Robust population designs for longitudinal linear regression model with a random intercept
Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhou, Xiao-Dong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Computational statistics - Berlin : Springer, 1999, 33(2017), 2 vom: 03. Okt., Seite 903-931 |
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| Übergeordnetes Werk: |
volume:33 ; year:2017 ; number:2 ; day:03 ; month:10 ; pages:903-931 |
| Links: |
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| DOI / URN: |
10.1007/s00180-017-0767-6 |
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| 520 | |a Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of admissible sampling sequence, we determine the optimal number and allocation of sampling times in order to estimate the fixed effects model as accurately as possible. To make comparisons between different designs in a meaningful manner, we take experimental costs into account when defining the D-optimal design criterion function. We take a Bayesian method to overcome the uncertainty of the parameters in the design criterion to derive Bayesian optimal population designs. For complicated cases, we propose a hybrid algorithm to find optimal designs. Meanwhile, we apply the Equivalence Theorem to check the global optimality of these designs. | ||
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| 700 | 1 | |a Wang, Yun-Juan |4 aut | |
| 700 | 1 | |a Yue, Rong-Xian |4 aut | |
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10.1007/s00180-017-0767-6 doi (DE-627)SPR00151928X (SPR)s00180-017-0767-6-e DE-627 ger DE-627 rakwb eng Zhou, Xiao-Dong verfasserin (orcid)0000-0002-2498-1582 aut Robust population designs for longitudinal linear regression model with a random intercept 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of admissible sampling sequence, we determine the optimal number and allocation of sampling times in order to estimate the fixed effects model as accurately as possible. To make comparisons between different designs in a meaningful manner, we take experimental costs into account when defining the D-optimal design criterion function. We take a Bayesian method to overcome the uncertainty of the parameters in the design criterion to derive Bayesian optimal population designs. For complicated cases, we propose a hybrid algorithm to find optimal designs. Meanwhile, we apply the Equivalence Theorem to check the global optimality of these designs. Optimal design (dpeaa)DE-He213 Mixed effects models (dpeaa)DE-He213 Equivalence theorem (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Wang, Yun-Juan aut Yue, Rong-Xian aut Enthalten in Computational statistics Berlin : Springer, 1999 33(2017), 2 vom: 03. Okt., Seite 903-931 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:33 year:2017 number:2 day:03 month:10 pages:903-931 https://dx.doi.org/10.1007/s00180-017-0767-6 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_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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 33 2017 2 03 10 903-931 |
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10.1007/s00180-017-0767-6 doi (DE-627)SPR00151928X (SPR)s00180-017-0767-6-e DE-627 ger DE-627 rakwb eng Zhou, Xiao-Dong verfasserin (orcid)0000-0002-2498-1582 aut Robust population designs for longitudinal linear regression model with a random intercept 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of admissible sampling sequence, we determine the optimal number and allocation of sampling times in order to estimate the fixed effects model as accurately as possible. To make comparisons between different designs in a meaningful manner, we take experimental costs into account when defining the D-optimal design criterion function. We take a Bayesian method to overcome the uncertainty of the parameters in the design criterion to derive Bayesian optimal population designs. For complicated cases, we propose a hybrid algorithm to find optimal designs. Meanwhile, we apply the Equivalence Theorem to check the global optimality of these designs. Optimal design (dpeaa)DE-He213 Mixed effects models (dpeaa)DE-He213 Equivalence theorem (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Wang, Yun-Juan aut Yue, Rong-Xian aut Enthalten in Computational statistics Berlin : Springer, 1999 33(2017), 2 vom: 03. Okt., Seite 903-931 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:33 year:2017 number:2 day:03 month:10 pages:903-931 https://dx.doi.org/10.1007/s00180-017-0767-6 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_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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 33 2017 2 03 10 903-931 |
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10.1007/s00180-017-0767-6 doi (DE-627)SPR00151928X (SPR)s00180-017-0767-6-e DE-627 ger DE-627 rakwb eng Zhou, Xiao-Dong verfasserin (orcid)0000-0002-2498-1582 aut Robust population designs for longitudinal linear regression model with a random intercept 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of admissible sampling sequence, we determine the optimal number and allocation of sampling times in order to estimate the fixed effects model as accurately as possible. To make comparisons between different designs in a meaningful manner, we take experimental costs into account when defining the D-optimal design criterion function. We take a Bayesian method to overcome the uncertainty of the parameters in the design criterion to derive Bayesian optimal population designs. For complicated cases, we propose a hybrid algorithm to find optimal designs. Meanwhile, we apply the Equivalence Theorem to check the global optimality of these designs. Optimal design (dpeaa)DE-He213 Mixed effects models (dpeaa)DE-He213 Equivalence theorem (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Wang, Yun-Juan aut Yue, Rong-Xian aut Enthalten in Computational statistics Berlin : Springer, 1999 33(2017), 2 vom: 03. Okt., Seite 903-931 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:33 year:2017 number:2 day:03 month:10 pages:903-931 https://dx.doi.org/10.1007/s00180-017-0767-6 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_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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 33 2017 2 03 10 903-931 |
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Zhou, Xiao-Dong |
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Zhou, Xiao-Dong misc Optimal design misc Mixed effects models misc Equivalence theorem misc Particle swarm optimization Robust population designs for longitudinal linear regression model with a random intercept |
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Robust population designs for longitudinal linear regression model with a random intercept Optimal design (dpeaa)DE-He213 Mixed effects models (dpeaa)DE-He213 Equivalence theorem (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 |
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Robust population designs for longitudinal linear regression model with a random intercept |
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robust population designs for longitudinal linear regression model with a random intercept |
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Robust population designs for longitudinal linear regression model with a random intercept |
| abstract |
Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of admissible sampling sequence, we determine the optimal number and allocation of sampling times in order to estimate the fixed effects model as accurately as possible. To make comparisons between different designs in a meaningful manner, we take experimental costs into account when defining the D-optimal design criterion function. We take a Bayesian method to overcome the uncertainty of the parameters in the design criterion to derive Bayesian optimal population designs. For complicated cases, we propose a hybrid algorithm to find optimal designs. Meanwhile, we apply the Equivalence Theorem to check the global optimality of these designs. © Springer-Verlag GmbH Germany 2017 |
| abstractGer |
Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of admissible sampling sequence, we determine the optimal number and allocation of sampling times in order to estimate the fixed effects model as accurately as possible. To make comparisons between different designs in a meaningful manner, we take experimental costs into account when defining the D-optimal design criterion function. We take a Bayesian method to overcome the uncertainty of the parameters in the design criterion to derive Bayesian optimal population designs. For complicated cases, we propose a hybrid algorithm to find optimal designs. Meanwhile, we apply the Equivalence Theorem to check the global optimality of these designs. © Springer-Verlag GmbH Germany 2017 |
| abstract_unstemmed |
Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of admissible sampling sequence, we determine the optimal number and allocation of sampling times in order to estimate the fixed effects model as accurately as possible. To make comparisons between different designs in a meaningful manner, we take experimental costs into account when defining the D-optimal design criterion function. We take a Bayesian method to overcome the uncertainty of the parameters in the design criterion to derive Bayesian optimal population designs. For complicated cases, we propose a hybrid algorithm to find optimal designs. Meanwhile, we apply the Equivalence Theorem to check the global optimality of these designs. © Springer-Verlag GmbH Germany 2017 |
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Robust population designs for longitudinal linear regression model with a random intercept |
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https://dx.doi.org/10.1007/s00180-017-0767-6 |
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Wang, Yun-Juan Yue, Rong-Xian |
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10.1007/s00180-017-0767-6 |
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