Accounting for Nonrandomly Sampled Data in Nonlinear Regression
We analyze data that are "cherry picked" (i.e., nonrandomly sampled) from a population and are then used for regression modeling and prediction. Nonrandom data are encountered in numerous situations, and the application of standard statistical methods developed for random samples can easil...
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| Veröffentlicht in: | Quality engineering 2015-04, Vol.27 (2), p.168-176 |
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| container_title | Quality engineering |
| container_volume | 27 |
| creator | Picard, R. R. Hamada, M. S. Hemphill, G. M. Hackenberg, R. E. |
| description | We analyze data that are "cherry picked" (i.e., nonrandomly sampled) from a population and are then used for regression modeling and prediction. Nonrandom data are encountered in numerous situations, and the application of standard statistical methods developed for random samples can easily lead to incorrect conclusions. A case study is presented to illustrate the related issues, as well as the repercussions of erroneously ignoring the nonrandom sampling. |
| doi_str_mv | 10.1080/08982112.2014.933979 |
| format | Article |
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| language | eng |
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| source | Business Source Complete |
| subjects | Bayesian inference mixture distribution order statistics Quality control Random number sampling ranked set sampling ranking error Regression analysis Statistical methods Studies |
| title | Accounting for Nonrandomly Sampled Data in Nonlinear Regression |
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