Sampling from a finite population

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Sampling from a finite population

01 Jan 1981-

About: The article was published on 1981-01-01 and is currently open access. It has received 217 citations till now. The article focuses on the topics: Sampling (statistics) & Sampling design.

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Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition

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Raymond J. Carroll, David Ruppert, Leonard A. Stefanski, Ciprian M. Crainiceanu¹•Institutions (1)

North Carolina State University¹

21 Jun 2006

TL;DR: The second edition of Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revised and re-released in this paper, with a new cover and a new introduction.

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Abstract: It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex

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1,515 citations

Journal Article•DOI•

Covariate balance in simple, stratified and clustered comparative studies

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Ben B. Hansen¹, Jake Bowers²•Institutions (2)

University of Michigan¹, University of Illinois at Urbana–Champaign²

01 May 2008-Statistical Science

TL;DR: In randomized experiments, treatment and control groups should be roughly the same in their distributions of pre-treatment variables as mentioned in this paper. But how nearly so? Can descriptive comparisons meaningfully be paired with significance tests? If so, should there be several such tests, one for each pretreatment variable, or should a single, omnibus test?

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Abstract: In randomized experiments, treatment and control groups should be roughly the same—balanced—in their distributions of pre- treatment variables. But how nearly so? Can descriptive comparisons meaningfully be paired with significance tests? If so, should there be several such tests, one for each pretreatment variable, or should there be a single, omnibus test? Could such a test be engineered to give eas- ily computed p-values that are reliable in samples of moderate size, or would simulation be needed for reliable calibration? What new con- cerns are introduced by random assignment of clusters? Which tests of balance would be optimal? To address these questions, Fisher's randomization inference is ap- plied to the question of balance. Its application suggests the reversal of published conclusions about two studies, one clinical and the other a field experiment in political participation.

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404 citations

Journal Article•DOI•

Efficient balanced sampling: The cube method

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Jean-Claude Deville, Yves Tillé

01 Dec 2004-Biometrika

TL;DR: The cube method as discussed by the authors selects approximately balanced samples with equal or unequal inclusion probabilities and any number of auxiliary variables, depending on the correlations of these variables with the controlled variables, i.e., the correlation of the variables of interest with the control variables.

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Abstract: A balanced sampling design is defined by the property that the Horvitz-Thompson estimators of the population totals of a set of auxiliary variables equal the known totals of these variables. Therefore the variances of estimators of totals of all the variables of interest are reduced, depending on the correlations of these variables with the controlled variables. In this paper, we develop a general method, called the cube method, for selecting approximately balanced samples with equal or unequal inclusion probabilities and any number of auxiliary variables.

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242 citations

Book•

Statistical demography and forecasting

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Juha Alho, Bruce D. Spencer

01 Jan 2005

TL;DR: In this paper, the authors present a model for counting and survival of a population in the US population, using regression models for counting, counting, and survival, as well as counting.

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Abstract: Sources of Demographic Data.- Sampling Designs and Inference.- Waiting Times and Their Statistical Estimation.- Regression Models for Counts and Survival.- Multistate Models and Cohort-Component Book-Keeping.- Approaches to Forecasting Demographic Rates.- Uncertainty in Demographic Forecasts: Concepts, Issues, and Evidence.- Statistical Propagation of Error in Forecasting.- Errors in Census Numbers.- Financial Applications.- Decision Analysis and Small Area Estimates.

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191 citations

Journal Article•DOI•

Spatially Balanced Sampling through the Pivotal Method

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Anton Grafström¹, Niklas L.P. Lundström², Lina Schelin²•Institutions (2)

Swedish University of Agricultural Sciences¹, Umeå University²

01 Jun 2012-Biometrics

TL;DR: A simple method to select a spatially balanced sample using equal or unequal inclusion probabilities is presented and achieves a high degree of spatial balance and is therefore efficient for populations with trends.

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Abstract: A simple method to select a spatially balanced sample using equal or unequal inclusion probabilities is presented. For populations with spatial trends in the variables of interest, the estimation can be much improved by selecting samples that are well spread over the population. The method can be used for any number of dimensions and can hence also select spatially balanced samples in a space spanned by several auxiliary variables. Analysis and examples indicate that the suggested method achieves a high degree of spatial balance and is therefore efficient for populations with trends.

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162 citations

Cites methods from "Sampling from a finite population"

...Also the conditional Poisson (CP) design (Hájek, 1981; Tillé, 2006, Chapter 5) is included as a reference to compare against the other methods....
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...Hájek (1981) and Rosén (1997a, 1997b). An even better option is to use a local mean variance estimator, such as the one derived by Stevens and Olsen (2003). It seemed to produce good variance estimates for the GRTS method....
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...Also the conditional Poisson (CP) design (Hájek, 1981; Tillé, 2006, Chapter 5) is included as a reference to compare against the other methods. When the inclusion probabilities are equal, the CP design corresponds to SRS. The CP design totally ignores the spatial aspect because the design is unaffected by a relocation of units within the population. See Section 6 for implementation details. Spatial balance can be measured in different ways. We will use the approach of Voronoi polygons suggested by Stevens and Olsen (2004). We assume that n = ∑ i∈U πi is a positive integer....
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...…entropy and a simple approximative variance estimator, using only first-order inclusion probabilities is the Hájek–Rosén estimator V̂HR(Ŷ ) = n n − 1 ∑ i∈s (1 − πi ) ( yi πi − ∑ j∈s yj (1 − πj )/πj∑ j∈s (1 − πj ) )2 , (5) where s is the sample, cf. Hájek (1981) and Rosén (1997a, 1997b)....
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Sampling from a finite population

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Cites methods from "Sampling from a finite population"

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