Operational time and in-sample density forecasting

In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assump...
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Autor*in:	Young K Lee [verfasserIn] Enno Mammen Jens P Nielsen Byeong U Park

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Mathematical models Computer simulation Transformations (mathematics) Convergence Density Simulation Estimating techniques Forecasting Studies

Übergeordnetes Werk:	Enthalten in: The annals of statistics - Cleveland, Ohio [u.a.] : Institute of Mathematical Statistics, 1973, 45(2017), 3, Seite 1312
Übergeordnetes Werk:	volume:45 ; year:2017 ; number:3 ; pages:1312

Links:	Link aufrufen

Katalog-ID:	OLC199618878X

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allfields	PQ20171125 (DE-627)OLC199618878X (DE-599)GBVOLC199618878X (PRQ)p933-35f13a06ff6c012aebebcef0e0d4be0c0309cb24708bfe2638d80f01c1ca08dc0 (KEY)0154054820170000045000301312operationaltimeandinsampledensityforecasting DE-627 ger DE-627 rakwb eng 510 DNB Young K Lee verfasserin aut Operational time and in-sample density forecasting 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study. Mathematical models Computer simulation Transformations (mathematics) Convergence Density Simulation Estimating techniques Forecasting Studies Enno Mammen oth Jens P Nielsen oth Byeong U Park oth Enthalten in The annals of statistics Cleveland, Ohio [u.a.] : Institute of Mathematical Statistics, 1973 45(2017), 3, Seite 1312 (DE-627)129390569 (DE-600)184724-7 (DE-576)014776022 0090-5364 nnns volume:45 year:2017 number:3 pages:1312 https://search.proquest.com/docview/1927469322 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_2088 GBV_ILN_2409 GBV_ILN_4027 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4323 AR 45 2017 3 1312
spelling	PQ20171125 (DE-627)OLC199618878X (DE-599)GBVOLC199618878X (PRQ)p933-35f13a06ff6c012aebebcef0e0d4be0c0309cb24708bfe2638d80f01c1ca08dc0 (KEY)0154054820170000045000301312operationaltimeandinsampledensityforecasting DE-627 ger DE-627 rakwb eng 510 DNB Young K Lee verfasserin aut Operational time and in-sample density forecasting 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study. Mathematical models Computer simulation Transformations (mathematics) Convergence Density Simulation Estimating techniques Forecasting Studies Enno Mammen oth Jens P Nielsen oth Byeong U Park oth Enthalten in The annals of statistics Cleveland, Ohio [u.a.] : Institute of Mathematical Statistics, 1973 45(2017), 3, Seite 1312 (DE-627)129390569 (DE-600)184724-7 (DE-576)014776022 0090-5364 nnns volume:45 year:2017 number:3 pages:1312 https://search.proquest.com/docview/1927469322 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_2088 GBV_ILN_2409 GBV_ILN_4027 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4323 AR 45 2017 3 1312
allfields_unstemmed	PQ20171125 (DE-627)OLC199618878X (DE-599)GBVOLC199618878X (PRQ)p933-35f13a06ff6c012aebebcef0e0d4be0c0309cb24708bfe2638d80f01c1ca08dc0 (KEY)0154054820170000045000301312operationaltimeandinsampledensityforecasting DE-627 ger DE-627 rakwb eng 510 DNB Young K Lee verfasserin aut Operational time and in-sample density forecasting 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study. Mathematical models Computer simulation Transformations (mathematics) Convergence Density Simulation Estimating techniques Forecasting Studies Enno Mammen oth Jens P Nielsen oth Byeong U Park oth Enthalten in The annals of statistics Cleveland, Ohio [u.a.] : Institute of Mathematical Statistics, 1973 45(2017), 3, Seite 1312 (DE-627)129390569 (DE-600)184724-7 (DE-576)014776022 0090-5364 nnns volume:45 year:2017 number:3 pages:1312 https://search.proquest.com/docview/1927469322 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_2088 GBV_ILN_2409 GBV_ILN_4027 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4323 AR 45 2017 3 1312
allfieldsGer	PQ20171125 (DE-627)OLC199618878X (DE-599)GBVOLC199618878X (PRQ)p933-35f13a06ff6c012aebebcef0e0d4be0c0309cb24708bfe2638d80f01c1ca08dc0 (KEY)0154054820170000045000301312operationaltimeandinsampledensityforecasting DE-627 ger DE-627 rakwb eng 510 DNB Young K Lee verfasserin aut Operational time and in-sample density forecasting 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study. Mathematical models Computer simulation Transformations (mathematics) Convergence Density Simulation Estimating techniques Forecasting Studies Enno Mammen oth Jens P Nielsen oth Byeong U Park oth Enthalten in The annals of statistics Cleveland, Ohio [u.a.] : Institute of Mathematical Statistics, 1973 45(2017), 3, Seite 1312 (DE-627)129390569 (DE-600)184724-7 (DE-576)014776022 0090-5364 nnns volume:45 year:2017 number:3 pages:1312 https://search.proquest.com/docview/1927469322 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_2088 GBV_ILN_2409 GBV_ILN_4027 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4323 AR 45 2017 3 1312
allfieldsSound	PQ20171125 (DE-627)OLC199618878X (DE-599)GBVOLC199618878X (PRQ)p933-35f13a06ff6c012aebebcef0e0d4be0c0309cb24708bfe2638d80f01c1ca08dc0 (KEY)0154054820170000045000301312operationaltimeandinsampledensityforecasting DE-627 ger DE-627 rakwb eng 510 DNB Young K Lee verfasserin aut Operational time and in-sample density forecasting 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study. Mathematical models Computer simulation Transformations (mathematics) Convergence Density Simulation Estimating techniques Forecasting Studies Enno Mammen oth Jens P Nielsen oth Byeong U Park oth Enthalten in The annals of statistics Cleveland, Ohio [u.a.] : Institute of Mathematical Statistics, 1973 45(2017), 3, Seite 1312 (DE-627)129390569 (DE-600)184724-7 (DE-576)014776022 0090-5364 nnns volume:45 year:2017 number:3 pages:1312 https://search.proquest.com/docview/1927469322 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_2088 GBV_ILN_2409 GBV_ILN_4027 GBV_ILN_4046 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4305 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4323 AR 45 2017 3 1312
language	English
source	Enthalten in The annals of statistics 45(2017), 3, Seite 1312 volume:45 year:2017 number:3 pages:1312
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mathematical models Computer simulation Transformations (mathematics) Convergence Density Simulation Estimating techniques Forecasting Studies
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container_title	The annals of statistics
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author	Young K Lee
spellingShingle	Young K Lee ddc 510 misc Mathematical models misc Computer simulation misc Transformations (mathematics) misc Convergence misc Density misc Simulation misc Estimating techniques misc Forecasting misc Studies Operational time and in-sample density forecasting
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topic_title	510 DNB Operational time and in-sample density forecasting Mathematical models Computer simulation Transformations (mathematics) Convergence Density Simulation Estimating techniques Forecasting Studies
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title	Operational time and in-sample density forecasting
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title_auth	Operational time and in-sample density forecasting
abstract	In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study.
abstractGer	In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study.
abstract_unstemmed	In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study.
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title_short	Operational time and in-sample density forecasting
url	https://search.proquest.com/docview/1927469322
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author2	Enno Mammen Jens P Nielsen Byeong U Park
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