Maximum likelihood estimation of missing data probability for nonmonotone missing at random data
Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, Yang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Statistical methods & applications - [Berlin] : Springer, 1992, 32(2022), 1 vom: 17. Juni, Seite 197-209 |
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| Übergeordnetes Werk: |
volume:32 ; year:2022 ; number:1 ; day:17 ; month:06 ; pages:197-209 |
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| DOI / URN: |
10.1007/s10260-022-00650-5 |
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| Katalog-ID: |
SPR049597833 |
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| 520 | |a Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. | ||
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| 650 | 4 | |a Model selection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nonmonotone missing data patterns |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Semiparametric likelihood |7 (dpeaa)DE-He213 | |
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10.1007/s10260-022-00650-5 doi (DE-627)SPR049597833 (SPR)s10260-022-00650-5-e DE-627 ger DE-627 rakwb eng Zhao, Yang verfasserin (orcid)0000-0002-9768-7683 aut Maximum likelihood estimation of missing data probability for nonmonotone missing at random data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. Curse of dimensionality (dpeaa)DE-He213 Missing at random (dpeaa)DE-He213 Missing data mechanism (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Nonmonotone missing data patterns (dpeaa)DE-He213 Semiparametric likelihood (dpeaa)DE-He213 Enthalten in Statistical methods & applications [Berlin] : Springer, 1992 32(2022), 1 vom: 17. Juni, Seite 197-209 (DE-627)360060099 (DE-600)2098826-6 1613-981X nnns volume:32 year:2022 number:1 day:17 month:06 pages:197-209 https://dx.doi.org/10.1007/s10260-022-00650-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2022 1 17 06 197-209 |
| spelling |
10.1007/s10260-022-00650-5 doi (DE-627)SPR049597833 (SPR)s10260-022-00650-5-e DE-627 ger DE-627 rakwb eng Zhao, Yang verfasserin (orcid)0000-0002-9768-7683 aut Maximum likelihood estimation of missing data probability for nonmonotone missing at random data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. Curse of dimensionality (dpeaa)DE-He213 Missing at random (dpeaa)DE-He213 Missing data mechanism (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Nonmonotone missing data patterns (dpeaa)DE-He213 Semiparametric likelihood (dpeaa)DE-He213 Enthalten in Statistical methods & applications [Berlin] : Springer, 1992 32(2022), 1 vom: 17. Juni, Seite 197-209 (DE-627)360060099 (DE-600)2098826-6 1613-981X nnns volume:32 year:2022 number:1 day:17 month:06 pages:197-209 https://dx.doi.org/10.1007/s10260-022-00650-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2022 1 17 06 197-209 |
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10.1007/s10260-022-00650-5 doi (DE-627)SPR049597833 (SPR)s10260-022-00650-5-e DE-627 ger DE-627 rakwb eng Zhao, Yang verfasserin (orcid)0000-0002-9768-7683 aut Maximum likelihood estimation of missing data probability for nonmonotone missing at random data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. Curse of dimensionality (dpeaa)DE-He213 Missing at random (dpeaa)DE-He213 Missing data mechanism (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Nonmonotone missing data patterns (dpeaa)DE-He213 Semiparametric likelihood (dpeaa)DE-He213 Enthalten in Statistical methods & applications [Berlin] : Springer, 1992 32(2022), 1 vom: 17. Juni, Seite 197-209 (DE-627)360060099 (DE-600)2098826-6 1613-981X nnns volume:32 year:2022 number:1 day:17 month:06 pages:197-209 https://dx.doi.org/10.1007/s10260-022-00650-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2022 1 17 06 197-209 |
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10.1007/s10260-022-00650-5 doi (DE-627)SPR049597833 (SPR)s10260-022-00650-5-e DE-627 ger DE-627 rakwb eng Zhao, Yang verfasserin (orcid)0000-0002-9768-7683 aut Maximum likelihood estimation of missing data probability for nonmonotone missing at random data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. Curse of dimensionality (dpeaa)DE-He213 Missing at random (dpeaa)DE-He213 Missing data mechanism (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Nonmonotone missing data patterns (dpeaa)DE-He213 Semiparametric likelihood (dpeaa)DE-He213 Enthalten in Statistical methods & applications [Berlin] : Springer, 1992 32(2022), 1 vom: 17. Juni, Seite 197-209 (DE-627)360060099 (DE-600)2098826-6 1613-981X nnns volume:32 year:2022 number:1 day:17 month:06 pages:197-209 https://dx.doi.org/10.1007/s10260-022-00650-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2022 1 17 06 197-209 |
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10.1007/s10260-022-00650-5 doi (DE-627)SPR049597833 (SPR)s10260-022-00650-5-e DE-627 ger DE-627 rakwb eng Zhao, Yang verfasserin (orcid)0000-0002-9768-7683 aut Maximum likelihood estimation of missing data probability for nonmonotone missing at random data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. Curse of dimensionality (dpeaa)DE-He213 Missing at random (dpeaa)DE-He213 Missing data mechanism (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Nonmonotone missing data patterns (dpeaa)DE-He213 Semiparametric likelihood (dpeaa)DE-He213 Enthalten in Statistical methods & applications [Berlin] : Springer, 1992 32(2022), 1 vom: 17. Juni, Seite 197-209 (DE-627)360060099 (DE-600)2098826-6 1613-981X nnns volume:32 year:2022 number:1 day:17 month:06 pages:197-209 https://dx.doi.org/10.1007/s10260-022-00650-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2022 1 17 06 197-209 |
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Zhao, Yang |
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Maximum likelihood estimation of missing data probability for nonmonotone missing at random data Curse of dimensionality (dpeaa)DE-He213 Missing at random (dpeaa)DE-He213 Missing data mechanism (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Nonmonotone missing data patterns (dpeaa)DE-He213 Semiparametric likelihood (dpeaa)DE-He213 |
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maximum likelihood estimation of missing data probability for nonmonotone missing at random data |
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Maximum likelihood estimation of missing data probability for nonmonotone missing at random data |
| abstract |
Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. © Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. © Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate distribution. For nonmonotone missing data patterns, modeling and practical estimation of the missing data probability are very challenging. Recently a semiparametric likelihood model was developed to estimate parametric regression models for the missing data mechanism based on all the observed data, which can deal with arbitrary nonmonotone missing data patterns. However, due to the curse of dimensionality in the likelihood-based models, this method becomes impractical if the number of variables increases. This research generalizes the semiparametric likelihood model such that it can deal with any number of variables with arbitrary nonmonotone missing data patterns. It further introduces a semiparametric estimator of the missing data probability for the partially observed data, which can be used to assess the model fit. An EM algorithm with closed form expressions at each step are used to compute the estimates. Simulation studies in various settings indicate that the performance of the new method is acceptable for practical implementation. The missing data mechanism of a case-control study of hip fractures among male veterans is analyzed to illustrate the method. © Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Maximum likelihood estimation of missing data probability for nonmonotone missing at random data |
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https://dx.doi.org/10.1007/s10260-022-00650-5 |
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10.1007/s10260-022-00650-5 |
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