The effect of preferential sampling on sampling variance

2012 ◽  
pp. 223-226
Author(s):  
D Clifford ◽  
P Kuhnert ◽  
M Dobbie ◽  
J Baldock ◽  
B Harch ◽  
...  
Keyword(s):  
Sampling Variance ◽  
Preferential Sampling

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

A Method for Decreasing Sampling Variance in Bacteriological Analyses of Meat Surfaces1,2

1980 ◽  
Vol 43 (1) ◽  
pp. 21-22 ◽  
Author(s):  
M. E. ANDERSON ◽  
J. L. SEBAUGH ◽  
R. T. MARSHALL ◽  
W. C. STRINGER
Keyword(s):  
Sample Variance ◽  
Sampling Variance ◽  
Fresh Meat ◽  
Plate Counts

Viable counts of bacteria are often high in some areas and low in adjacent areas of the same surface of fresh meat. The present study indicated that rubbing meat surfaces together before sampling reduces variation among bacterial plate counts of pieces of beef plate meat. Counts before rubbing ranged from 2 to 6,187/cm2, whereas counts after rubbing ranged from 15 to 2,043/cm2. The reduced sample variance allowed for fewer samples to be taken in studies of cleaning and sanitizing of fresh beef.

scholarly journals Comparison of Variance Estimators for Systematic Environmental Sample Surveys: Considerations for Post-Stratified Estimation

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 772
Author(s):  
Bryce Frank ◽  
Vicente J. Monleon
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
National Forest Inventory ◽  
Point Estimation ◽  
Systematic Sampling ◽  
Sampling Variance ◽  
Variance Estimators ◽  
The Past ◽  
Point Estimators ◽  
Species Specific

The estimation of the sampling variance of point estimators under two-dimensional systematic sampling designs remains a challenge, and several alternative variance estimators have been proposed in the past few decades. In this work, we compared six alternative variance estimators under Horvitz-Thompson (HT) and post-stratification (PS) point estimation regimes. We subsampled a multitude of species-specific forest attributes from a large, spatially balanced national forest inventory to compare the variance estimators. A variance estimator that assumes a simple random sampling design exhibited positive relative bias under both HT and PS point estimation regimes ranging between 1.23 to 1.88 and 1.11 to 1.78 for HT and PS, respectively. Alternative estimators reduced this positive bias with relative biases ranging between 1.01 to 1.66 and 0.90 to 1.64 for HT and PS, respectively. The alternative estimators generally obtained improved efficiencies under both HT and PS, with relative efficiency values ranging between 0.68 to 1.28 and 0.68 to 1.39, respectively. We identified two estimators as promising alternatives that provide clear improvements over the simple random sampling estimator for a wide variety of attributes and under HT and PS estimation regimes.

scholarly journals Biased Estimation of the Recombination Fraction Using Half-Sib Families and Informative Offspring

Genetics ◽  
2001 ◽  
Vol 157 (3) ◽  
pp. 1357-1367 ◽  
Author(s):  
L Gomez-Raya
Keyword(s):  
Recombination Fraction ◽  
Allele Frequencies ◽  
Likelihood Method ◽  
Simultaneous Estimation ◽  
Sampling Variance ◽  
Production Traits ◽  
Lod Score ◽  
Absolute Value ◽  
Sib Families ◽  
Map Distance

Abstract A maximum-likelihood method to estimate the recombination fraction and its sampling variance using informative and noninformative half-sib offspring is derived. Estimates of the recombination fraction are biased up to 20 cM when noninformative offspring are discarded. In certain scenarios, the sampling variance can be increased or reduced up to fivefold due to the bias in estimating the recombination fraction and the LOD score can be reduced up to 5 units when discarding noninformative offspring. Comparison of the estimates of recombination fraction, map distance, and LOD score when constructing a genetic map with 251 two-point linkage analyses and six families of Norwegian cattle was carried out to evaluate the implications of discarding noninformative offspring in practical situations. The average discrepancies in absolute value (average difference when using and neglecting noninformative offspring) were 0.0146, 1.64 cM, and 2.61 for the recombination fraction, map distance, and the LOD score, respectively. A method for simultaneous estimation of allele frequencies in the dam population and a transmission disequilibrium parameter is proposed. This method might account for the bias in estimating allele frequencies in the dam population when the half-sib offspring is selected for production traits.

scholarly journals Inertial-particle accelerations in turbulence: a Lagrangian closure

2016 ◽  
Vol 798 ◽  
pp. 187-200 ◽  
Author(s):  
S. Vajedi ◽  
K. Gustavsson ◽  
B. Mehlig ◽  
L. Biferale
Keyword(s):  
Numerical Simulations ◽  
Numerical Data ◽  
Direct Numerical Simulations ◽  
Inertial Particles ◽  
Inertial Particle ◽  
Order Unity ◽  
Heavy Particles ◽  
Preferential Sampling ◽  
Closure Scheme ◽  
Light Particles

The distribution of particle accelerations in turbulence is intermittent, with non-Gaussian tails that are quite different for light and heavy particles. In this article we analyse a closure scheme for the acceleration fluctuations of light and heavy inertial particles in turbulence, formulated in terms of Lagrangian correlation functions of fluid tracers. We compute the variance and the flatness of inertial-particle accelerations and we discuss their dependency on the Stokes number. The closure incorporates effects induced by the Lagrangian correlations along the trajectories of fluid tracers, and its predictions agree well with results of direct numerical simulations of inertial particles in turbulence, provided that the effects induced by inertial preferential sampling of heavy/light particles outside/inside vortices are negligible. In particular, the scheme predicts the correct functional behaviour of the acceleration variance, as a function of $St$, as well as the presence of a minimum/maximum for the flatness of the acceleration of heavy/light particles, in good qualitative agreement with numerical data. We also show that the closure works well when applied to the Lagrangian evolution of particles using a stochastic surrogate for the underlying Eulerian velocity field. Our results support the conclusion that there exist important contributions to the statistics of the acceleration of inertial particles independent of the preferential sampling. For heavy particles we observe deviations between the predictions of the closure scheme and direct numerical simulations, at Stokes numbers of order unity. For light particles the deviation occurs for larger Stokes numbers.

Bias in vegetation databases? A comparison of stratified-random and preferential sampling

2011 ◽  
Vol 22 (2) ◽  
pp. 281-291 ◽  
Author(s):  
Dana Michalcová ◽  
Samuel Lvončík ◽  
Milan Chytrý ◽  
Ondřej Hájek
Keyword(s):  
Vegetation Databases ◽  
Preferential Sampling

scholarly journals Preferential Sampling of Elastic Chains in Turbulent Flows

2018 ◽  
Vol 121 (24) ◽  
Author(s):  
Jason R. Picardo ◽  
Dario Vincenzi ◽  
Nairita Pal ◽  
Samriddhi Sankar Ray
Keyword(s):  
Turbulent Flows ◽  
Preferential Sampling

Robustness to Parametric Assumptions in Missing Data Models

2011 ◽  
Vol 101 (3) ◽  
pp. 538-543 ◽  
Author(s):  
Bryan S Graham ◽  
Keisuke Hirano
Keyword(s):  
Empirical Bayes ◽  
Missing At Random ◽  
Data Driven ◽  
Robust Estimator ◽  
Sampling Variance ◽  
Bayes Estimators ◽  
Cell Means ◽  
Double Robust ◽  
Empirical Bayes Estimators ◽  
Empirical Bayes Estimator

We consider estimation of population averages when data are missing at random. If some cells contain few observations, there can be substantial gains from imposing parametric restrictions on the cell means, but there is also a danger of misspecification. We develop a simple empirical Bayes estimator, which combines parametric and unadjusted estimates of cell means in a data-driven way. We also consider ways to use knowledge of the form of the propensity score to increase robustness. We develop an empirical Bayes extension of a double robust estimator. In a small simulation study, the empirical Bayes estimators perform well. They are similar to fully nonparametric methods and robust to misspecification when cells are moderate to large in size, and when cells are small they maintain the benefits of parametric methods and can have lower sampling variance.

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