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  • 41. Empirical comparison of small area estimation methods for the Italian Labour Force Survey Archived
    Articles and reports: 12-001-X199400214419
    Description:

    The study was undertaken to evaluate some alternative small areas estimators to produce level estimates for unplanned domains from the Italian Labour Force Sample Survey. In our study, the small areas are the Health Service Areas, which are unplanned sub-regional territorial domains and were not isolated at the time of sample design and thus cut across boundaries of the design strata. We consider the following estimators: post-stratified ratio, synthetic, composite expressed as linear combination of synthetic and of post-stratified ratio, and sample size dependent. For all the estimators considered in this study, the average percent relative biases and the average relative mean square errors were obtained in a Monte Carlo study in which the sample design was simulated using data from the 1981 Italian Census.

    Release date: 1994-12-15
  • 42. Dual system estimation of census undercount in the presence of matching error Archived
    Articles and reports: 12-001-X199400214422
    Description:

    Dual system estimation (DSE) has been used since 1950 by the U.S. Bureau of Census for coverage evaluation of the decennial census. In the DSE approach, data from a sample is combined with data from the census to estimate census undercount and overcount. DSE relies upon the assumption that individuals in both the census and the sample can be matched perfectly. The unavoidable mismatches and erroneous nonmatches reduce the accuracy of the DSE. This paper reconsiders the DSE approach by relaxing the perfect matching assumption and proposes models to describe two types of matching errors, false matches of nonmatching cases and false nonmatches of matching cases. Methods for estimating population total and census undercount are presented and illustrated using data from 1986 Los Angeles test census and 1990 Decennial Census.

    Release date: 1994-12-15
  • 43. Estimating the rate of rural homelessness: A study of nonurban Ohio Archived
    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
  • 44. Regression weighting in the presence of nonresponse with application to the 1987-1988 Nationwide Food Consumption Survey Archived
    Articles and reports: 12-001-X199400114429
    Description:

    A regression weight generation procedure is applied to the 1987-1988 Nationwide Food Consumption Survey of the U.S. Department of Agriculture. Regression estimation was used because of the large nonresponse in the survey. The regression weights are generalized least squares weights modified so that all weights are positive and so that large weights are smaller than the least squares weights. It is demonstrated that the regression estimator has the potential for large reductions in mean square error relative to the simple direct estimator in the presence of nonresponse.

    Release date: 1994-06-15
  • 45. Bibliography on capture-recapture modelling with application to census undercount adjustment Archived
    Articles and reports: 12-001-X199200114494
    Description:

    This article presents a selected annotated bibliography of the literature on capture-recapture (dual system) estimation of population size, on extensions to the basic methodology, and the application of these techniques in the context of census undercount estimation.

    Release date: 1992-06-15
  • 46. Should we have adjusted the U.S. census of 1980? Archived
    Articles and reports: 12-001-X199200114499
    Description:

    This paper reviews some of the arguments for and against adjusting the U.S. census of 1980, and the decision of the court.

    Release date: 1992-06-15
  • 47. Benchmarking of economic time series Archived
    Articles and reports: 12-001-X199000214531
    Description:

    Benchmarking is a method of improving estimates from a sub-annual survey with the help of corresponding estimates from an annual survey. For example, estimates of monthly retail sales might be improved using estimates from the annual survey. This article deals, first with the problem posed by the benchmarking of time series produced by economic surveys, and then reviews the most relevant methods for solving this problem. Next, two new statistical methods are proposed, based on a non-linear model for sub-annual data. The benchmarked estimates are then obtained by applying weighted least squares.

    Release date: 1990-12-14
  • 48. Analysis of repeated surveys Archived
    Articles and reports: 12-001-X199000214537
    Description:

    Repeated surveys in which a portion of the units are observed at more than one time point and some units are not observed at some time points are of primary interest. Least squares estimation for such surveys is reviewed. Included in the discussion are estimation procedures in which existing estimates are not revised when new data become available. Also considered are techniques for the estimation of longitudinal parameters, such as gross change tables. Estimation for a repeated survey of land use conducted by the U.S. Soil Conservation Service is described. The effects of measurement error on gross change estimates is illustrated and it is shown that survey designs constructed to enable estimation of the parameters of the measurement error process can be very efficient.

    Release date: 1990-12-14
  • 49. A historical perspective on the institutional bases for survey research in the United States Archived
    Articles and reports: 12-001-X199000114559
    Description:

    The basic theme of this paper is that the development of survey methods in the technical sense can only be well understood in the context of the development of the institutions through which survey-taking is done. Thus we consider here survey methods in the large, in order to better prepare the reader for consideration of more formal methodological developments in sampling theory in the mathematical statistics sense. After a brief introduction, we give a historical overview of the evolution of institutional and contextual factors in Europe and the United States, up through the early part of the twentieth century, concentrating on governmental activities. We then focus on the emergence of institutional bases for survey research in the United States, primarily in the 1930s and 1940s. In a separate section, we take special note of the role of the U.S. Bureau of the Census in the study of non-sampling errors that was initiated in the 1940s and 1950s. Then, we look at three areas of basic change in survey methodology since 1960.

    Release date: 1990-06-15
  • 50. Randomized response sampling from dichotomous populations with continuous randomization Archived
    Articles and reports: 12-001-X198900214566
    Description:

    A randomized response model for sampling from dichotomous populations is developed in this paper. The model permits the use of continuous randomization and multiple trials per respondent. The special case of randomization with normal distributions is considered, and a computer simulation of such a sampling procedure is presented as an initial exploration into the effects such a scheme has on the amount of information in the sample. A portable electronic device is discussed which would implement the presented model. The results of a study taken, using the electronic randomizing device, is presented. The results show that randomized response sampling is a superior technique to direct questioning for at least some sensitive questions.

    Release date: 1989-12-15
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Articles and reports (61)

Articles and reports (61) (0 to 10 of 61 results)

  • 1. Bayesian inference for a variance component model using pairwise composite likelihood with survey data
    Articles and reports: 12-001-X202200100002
    Description:

    We consider an intercept only linear random effects model for analysis of data from a two stage cluster sampling design. At the first stage a simple random sample of clusters is drawn, and at the second stage a simple random sample of elementary units is taken within each selected cluster. The response variable is assumed to consist of a cluster-level random effect plus an independent error term with known variance. The objects of inference are the mean of the outcome variable and the random effect variance. With a more complex two stage sampling design, the use of an approach based on an estimated pairwise composite likelihood function has appealing properties. Our purpose is to use our simpler context to compare the results of likelihood inference with inference based on a pairwise composite likelihood function that is treated as an approximate likelihood, in particular treated as the likelihood component in Bayesian inference. In order to provide credible intervals having frequentist coverage close to nominal values, the pairwise composite likelihood function and corresponding posterior density need modification, such as a curvature adjustment. Through simulation studies, we investigate the performance of an adjustment proposed in the literature, and find that it works well for the mean but provides credible intervals for the random effect variance that suffer from under-coverage. We propose possible future directions including extensions to the case of a complex design.

    Release date: 2022-06-21
  • 2. Cost optimal sampling for the integrated observation of different populations
    Articles and reports: 12-001-X201900300004
    Description:

    Social or economic studies often need to have a global view of society. For example, in agricultural studies, the characteristics of farms can be linked to the social activities of individuals. Hence, studies of a given phenomenon should be done by considering variables of interest referring to different target populations that are related to each other. In order to get an insight into an underlying phenomenon, the observations must be carried out in an integrated way, in which the units of a given population have to be observed jointly with related units of the other population. In the agricultural example, this means that a sample of rural households should be selected that have some relationship with the farm sample to be used for the study. There are several ways to select integrated samples. This paper studies the problem of defining an optimal sampling strategy for this situation: the solution proposed minimizes the sampling cost, ensuring a predefined estimation precision for the variables of interest (of either one or both populations) describing the phenomenon. Indirect sampling provides a natural framework for this setting since the units belonging to a population can become carriers of information on another population that is the object of a given survey. The problem is studied for different contexts which characterize the information concerning the links available in the sampling design phase, ranging from situations in which the links among the different units are known in the design phase to a situation in which the available information on links is very poor. An empirical study of agricultural data for a developing country is presented. It shows how controlling the inclusion probabilities at the design phase using the available information (namely the links) is effective, can significantly reduce the errors of the estimates for the indirectly observed population. The need for good models for predicting the unknown variables or the links is also demonstrated.

    Release date: 2019-12-17
  • 3. An alternative way of estimating a cumulative logistic model with complex survey data
    Articles and reports: 12-001-X201900200007
    Description:

    When fitting an ordered categorical variable with L > 2 levels to a set of covariates onto complex survey data, it is common to assume that the elements of the population fit a simple cumulative logistic regression model (proportional-odds logistic-regression model). This means the probability that the categorical variable is at or below some level is a binary logistic function of the model covariates. Moreover, except for the intercept, the values of the logistic-regression parameters are the same at each level. The conventional “design-based” method used for fitting the proportional-odds model is based on pseudo-maximum likelihood. We compare estimates computed using pseudo-maximum likelihood with those computed by assuming an alternative design-sensitive robust model-based framework. We show with a simple numerical example how estimates using the two approaches can differ. The alternative approach is easily extended to fit a general cumulative logistic model, in which the parallel-lines assumption can fail. A test of that assumption easily follows.

    Release date: 2019-06-27
  • 4. Weighted censored quantile regression Archived
    Articles and reports: 12-001-X201900100004
    Description:

    In this paper, we make use of auxiliary information to improve the efficiency of the estimates of the censored quantile regression parameters. Utilizing the information available from previous studies, we computed empirical likelihood probabilities as weights and proposed weighted censored quantile regression. Theoretical properties of the proposed method are derived. Our simulation studies shown that our proposed method has advantages compared to standard censored quantile regression.

    Release date: 2019-05-07
  • 5. Measurement error in small area estimation: Functional versus structural versus naïve models Archived
    Articles and reports: 12-001-X201900100005
    Description:

    Small area estimation using area-level models can sometimes benefit from covariates that are observed subject to random errors, such as covariates that are themselves estimates drawn from another survey. Given estimates of the variances of these measurement (sampling) errors for each small area, one can account for the uncertainty in such covariates using measurement error models (e.g., Ybarra and Lohr, 2008). Two types of area-level measurement error models have been examined in the small area estimation literature. The functional measurement error model assumes that the underlying true values of the covariates with measurement error are fixed but unknown quantities. The structural measurement error model assumes that these true values follow a model, leading to a multivariate model for the covariates observed with error and the original dependent variable. We compare and contrast these two models with the alternative of simply ignoring measurement error when it is present (naïve model), exploring the consequences for prediction mean squared errors of use of an incorrect model under different underlying assumptions about the true model. Comparisons done using analytic formulas for the mean squared errors assuming model parameters are known yield some surprising results. We also illustrate results with a model fitted to data from the U.S. Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) Program.

    Release date: 2019-05-07
  • 6. Comparison of the conditional bias and Kokic and Bell methods for Poisson and stratified sampling Archived
    Articles and reports: 12-001-X201800254961
    Description:

    In business surveys, it is common to collect economic variables with highly skewed distribution. In this context, winsorization is frequently used to address the problem of influential values. In stratified simple random sampling, there are two methods for selecting the thresholds involved in winsorization. This article comprises two parts. The first reviews the notations and the concept of a winsorization estimator. The second part details the two methods and extends them to the case of Poisson sampling, and then compares them on simulated data sets and on the labour cost and structure of earnings survey carried out by INSEE.

    Release date: 2018-12-20
  • 7. Sample survey theory and methods: Past, present, and future directions Archived
    Articles and reports: 12-001-X201700254888
    Description:

    We discuss developments in sample survey theory and methods covering the past 100 years. Neyman’s 1934 landmark paper laid the theoretical foundations for the probability sampling approach to inference from survey samples. Classical sampling books by Cochran, Deming, Hansen, Hurwitz and Madow, Sukhatme, and Yates, which appeared in the early 1950s, expanded and elaborated the theory of probability sampling, emphasizing unbiasedness, model free features, and designs that minimize variance for a fixed cost. During the period 1960-1970, theoretical foundations of inference from survey data received attention, with the model-dependent approach generating considerable discussion. Introduction of general purpose statistical software led to the use of such software with survey data, which led to the design of methods specifically for complex survey data. At the same time, weighting methods, such as regression estimation and calibration, became practical and design consistency replaced unbiasedness as the requirement for standard estimators. A bit later, computer-intensive resampling methods also became practical for large scale survey samples. Improved computer power led to more sophisticated imputation for missing data, use of more auxiliary data, some treatment of measurement errors in estimation, and more complex estimation procedures. A notable use of models was in the expanded use of small area estimation. Future directions in research and methods will be influenced by budgets, response rates, timeliness, improved data collection devices, and availability of auxiliary data, some of which will come from “Big Data”. Survey taking will be impacted by changing cultural behavior and by a changing physical-technical environment.

    Release date: 2017-12-21
  • 8. Nonresponse adjustments with misspecified models in stratified designs Archived
    Articles and reports: 12-001-X201600114546
    Description:

    Adjusting the base weights using weighting classes is a standard approach for dealing with unit nonresponse. A common approach is to create nonresponse adjustments that are weighted by the inverse of the assumed response propensity of respondents within weighting classes under a quasi-randomization approach. Little and Vartivarian (2003) questioned the value of weighting the adjustment factor. In practice the models assumed are misspecified, so it is critical to understand the impact of weighting might have in this case. This paper describes the effects on nonresponse adjusted estimates of means and totals for population and domains computed using the weighted and unweighted inverse of the response propensities in stratified simple random sample designs. The performance of these estimators under different conditions such as different sample allocation, response mechanism, and population structure is evaluated. The findings show that for the scenarios considered the weighted adjustment has substantial advantages for estimating totals and using an unweighted adjustment may lead to serious biases except in very limited cases. Furthermore, unlike the unweighted estimates, the weighted estimates are not sensitive to how the sample is allocated.

    Release date: 2016-06-22
  • 9. Combining link-tracing sampling and cluster sampling to estimate the size of a hidden population in presence of heterogeneous link-probabilities Archived
    Articles and reports: 12-001-X201500214238
    Description:

    Félix-Medina and Thompson (2004) proposed a variant of link-tracing sampling to sample hidden and/or hard-to-detect human populations such as drug users and sex workers. In their variant, an initial sample of venues is selected and the people found in the sampled venues are asked to name other members of the population to be included in the sample. Those authors derived maximum likelihood estimators of the population size under the assumption that the probability that a person is named by another in a sampled venue (link-probability) does not depend on the named person (homogeneity assumption). In this work we extend their research to the case of heterogeneous link-probabilities and derive unconditional and conditional maximum likelihood estimators of the population size. We also propose profile likelihood and bootstrap confidence intervals for the size of the population. The results of simulations studies carried out by us show that in presence of heterogeneous link-probabilities the proposed estimators perform reasonably well provided that relatively large sampling fractions, say larger than 0.5, be used, whereas the estimators derived under the homogeneity assumption perform badly. The outcomes also show that the proposed confidence intervals are not very robust to deviations from the assumed models.

    Release date: 2015-12-17
  • 10. Generalized framework for defining the optimal inclusion probabilities of one-stage sampling designs for multivariate and multi-domain surveys Archived
    Articles and reports: 12-001-X201500114149
    Description:

    This paper introduces a general framework for deriving the optimal inclusion probabilities for a variety of survey contexts in which disseminating survey estimates of pre-established accuracy for a multiplicity of both variables and domains of interest is required. The framework can define either standard stratified or incomplete stratified sampling designs. The optimal inclusion probabilities are obtained by minimizing costs through an algorithm that guarantees the bounding of sampling errors at the domains level, assuming that the domain membership variables are available in the sampling frame. The target variables are unknown, but can be predicted with suitable super-population models. The algorithm takes properly into account this model uncertainty. Some experiments based on real data show the empirical properties of the algorithm.

    Release date: 2015-06-29
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