Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed
Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities...
Ausführliche Beschreibung
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| Autor*in: |
Crema, E. R. [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: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of archaeological method and theory - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 29(2022), 4 vom: 21. Apr., Seite 1387-1418 |
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| Übergeordnetes Werk: |
volume:29 ; year:2022 ; number:4 ; day:21 ; month:04 ; pages:1387-1418 |
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| DOI / URN: |
10.1007/s10816-022-09559-5 |
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SPR048780731 |
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| 520 | |a Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research. | ||
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10.1007/s10816-022-09559-5 doi (DE-627)SPR048780731 (SPR)s10816-022-09559-5-e DE-627 ger DE-627 rakwb eng Crema, E. R. verfasserin (orcid)0000-0001-6727-5138 aut Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research. Prehistoric demography (dpeaa)DE-He213 Dates as data (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Radiocarbon dates (dpeaa)DE-He213 Enthalten in Journal of archaeological method and theory Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 29(2022), 4 vom: 21. Apr., Seite 1387-1418 (DE-627)320572625 (DE-600)2016666-7 1573-7764 nnns volume:29 year:2022 number:4 day:21 month:04 pages:1387-1418 https://dx.doi.org/10.1007/s10816-022-09559-5 kostenfrei 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_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_374 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_2018 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_2937 GBV_ILN_2949 GBV_ILN_2950 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 4 21 04 1387-1418 |
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10.1007/s10816-022-09559-5 doi (DE-627)SPR048780731 (SPR)s10816-022-09559-5-e DE-627 ger DE-627 rakwb eng Crema, E. R. verfasserin (orcid)0000-0001-6727-5138 aut Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research. Prehistoric demography (dpeaa)DE-He213 Dates as data (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Radiocarbon dates (dpeaa)DE-He213 Enthalten in Journal of archaeological method and theory Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 29(2022), 4 vom: 21. Apr., Seite 1387-1418 (DE-627)320572625 (DE-600)2016666-7 1573-7764 nnns volume:29 year:2022 number:4 day:21 month:04 pages:1387-1418 https://dx.doi.org/10.1007/s10816-022-09559-5 kostenfrei 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_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_374 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_2018 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_2937 GBV_ILN_2949 GBV_ILN_2950 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 4 21 04 1387-1418 |
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10.1007/s10816-022-09559-5 doi (DE-627)SPR048780731 (SPR)s10816-022-09559-5-e DE-627 ger DE-627 rakwb eng Crema, E. R. verfasserin (orcid)0000-0001-6727-5138 aut Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research. Prehistoric demography (dpeaa)DE-He213 Dates as data (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Radiocarbon dates (dpeaa)DE-He213 Enthalten in Journal of archaeological method and theory Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 29(2022), 4 vom: 21. Apr., Seite 1387-1418 (DE-627)320572625 (DE-600)2016666-7 1573-7764 nnns volume:29 year:2022 number:4 day:21 month:04 pages:1387-1418 https://dx.doi.org/10.1007/s10816-022-09559-5 kostenfrei 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_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_374 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_2018 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_2937 GBV_ILN_2949 GBV_ILN_2950 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 4 21 04 1387-1418 |
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10.1007/s10816-022-09559-5 doi (DE-627)SPR048780731 (SPR)s10816-022-09559-5-e DE-627 ger DE-627 rakwb eng Crema, E. R. verfasserin (orcid)0000-0001-6727-5138 aut Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research. Prehistoric demography (dpeaa)DE-He213 Dates as data (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Radiocarbon dates (dpeaa)DE-He213 Enthalten in Journal of archaeological method and theory Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 29(2022), 4 vom: 21. Apr., Seite 1387-1418 (DE-627)320572625 (DE-600)2016666-7 1573-7764 nnns volume:29 year:2022 number:4 day:21 month:04 pages:1387-1418 https://dx.doi.org/10.1007/s10816-022-09559-5 kostenfrei 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_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_374 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_2018 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_2937 GBV_ILN_2949 GBV_ILN_2950 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 4 21 04 1387-1418 |
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Crema, E. R. |
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Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed Prehistoric demography (dpeaa)DE-He213 Dates as data (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Radiocarbon dates (dpeaa)DE-He213 |
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statistical inference of prehistoric demography from frequency distributions of radiocarbon dates: a review and a guide for the perplexed |
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Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed |
| abstract |
Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research. © The Author(s) 2022 |
| abstractGer |
Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research. © The Author(s) 2022 |
| abstract_unstemmed |
Abstract The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research. © The Author(s) 2022 |
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| title_short |
Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed |
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https://dx.doi.org/10.1007/s10816-022-09559-5 |
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10.1007/s10816-022-09559-5 |
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2024-07-03T21:26:24.477Z |
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