towards counting and characterizing the homeless. Most of this work, however, has focused on homeless persons in urban areas. In this paper, we describe efforts to estimate the rate of homelessness in nonurban counties in Ohio. The methods for locating homeless persons and even the definition of homelessness are different in rural areas where there are fewer institutions for sheltering and feeding the homeless. There may also be a problem with using standard survey sampling estimators, which typically require large population sizes, large sample sizes, and small sampling fractions. We describe a survey of homeless persons in nonurban Ohio and present a simulation study to assess the usefulness of standard estimators for a population proportion from a stratified cluster sample.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114432
    Description:

    Two sampling strategies for estimation of population mean in overlapping clusters with known population size have been proposed by Singh (1988). In this paper, ratio estimators under these two strategies are studied assuming the actual population size to be unknown, which is the more realistic situation in sample surveys. The sampling efficiencies of the two strategies are compared and a numerical illustration is provided.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114434
    Description:

    In estimation for small areas it is common to borrow strength from other small areas since the direct survey estimates often have large sampling variability. A class of methods called composite estimation addresses the problem by using a linear combination of direct and synthetic estimators. The synthetic component is based on a model which connects small area means cross-sectionally (over areas) and/or over time. A cross-sectional empirical best linear unbiased predictor (EBLUP) is a composite estimator based on a linear regression model with small area effects. In this paper we consider three models to generalize the cross-sectional EBLUP to use data from more than one time point. In the first model, regression parameters are random and serially dependent but the small area effects are assumed to be independent over time. In the second model, regression parameters are nonrandom and may take common values over time but the small area effects are serially dependent. The third model is more general in that regression parameters and small area effects are assumed to be serially dependent. The resulting estimators, as well as some cross-sectional estimators, are evaluated using bi-annual data from Statistics Canada’s National Farm Survey and January Farm Survey.

    Release date: 1994-06-15
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Articles and reports (4)

Articles and reports (4) ((4 results))

  • Articles and reports: 12-001-X199400214425
    Description:

    We present a formal model based sampling solution to the problem of estimating list frame size based on capture-recapture sampling which has been widely used for animal populations and for adjusting the US census. For two incomplete lists it is easy to estimate total frame size using the Lincoln-Petersen estimator. This estimator is model based with a key assumption being independence of the two lists. Once an estimator of the population (frame) size has been obtained it is possible to obtain an estimator of a population total for some characteristic if a sample of units has that characteristic measured. A discussion of the properties of this estimator will be presented. An example where the establishments are fishing boats taking part in an ocean fishery off the Atlantic Coast of the United States is presented. Estimation of frame size and then population totals using a capture-recapture model is likely to have broad application in establishment surveys due to practicality and cost savings but possible biases due to assumption violations need to be considered.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400114428
    Description:

    Recently, much effort has been directed towards counting and characterizing the homeless. Most of this work, however, has focused on homeless persons in urban areas. In this paper, we describe efforts to estimate the rate of homelessness in nonurban counties in Ohio. The methods for locating homeless persons and even the definition of homelessness are different in rural areas where there are fewer institutions for sheltering and feeding the homeless. There may also be a problem with using standard survey sampling estimators, which typically require large population sizes, large sample sizes, and small sampling fractions. We describe a survey of homeless persons in nonurban Ohio and present a simulation study to assess the usefulness of standard estimators for a population proportion from a stratified cluster sample.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114432
    Description:

    Two sampling strategies for estimation of population mean in overlapping clusters with known population size have been proposed by Singh (1988). In this paper, ratio estimators under these two strategies are studied assuming the actual population size to be unknown, which is the more realistic situation in sample surveys. The sampling efficiencies of the two strategies are compared and a numerical illustration is provided.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114434
    Description:

    In estimation for small areas it is common to borrow strength from other small areas since the direct survey estimates often have large sampling variability. A class of methods called composite estimation addresses the problem by using a linear combination of direct and synthetic estimators. The synthetic component is based on a model which connects small area means cross-sectionally (over areas) and/or over time. A cross-sectional empirical best linear unbiased predictor (EBLUP) is a composite estimator based on a linear regression model with small area effects. In this paper we consider three models to generalize the cross-sectional EBLUP to use data from more than one time point. In the first model, regression parameters are random and serially dependent but the small area effects are assumed to be independent over time. In the second model, regression parameters are nonrandom and may take common values over time but the small area effects are serially dependent. The third model is more general in that regression parameters and small area effects are assumed to be serially dependent. The resulting estimators, as well as some cross-sectional estimators, are evaluated using bi-annual data from Statistics Canada’s National Farm Survey and January Farm Survey.

    Release date: 1994-06-15
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