The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Carlerik Sarndal - One of the best experts on this subject based on the ideXlab platform.
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efficient estimators with simple variance in unequal Probability sampling
Journal of the American Statistical Association, 1996Co-Authors: Carlerik SarndalAbstract:Abstract For unequal Probability sampling designs, design-based variance estimation is cumbersome because it requires second-order inclusion probabilities. For most fixed sample size Probability Proportional-to-size (φPS) schemes, these probabilities are difficult to compute, and the variance estimation depends on them for a tedious double-sum calculation. We show how to replace the traditional φPS scenario with simpler design/estimator alternatives that preserve the high efficiency characteristic of φPS schemes. These use the generalized regression estimator, and the variance estimation entails only the calculation of a simple weighted squared residual sum.
Mark J. Ducey - One of the best experts on this subject based on the ideXlab platform.
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sampling trees with Probability nearly Proportional to biomass
Forest Ecology and Management, 2009Co-Authors: Mark J. DuceyAbstract:It is a truism in the sampling literature that sampling is most efficient when it is conducted with Probability Proportional to the variable of interest. Variable Probability sampling methods have long been applied to trees. The most familiar approach is horizontal point sampling (HPS) which samples trees with Probability Proportional to basal area. Here, I introduce a generalization of horizontal point sampling (GHPS). GHPS is a simple practical technique for sampling trees with Probability Proportional to an approximate equation for biomass. The technique requires construction of a gauge, but the gauge need not be complicated. In principle, GHPS should be more efficient than ordinary HPS. This hypothesis was tested with a field trial. Somewhat surprisingly, GHPS was only marginally superior to HPS in terms of sampling variance and efficiency. However, GHPS took no longer to perform, and was not associated with detectable non-sampling error. Results suggest that a well-designed subsampling approach, used in conjunction with GHPS, might lead to appreciable improvements.
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Estimating the carbon in coarse woody debris with perpendicular distance sampling. Chapter 6
2008Co-Authors: Harry T. Valentine, Jeffery H. Gove, Mark J. Ducey, Timothy G. Gregoire, Michael S. WilliamsAbstract:Perpendicular distance sampling (PDS) is a design for sampling the population of pieces of coarse woody debris (logs) in a forested tract. In application, logs are selected at sample points with Probability Proportional to volume. Consequently, aggregate log volume per unit land area can be estimated from tallies of logs at sample points. In this chapter we provide protocols and formulae for estimating the carbon in coarse woody debris with PDS. We also provide formulae for estimating components of change in the log population between two points in time.
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Simultaneous unbiased estimates of multiple downed wood attributes in perpendicular distance sampling
Canadian Journal of Forest Research, 2008Co-Authors: Mark J. Ducey, Michael S. Williams, Harry T. ValentineAbstract:Perpendicular distance sampling (PDS) is a fast Probability-Proportional-to-size method for inventory of downed wood. However, previous development of PDS had limited the method to estimating only one variable (such as volume per hectare, or surface area per hectare) at a time. Here, we develop a general design-unbiased estimator for PDS. We then show how that estimator can be used to develop simple measurement protocols that allow simultaneous, unbiased estimation of multiple downed wood variables, including logs per hectare, length of logs per hectare, surface area or area coverage per hectare, and volume per hectare.
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What expansion factor should be used in binned Probability Proportional to size sampling
Canadian Journal of Forest Research, 1999Co-Authors: Mark J. DuceyAbstract:In Probability Proportional to size sampling, including prism cruising and other forms of point and line sampling, calculation of an exact expansion factor requires that size be recorded exactly. When sizes are binned or recorded by class, this information is lost. While several alternatives for calculating the expansion factor have been proposed, theoretical attention has been lacking. A decision-theoretic perspective helps distinguish between the alternatives and offers some support to the use of the arithmetic mean size in calculating the expansion factor, a choice which had previously come under some criticism. However, consistency arguments strongly favor estimators based on squared error loss or minimax principles. Some new alternatives are suggested when prior information about the diameter distribution in a stratum is available.
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Estimating the Carbon in Coarse Woody Debris with Perpendicular Distance Sampling
Field Measurements for Forest Carbon Monitoring, 1Co-Authors: Harry T. Valentine, Jeffery H. Gove, Mark J. Ducey, Timothy G. Gregoire, Michael S. WilliamsAbstract:Perpendicular distance sampling (PDS) is a design for sampling the population of pieces of coarse woody debris (logs) in a forested tract. In applica- tion, logs are selected at sample points with Probability Proportional to volume. Consequently, aggregate log volume per unit land area can be estimated from tallies of logs at sample points. In this chapter we provide protocols and formulae for esti- mating the carbon in coarse woody debris with PDS. We also provide formulae for estimating components of change in the log population between two points in time.
Jane M Horgan - One of the best experts on this subject based on the ideXlab platform.
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stabilized sieve sampling a point estimator analysis
Journal of Business & Economic Statistics, 1998Co-Authors: Jane M HorganAbstract:A modification of sieve sampling is proposed that returns a constant sample size. It is a scheme that selects line items with Probability Proportional to size (PPS) and nearly without replacement. An unbiased estimator of the total error amount is presented and its variance derived. Conditions under which the scheme is more efficient than sieve sampling and than PPS with replacement sampling are given.
Jeffrey J Tsay - One of the best experts on this subject based on the ideXlab platform.
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modified sieve sampling a method for single and multi stage Probability Proportional to size sampling
Ear and Hearing, 2010Co-Authors: Lucas Hoogduin, Thomas W Hall, Jeffrey J TsayAbstract:SUMMARY: Widely used Probability-Proportional-to-size (PPS) selection methods are not well adapted to circumstances requiring sample augmentation. Limitations include: (1) an inability to augment selections while maintaining PPS properties, (2) a failure to recognize changes in census stratum membership which result from sample augmentation, and (3) imprecise control over line item sample size. This paper presents a new method of PPS selection, a modified version of sieve sampling which overcomes these limitations. Simulations indicate the new method effectively maintains sampling stratum PPS properties in single- and multi-stage samples, appropriately recognizes changes in census stratum membership which result from sample augmentation, and provides precise control over line item sample sizes. In single-stage applications the method provides reliable control of sampling risk over varied tainting levels and error bunching patterns. Tightness and efficiency measures are comparable to randomized systematic ...
Rod Little - One of the best experts on this subject based on the ideXlab platform.
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inference for the population total from Probability Proportional to size samples based on predictions from a penalized spline nonparametric model
Journal of Official Statistics, 2003Co-Authors: Hui Zheng, Rod LittleAbstract:Inference about the finite population total from Probability-Proportional-to-size (PPS) samples is considered. In previous work (Zheng and Little, 2003), penalized spline (p-spline) nonparametric model-based estimators were shown to generally outperform the Horvitz-Thompson (HT) and generalized regression (GR) estimators in terms of the root mean squared error. In this article we develop modelbased, jackknife and balanced repeated replicate variance estimation methods for the p-spline based estimators. Asymptotic properties of the jackknife method are discussed. Simulations show that p-spline point estimators and their jackknife standard errors lead to inferences that are superior to HT or GR based inferences. This suggests that nonparametric model-based prediction approaches can be successfully applied in the finite population setting by avoiding strong parametric assumptions. Inference for the Population Total from Probability-Proportional-to-Size Samples Based on Predictions from a Penalized Spline Nonparametric Model Hui Zheng Post-doctoral Fellow, Department of Health Care Policy, Harvard Medical School 180 Longwood Avenue, Boston, MA 02115, USA. Email: huizheng@umich.edu Roderick J. A. Little Professor, Department of Biostatistics, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109, USA. Email: rlittle@umich.edu Hosted by The Berkeley Electronic Press 1 Summary. Inference about the finite population total from Probability-Proportional-tosize (PPS) samples is considered. In previous work (Zheng and Little, 2002), penalized spline (p-spline) nonparametric model-based estimators were shown to generally outperform the Horvitz-Thompson (HT) and generalized regression (GR) estimators in terms of the root mean squared error. In this article we develop model-based, jackknife and balanced repeated replicate variance estimation methods for the p-spline based estimators. Asymptotic properties of the jackknife method are discussed. Simulations show that p-spline point estimators and their jackknife standard errors lead to inferences that are superior to HT or GR based inferences. This suggests that nonparametric model-based prediction approaches can be successfully applied in the finite population setting by avoiding strong parametric assumptions.
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Inference for the Population Total from Probability-Proportional-to-Size Samples Based on Predictions from a Penalized Spline Nonparametric Model
Journal of Official Statistics, 2003Co-Authors: Hui Zheng, Rod LittleAbstract:Inference about the finite population total from Probability-Proportional-to-size (PPS) samples is considered. In previous work (Zheng and Little, 2003), penalized spline (p-spline) nonparametric model-based estimators were shown to generally outperform the Horvitz-Thompson (HT) and generalized regression (GR) estimators in terms of the root mean squared error. In this article we develop modelbased, jackknife and balanced repeated replicate variance estimation methods for the p-spline based estimators. Asymptotic properties of the jackknife method are discussed. Simulations show that p-spline point estimators and their jackknife standard errors lead to inferences that are superior to HT or GR based inferences. This suggests that nonparametric model-based prediction approaches can be successfully applied in the finite population setting by avoiding strong parametric assumptions. Inference for the Population Total from Probability-Proportional-to-Size Samples Based on Predictions from a Penalized Spline Nonparametric Model Hui Zheng Post-doctoral Fellow, Department of Health Care Policy, Harvard Medical School 180 Longwood Avenue, Boston, MA 02115, USA. Email: huizheng@umich.edu Roderick J. A. Little Professor, Department of Biostatistics, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109, USA. Email: rlittle@umich.edu Hosted by The Berkeley Electronic Press 1 Summary. Inference about the finite population total from Probability-Proportional-tosize (PPS) samples is considered. In previous work (Zheng and Little, 2002), penalized spline (p-spline) nonparametric model-based estimators were shown to generally outperform the Horvitz-Thompson (HT) and generalized regression (GR) estimators in terms of the root mean squared error. In this article we develop model-based, jackknife and balanced repeated replicate variance estimation methods for the p-spline based estimators. Asymptotic properties of the jackknife method are discussed. Simulations show that p-spline point estimators and their jackknife standard errors lead to inferences that are superior to HT or GR based inferences. This suggests that nonparametric model-based prediction approaches can be successfully applied in the finite population setting by avoiding strong parametric assumptions.
