Results 241 to 250 of about 1,709,075 (283)

Sampling Bias in BitTorrent Measurements

2010
Real-world measurements play an important role in understanding the characteristics and in improving the operation of BitTorrent, which is currently a popular Internet application. Much like measuring the Internet, the complexity and scale of the BitTorrent network make a single, complete measurement impractical.
Zhang B., Iosup A., Pouwelse J., Epema D., Sips H.   +4 more
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Pooled samples bias fungal community descriptions

Molecular Ecology Resources, 2009
AbstractWe tested the accuracy of molecular analyses for recovering the species richness and structure of pooled fungal communities of known composition. We constructed replicate pools of 2–20 species and analysed these pools by two separate pooling‐DNA extraction procedures and three different molecular analyses (Automated Ribosomal Intergenic Spacer ...
P G, Avis, S, Branco, Y, Tang, G M, Mueller   +3 more
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Selection Bias with Outcome-dependent Sampling

Epidemiology, 2022
In a seminal paper, Hernán et al. 2004 provided a systematic classification of selection biases, for scenarios where the selection is a collider between the exposure and the outcome. Hernán 2017 discussed another scenario, where the selection is statistically independent of the exposure, but associated with the outcome through common causes.
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Testing for Sample Selection Bias

The Review of Economic Studies, 1982
A simple regression test of the null hypothesis of no sample selection bias has been suggested by J. J. Heckman. Because of its computational simplicity, this test is widely used by applied researchers. This paper shows that Heckman's test is equivalent to the Lagrange multiplier test of the null hypothesis.
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Telephone Sampling Bias in Surveying Disability

Public Opinion Quarterly, 1982
Results of the California Disability Survey indicate that telephone interviewing is well suited for undertaking disability studies that provide (1) estimates of subgroups of the disabled population, including those that are statistically rare: (2) information on current and anticipated areas of policy concern; and (3) information for geographic areas ...
H E, Freeman, K J, Kiecolt, W L, Nicholls, J M, Shanks   +3 more
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YTS, EMPLOYMENT, AND SAMPLE SELECTION BIAS

Oxford Economic Papers, 1994
In this paper, data from the first Youth Cohort Study (YCS) are used to analyze the effectiveness of the Youth Training Scheme (YTS) in England and Wales in terms of its impact, at an individual level, on the employment prospects of young people.
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Sample Selection Bias as a Specification Error [PDF]

Econometrica, 1979
Sample selection bias as a specification error This paper discusses the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or «omitted variables» bias. A simple consistent two stage estimator is considered that enables analysts to utilize simple regression methods to ...
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Small Sample Bias and Adjustment Costs

The Review of Economics and Statistics, 1994
The response of most stock variables (e.g., capital, housing, consumer durables, and prices) to exogenous impulses involves a dynamic-or 'short-run' - reaction, and a target - or 'long-run' - reaction. The difference between these two is typically attributed to some form of adjustment cost.
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Models for Sample Selection Bias

Annual Review of Sociology, 1992
When observations in social research are selected so that they are not independent of the outcome variables in a study, sample selection leads to biased inferences about social processes. Nonrandom selection is both a source of bias in empirical research and a fundamental aspect of many social processes.
Christopher Winship, Robert D. Mare
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Post-Stratification against Bias in Sampling

International Statistical Review / Revue Internationale de Statistique, 1986
In this paper poststratification estimator of the population mean \(\mu\) is studied in the model of conventional statistical field with repeated measurements of object and (discrete) auxiliary variable. There is one disputable assumption in the method: for possible, but not sampled values of the auxiliary variable (that is \(x\in {\mathcal X ...
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