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Simultaneously Model‐Unbiased, Design‐Unbiased Estimation
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| Zeitschriftentitel: | Biometrics |
|---|---|
| Personen und Körperschaften: | , |
| In: | Biometrics, 56, 2000, 3, S. 873-878 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Gerow, Ken McCulloch, Charles E. Gerow, Ken McCulloch, Charles E. |
|---|---|
| author |
Gerow, Ken McCulloch, Charles E. |
| spellingShingle |
Gerow, Ken McCulloch, Charles E. Biometrics Simultaneously Model‐Unbiased, Design‐Unbiased Estimation Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
| author_sort |
gerow, ken |
| spelling |
Gerow, Ken McCulloch, Charles E. 0006-341X 1541-0420 Oxford University Press (OUP) Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability http://dx.doi.org/10.1111/j.0006-341x.2000.00873.x <jats:p><jats:bold>Summary. </jats:bold> This paper proposes a class of inferential procedures (incorporating both design and estimation elements) that yield estimates of means that are simultaneously model unbiased and design unbiased. Classical regression procedures yield conditionally unbiased estimators for the mean (conditioning on the model and choice of observation points). In contrast, design‐based methods yield estimators that are unconditionally unbiased no matter what the form of the underlying model. Variance properties of the proposed class are examined, and applications to bioavailability, water quality from mine run‐off, and finite population regression estimation are considered. The proposed procedures perform well, especially in the typical case where a model is only approximately correct.</jats:p> Simultaneously Model‐Unbiased, Design‐Unbiased Estimation Biometrics |
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| title |
Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
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Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_full |
Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_fullStr |
Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
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Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_short |
Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_sort |
simultaneously model‐unbiased, design‐unbiased estimation |
| topic |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
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http://dx.doi.org/10.1111/j.0006-341x.2000.00873.x |
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2000 |
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873-878 |
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<jats:p><jats:bold>Summary. </jats:bold> This paper proposes a class of inferential procedures (incorporating both design and estimation elements) that yield estimates of means that are simultaneously model unbiased and design unbiased. Classical regression procedures yield conditionally unbiased estimators for the mean (conditioning on the model and choice of observation points). In contrast, design‐based methods yield estimators that are unconditionally unbiased no matter what the form of the underlying model. Variance properties of the proposed class are examined, and applications to bioavailability, water quality from mine run‐off, and finite population regression estimation are considered. The proposed procedures perform well, especially in the typical case where a model is only approximately correct.</jats:p> |
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| author | Gerow, Ken, McCulloch, Charles E. |
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| container_title | Biometrics |
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| description | <jats:p><jats:bold>Summary. </jats:bold> This paper proposes a class of inferential procedures (incorporating both design and estimation elements) that yield estimates of means that are simultaneously model unbiased and design unbiased. Classical regression procedures yield conditionally unbiased estimators for the mean (conditioning on the model and choice of observation points). In contrast, design‐based methods yield estimators that are unconditionally unbiased no matter what the form of the underlying model. Variance properties of the proposed class are examined, and applications to bioavailability, water quality from mine run‐off, and finite population regression estimation are considered. The proposed procedures perform well, especially in the typical case where a model is only approximately correct.</jats:p> |
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| imprint | Oxford University Press (OUP), 2000 |
| imprint_str_mv | Oxford University Press (OUP), 2000 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
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| series | Biometrics |
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| spelling | Gerow, Ken McCulloch, Charles E. 0006-341X 1541-0420 Oxford University Press (OUP) Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability http://dx.doi.org/10.1111/j.0006-341x.2000.00873.x <jats:p><jats:bold>Summary. </jats:bold> This paper proposes a class of inferential procedures (incorporating both design and estimation elements) that yield estimates of means that are simultaneously model unbiased and design unbiased. Classical regression procedures yield conditionally unbiased estimators for the mean (conditioning on the model and choice of observation points). In contrast, design‐based methods yield estimators that are unconditionally unbiased no matter what the form of the underlying model. Variance properties of the proposed class are examined, and applications to bioavailability, water quality from mine run‐off, and finite population regression estimation are considered. The proposed procedures perform well, especially in the typical case where a model is only approximately correct.</jats:p> Simultaneously Model‐Unbiased, Design‐Unbiased Estimation Biometrics |
| spellingShingle | Gerow, Ken, McCulloch, Charles E., Biometrics, Simultaneously Model‐Unbiased, Design‐Unbiased Estimation, Applied Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, General Medicine, Statistics and Probability |
| title | Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_full | Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_fullStr | Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_full_unstemmed | Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_short | Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| title_sort | simultaneously model‐unbiased, design‐unbiased estimation |
| title_unstemmed | Simultaneously Model‐Unbiased, Design‐Unbiased Estimation |
| topic | Applied Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, General Medicine, Statistics and Probability |
| url | http://dx.doi.org/10.1111/j.0006-341x.2000.00873.x |