Autoregressive-output-analysis methods revisited
Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is st...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yuan, Mingjian [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1994 |
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| Schlagwörter: |
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| Anmerkung: |
© J.C. Baltzer AG, Science Publishers 1994 |
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| Übergeordnetes Werk: |
Enthalten in: Annals of operations research - Baltzer Science Publishers, Baarn/Kluwer Academic Publishers, 1984, 53(1994), 1 vom: Dez., Seite 391-418 |
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| Übergeordnetes Werk: |
volume:53 ; year:1994 ; number:1 ; month:12 ; pages:391-418 |
| Links: |
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| DOI / URN: |
10.1007/BF02136836 |
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| 520 | |a Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties. | ||
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10.1007/BF02136836 doi (DE-627)OLC2111120111 (DE-He213)BF02136836-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Yuan, Mingjian verfasserin aut Autoregressive-output-analysis methods revisited 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © J.C. Baltzer AG, Science Publishers 1994 Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties. Autoregressive process confidence interval output analysis simulation statistics time series Nelson, Barry L. aut Enthalten in Annals of operations research Baltzer Science Publishers, Baarn/Kluwer Academic Publishers, 1984 53(1994), 1 vom: Dez., Seite 391-418 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:53 year:1994 number:1 month:12 pages:391-418 https://doi.org/10.1007/BF02136836 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_4029 AR 53 1994 1 12 391-418 |
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10.1007/BF02136836 doi (DE-627)OLC2111120111 (DE-He213)BF02136836-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Yuan, Mingjian verfasserin aut Autoregressive-output-analysis methods revisited 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © J.C. Baltzer AG, Science Publishers 1994 Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties. Autoregressive process confidence interval output analysis simulation statistics time series Nelson, Barry L. aut Enthalten in Annals of operations research Baltzer Science Publishers, Baarn/Kluwer Academic Publishers, 1984 53(1994), 1 vom: Dez., Seite 391-418 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:53 year:1994 number:1 month:12 pages:391-418 https://doi.org/10.1007/BF02136836 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_4029 AR 53 1994 1 12 391-418 |
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10.1007/BF02136836 doi (DE-627)OLC2111120111 (DE-He213)BF02136836-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Yuan, Mingjian verfasserin aut Autoregressive-output-analysis methods revisited 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © J.C. Baltzer AG, Science Publishers 1994 Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties. Autoregressive process confidence interval output analysis simulation statistics time series Nelson, Barry L. aut Enthalten in Annals of operations research Baltzer Science Publishers, Baarn/Kluwer Academic Publishers, 1984 53(1994), 1 vom: Dez., Seite 391-418 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:53 year:1994 number:1 month:12 pages:391-418 https://doi.org/10.1007/BF02136836 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_4029 AR 53 1994 1 12 391-418 |
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10.1007/BF02136836 doi (DE-627)OLC2111120111 (DE-He213)BF02136836-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Yuan, Mingjian verfasserin aut Autoregressive-output-analysis methods revisited 1994 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © J.C. Baltzer AG, Science Publishers 1994 Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties. Autoregressive process confidence interval output analysis simulation statistics time series Nelson, Barry L. aut Enthalten in Annals of operations research Baltzer Science Publishers, Baarn/Kluwer Academic Publishers, 1984 53(1994), 1 vom: Dez., Seite 391-418 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:53 year:1994 number:1 month:12 pages:391-418 https://doi.org/10.1007/BF02136836 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_4029 AR 53 1994 1 12 391-418 |
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Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties. © J.C. Baltzer AG, Science Publishers 1994 |
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Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties. © J.C. Baltzer AG, Science Publishers 1994 |
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Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties. © J.C. Baltzer AG, Science Publishers 1994 |
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Baltzer AG, Science Publishers 1994</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Autoregressive process</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">confidence interval</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">output analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">time series</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nelson, Barry L.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Annals of operations research</subfield><subfield code="d">Baltzer Science Publishers, Baarn/Kluwer Academic Publishers, 1984</subfield><subfield code="g">53(1994), 1 vom: Dez., Seite 391-418</subfield><subfield code="w">(DE-627)12964370X</subfield><subfield code="w">(DE-600)252629-3</subfield><subfield code="w">(DE-576)018141862</subfield><subfield code="x">0254-5330</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:53</subfield><subfield code="g">year:1994</subfield><subfield code="g">number:1</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:391-418</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/BF02136836</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4029</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">53</subfield><subfield code="j">1994</subfield><subfield code="e">1</subfield><subfield code="c">12</subfield><subfield code="h">391-418</subfield></datafield></record></collection>
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