Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures

Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properti...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sun, Yan [verfasserIn] Chai, Gen Xiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2006

Schlagwörter:	Repeated measures Random effects Convergence system Strong convergence Strong convergence rate

Übergeordnetes Werk:	Enthalten in: Acta mathematica sinica - Berlin : Springer, 1985, 23(2006), 4 vom: 16. Juni, Seite 685-696
Übergeordnetes Werk:	volume:23 ; year:2006 ; number:4 ; day:16 ; month:06 ; pages:685-696

Links:	Volltext

DOI / URN:	10.1007/s10114-005-0793-0

Katalog-ID:	SPR008927146

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520			\|a Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality.
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912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
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912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
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912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
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912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
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912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
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912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
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912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
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912			\|a GBV_ILN_2059
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912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
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912			\|a GBV_ILN_2106
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912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
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author_variant	y s ys g x c gx gxc
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allfields	10.1007/s10114-005-0793-0 doi (DE-627)SPR008927146 (SPR)s10114-005-0793-0-e DE-627 ger DE-627 rakwb eng 510 ASE 510 ASE 31.00 bkl Sun, Yan verfasserin aut Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality. Repeated measures (dpeaa)DE-He213 Random effects (dpeaa)DE-He213 Convergence system (dpeaa)DE-He213 Strong convergence (dpeaa)DE-He213 Strong convergence rate (dpeaa)DE-He213 Chai, Gen Xiang verfasserin aut Enthalten in Acta mathematica sinica Berlin : Springer, 1985 23(2006), 4 vom: 16. Juni, Seite 685-696 (DE-627)312226470 (DE-600)2011055-8 1439-7617 nnns volume:23 year:2006 number:4 day:16 month:06 pages:685-696 https://dx.doi.org/10.1007/s10114-005-0793-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_206 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 23 2006 4 16 06 685-696
spelling	10.1007/s10114-005-0793-0 doi (DE-627)SPR008927146 (SPR)s10114-005-0793-0-e DE-627 ger DE-627 rakwb eng 510 ASE 510 ASE 31.00 bkl Sun, Yan verfasserin aut Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality. Repeated measures (dpeaa)DE-He213 Random effects (dpeaa)DE-He213 Convergence system (dpeaa)DE-He213 Strong convergence (dpeaa)DE-He213 Strong convergence rate (dpeaa)DE-He213 Chai, Gen Xiang verfasserin aut Enthalten in Acta mathematica sinica Berlin : Springer, 1985 23(2006), 4 vom: 16. Juni, Seite 685-696 (DE-627)312226470 (DE-600)2011055-8 1439-7617 nnns volume:23 year:2006 number:4 day:16 month:06 pages:685-696 https://dx.doi.org/10.1007/s10114-005-0793-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_206 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 23 2006 4 16 06 685-696
allfields_unstemmed	10.1007/s10114-005-0793-0 doi (DE-627)SPR008927146 (SPR)s10114-005-0793-0-e DE-627 ger DE-627 rakwb eng 510 ASE 510 ASE 31.00 bkl Sun, Yan verfasserin aut Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality. Repeated measures (dpeaa)DE-He213 Random effects (dpeaa)DE-He213 Convergence system (dpeaa)DE-He213 Strong convergence (dpeaa)DE-He213 Strong convergence rate (dpeaa)DE-He213 Chai, Gen Xiang verfasserin aut Enthalten in Acta mathematica sinica Berlin : Springer, 1985 23(2006), 4 vom: 16. Juni, Seite 685-696 (DE-627)312226470 (DE-600)2011055-8 1439-7617 nnns volume:23 year:2006 number:4 day:16 month:06 pages:685-696 https://dx.doi.org/10.1007/s10114-005-0793-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_206 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 23 2006 4 16 06 685-696
allfieldsGer	10.1007/s10114-005-0793-0 doi (DE-627)SPR008927146 (SPR)s10114-005-0793-0-e DE-627 ger DE-627 rakwb eng 510 ASE 510 ASE 31.00 bkl Sun, Yan verfasserin aut Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality. Repeated measures (dpeaa)DE-He213 Random effects (dpeaa)DE-He213 Convergence system (dpeaa)DE-He213 Strong convergence (dpeaa)DE-He213 Strong convergence rate (dpeaa)DE-He213 Chai, Gen Xiang verfasserin aut Enthalten in Acta mathematica sinica Berlin : Springer, 1985 23(2006), 4 vom: 16. Juni, Seite 685-696 (DE-627)312226470 (DE-600)2011055-8 1439-7617 nnns volume:23 year:2006 number:4 day:16 month:06 pages:685-696 https://dx.doi.org/10.1007/s10114-005-0793-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_206 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 23 2006 4 16 06 685-696
allfieldsSound	10.1007/s10114-005-0793-0 doi (DE-627)SPR008927146 (SPR)s10114-005-0793-0-e DE-627 ger DE-627 rakwb eng 510 ASE 510 ASE 31.00 bkl Sun, Yan verfasserin aut Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality. Repeated measures (dpeaa)DE-He213 Random effects (dpeaa)DE-He213 Convergence system (dpeaa)DE-He213 Strong convergence (dpeaa)DE-He213 Strong convergence rate (dpeaa)DE-He213 Chai, Gen Xiang verfasserin aut Enthalten in Acta mathematica sinica Berlin : Springer, 1985 23(2006), 4 vom: 16. Juni, Seite 685-696 (DE-627)312226470 (DE-600)2011055-8 1439-7617 nnns volume:23 year:2006 number:4 day:16 month:06 pages:685-696 https://dx.doi.org/10.1007/s10114-005-0793-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_206 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 23 2006 4 16 06 685-696
language	English
source	Enthalten in Acta mathematica sinica 23(2006), 4 vom: 16. Juni, Seite 685-696 volume:23 year:2006 number:4 day:16 month:06 pages:685-696
sourceStr	Enthalten in Acta mathematica sinica 23(2006), 4 vom: 16. Juni, Seite 685-696 volume:23 year:2006 number:4 day:16 month:06 pages:685-696
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Repeated measures Random effects Convergence system Strong convergence Strong convergence rate
dewey-raw	510
isfreeaccess_bool	false
container_title	Acta mathematica sinica
authorswithroles_txt_mv	Sun, Yan @@aut@@ Chai, Gen Xiang @@aut@@
publishDateDaySort_date	2006-06-16T00:00:00Z
hierarchy_top_id	312226470
dewey-sort	3510
id	SPR008927146
language_de	englisch
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author	Sun, Yan
spellingShingle	Sun, Yan ddc 510 bkl 31.00 misc Repeated measures misc Random effects misc Convergence system misc Strong convergence misc Strong convergence rate Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures
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topic_title	510 ASE 31.00 bkl Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures Repeated measures (dpeaa)DE-He213 Random effects (dpeaa)DE-He213 Convergence system (dpeaa)DE-He213 Strong convergence (dpeaa)DE-He213 Strong convergence rate (dpeaa)DE-He213
topic	ddc 510 bkl 31.00 misc Repeated measures misc Random effects misc Convergence system misc Strong convergence misc Strong convergence rate
topic_unstemmed	ddc 510 bkl 31.00 misc Repeated measures misc Random effects misc Convergence system misc Strong convergence misc Strong convergence rate
topic_browse	ddc 510 bkl 31.00 misc Repeated measures misc Random effects misc Convergence system misc Strong convergence misc Strong convergence rate
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title	Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures
ctrlnum	(DE-627)SPR008927146 (SPR)s10114-005-0793-0-e
title_full	Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures
author_sort	Sun, Yan
journal	Acta mathematica sinica
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author_browse	Sun, Yan Chai, Gen Xiang
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title_sort	parameter estimates in random intercept mixed effects model for repeated measures
title_auth	Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures
abstract	Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality.
abstractGer	Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality.
abstract_unstemmed	Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality.
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title_short	Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures
url	https://dx.doi.org/10.1007/s10114-005-0793-0
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author2	Chai, Gen Xiang
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doi_str	10.1007/s10114-005-0793-0
up_date	2024-07-03T23:54:40.271Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR008927146</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110204856.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2006 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10114-005-0793-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR008927146</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10114-005-0793-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2006</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this article the following random intercept mixed effects model will be considered: %$ where {νi} are i.i.d. random effects with mean α and finite variance %$; {∈ij} are i.i.d. random errors with finite variance %$. Here we will estimate α, %$, %$, β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Repeated measures</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random effects</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convergence system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Strong convergence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Strong convergence rate</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chai, Gen Xiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Acta mathematica sinica</subfield><subfield code="d">Berlin : Springer, 1985</subfield><subfield code="g">23(2006), 4 vom: 16. 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Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures

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