Variable selection for inhomogeneous spatial point process models
In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selecti...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yue, Yu (Ryan) [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © 2015 Statistical Society of Canada |
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| Schlagwörter: |
variable selection via regularization |
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| Übergeordnetes Werk: |
Enthalten in: The Canadian journal of statistics - Hoboken, NJ : Wiley, 1973, 43(2015), 2, Seite 288-305 |
|---|---|
| Übergeordnetes Werk: |
volume:43 ; year:2015 ; number:2 ; pages:288-305 |
| Links: |
|---|
| DOI / URN: |
10.1002/cjs.11244 |
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| Katalog-ID: |
OLC1966671415 |
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| 520 | |a In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. | ||
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10.1002/cjs.11244 doi PQ20160617 (DE-627)OLC1966671415 (DE-599)GBVOLC1966671415 (PRQ)c2724-c89e7b93396196cd87b49cd7f3dc3e09885435f4f0dac8a126df34965b9204a63 (KEY)0061553620150000043000200288variableselectionforinhomogeneousspatialpointproce DE-627 ger DE-627 rakwb eng 310 510 DE-600 31.73 bkl Yue, Yu (Ryan) verfasserin aut Variable selection for inhomogeneous spatial point process models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. Nutzungsrecht: © 2015 Statistical Society of Canada MSC 2010 Berman–Turner approximation spatial point processes weighted Poisson likelihood Intensity function variable selection via regularization maximum pseudo‐likelihood estimator Primary 62M30 secondary 62J07 New York City New York Generalized linear models Decision making models Regularization methods Poisson distribution Effectiveness studies Simulation United States--US Loh, Ji Meng oth Enthalten in The Canadian journal of statistics Hoboken, NJ : Wiley, 1973 43(2015), 2, Seite 288-305 (DE-627)12945009X (DE-600)197355-1 (DE-576)014815710 0319-5724 nnns volume:43 year:2015 number:2 pages:288-305 http://dx.doi.org/10.1002/cjs.11244 Volltext http://onlinelibrary.wiley.com/doi/10.1002/cjs.11244/abstract http://search.proquest.com/docview/1686446845 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_11 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4126 GBV_ILN_4311 31.73 AVZ AR 43 2015 2 288-305 |
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10.1002/cjs.11244 doi PQ20160617 (DE-627)OLC1966671415 (DE-599)GBVOLC1966671415 (PRQ)c2724-c89e7b93396196cd87b49cd7f3dc3e09885435f4f0dac8a126df34965b9204a63 (KEY)0061553620150000043000200288variableselectionforinhomogeneousspatialpointproce DE-627 ger DE-627 rakwb eng 310 510 DE-600 31.73 bkl Yue, Yu (Ryan) verfasserin aut Variable selection for inhomogeneous spatial point process models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. Nutzungsrecht: © 2015 Statistical Society of Canada MSC 2010 Berman–Turner approximation spatial point processes weighted Poisson likelihood Intensity function variable selection via regularization maximum pseudo‐likelihood estimator Primary 62M30 secondary 62J07 New York City New York Generalized linear models Decision making models Regularization methods Poisson distribution Effectiveness studies Simulation United States--US Loh, Ji Meng oth Enthalten in The Canadian journal of statistics Hoboken, NJ : Wiley, 1973 43(2015), 2, Seite 288-305 (DE-627)12945009X (DE-600)197355-1 (DE-576)014815710 0319-5724 nnns volume:43 year:2015 number:2 pages:288-305 http://dx.doi.org/10.1002/cjs.11244 Volltext http://onlinelibrary.wiley.com/doi/10.1002/cjs.11244/abstract http://search.proquest.com/docview/1686446845 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_11 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4126 GBV_ILN_4311 31.73 AVZ AR 43 2015 2 288-305 |
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10.1002/cjs.11244 doi PQ20160617 (DE-627)OLC1966671415 (DE-599)GBVOLC1966671415 (PRQ)c2724-c89e7b93396196cd87b49cd7f3dc3e09885435f4f0dac8a126df34965b9204a63 (KEY)0061553620150000043000200288variableselectionforinhomogeneousspatialpointproce DE-627 ger DE-627 rakwb eng 310 510 DE-600 31.73 bkl Yue, Yu (Ryan) verfasserin aut Variable selection for inhomogeneous spatial point process models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. Nutzungsrecht: © 2015 Statistical Society of Canada MSC 2010 Berman–Turner approximation spatial point processes weighted Poisson likelihood Intensity function variable selection via regularization maximum pseudo‐likelihood estimator Primary 62M30 secondary 62J07 New York City New York Generalized linear models Decision making models Regularization methods Poisson distribution Effectiveness studies Simulation United States--US Loh, Ji Meng oth Enthalten in The Canadian journal of statistics Hoboken, NJ : Wiley, 1973 43(2015), 2, Seite 288-305 (DE-627)12945009X (DE-600)197355-1 (DE-576)014815710 0319-5724 nnns volume:43 year:2015 number:2 pages:288-305 http://dx.doi.org/10.1002/cjs.11244 Volltext http://onlinelibrary.wiley.com/doi/10.1002/cjs.11244/abstract http://search.proquest.com/docview/1686446845 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_11 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4126 GBV_ILN_4311 31.73 AVZ AR 43 2015 2 288-305 |
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10.1002/cjs.11244 doi PQ20160617 (DE-627)OLC1966671415 (DE-599)GBVOLC1966671415 (PRQ)c2724-c89e7b93396196cd87b49cd7f3dc3e09885435f4f0dac8a126df34965b9204a63 (KEY)0061553620150000043000200288variableselectionforinhomogeneousspatialpointproce DE-627 ger DE-627 rakwb eng 310 510 DE-600 31.73 bkl Yue, Yu (Ryan) verfasserin aut Variable selection for inhomogeneous spatial point process models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. Nutzungsrecht: © 2015 Statistical Society of Canada MSC 2010 Berman–Turner approximation spatial point processes weighted Poisson likelihood Intensity function variable selection via regularization maximum pseudo‐likelihood estimator Primary 62M30 secondary 62J07 New York City New York Generalized linear models Decision making models Regularization methods Poisson distribution Effectiveness studies Simulation United States--US Loh, Ji Meng oth Enthalten in The Canadian journal of statistics Hoboken, NJ : Wiley, 1973 43(2015), 2, Seite 288-305 (DE-627)12945009X (DE-600)197355-1 (DE-576)014815710 0319-5724 nnns volume:43 year:2015 number:2 pages:288-305 http://dx.doi.org/10.1002/cjs.11244 Volltext http://onlinelibrary.wiley.com/doi/10.1002/cjs.11244/abstract http://search.proquest.com/docview/1686446845 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_11 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4126 GBV_ILN_4311 31.73 AVZ AR 43 2015 2 288-305 |
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10.1002/cjs.11244 doi PQ20160617 (DE-627)OLC1966671415 (DE-599)GBVOLC1966671415 (PRQ)c2724-c89e7b93396196cd87b49cd7f3dc3e09885435f4f0dac8a126df34965b9204a63 (KEY)0061553620150000043000200288variableselectionforinhomogeneousspatialpointproce DE-627 ger DE-627 rakwb eng 310 510 DE-600 31.73 bkl Yue, Yu (Ryan) verfasserin aut Variable selection for inhomogeneous spatial point process models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. Nutzungsrecht: © 2015 Statistical Society of Canada MSC 2010 Berman–Turner approximation spatial point processes weighted Poisson likelihood Intensity function variable selection via regularization maximum pseudo‐likelihood estimator Primary 62M30 secondary 62J07 New York City New York Generalized linear models Decision making models Regularization methods Poisson distribution Effectiveness studies Simulation United States--US Loh, Ji Meng oth Enthalten in The Canadian journal of statistics Hoboken, NJ : Wiley, 1973 43(2015), 2, Seite 288-305 (DE-627)12945009X (DE-600)197355-1 (DE-576)014815710 0319-5724 nnns volume:43 year:2015 number:2 pages:288-305 http://dx.doi.org/10.1002/cjs.11244 Volltext http://onlinelibrary.wiley.com/doi/10.1002/cjs.11244/abstract http://search.proquest.com/docview/1686446845 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_11 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4126 GBV_ILN_4311 31.73 AVZ AR 43 2015 2 288-305 |
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Enthalten in The Canadian journal of statistics 43(2015), 2, Seite 288-305 volume:43 year:2015 number:2 pages:288-305 |
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310 510 DE-600 31.73 bkl Variable selection for inhomogeneous spatial point process models MSC 2010 Berman–Turner approximation spatial point processes weighted Poisson likelihood Intensity function variable selection via regularization maximum pseudo‐likelihood estimator Primary 62M30 secondary 62J07 New York City New York Generalized linear models Decision making models Regularization methods Poisson distribution Effectiveness studies Simulation United States--US |
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Variable selection for inhomogeneous spatial point process models |
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variable selection for inhomogeneous spatial point process models |
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Variable selection for inhomogeneous spatial point process models |
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In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. |
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In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. |
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In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three-regularization methods in our procedure. We then use the procedure in two applications, modelling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model. |
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Variable selection for inhomogeneous spatial point process models |
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