Bayesian quantile regression using random B-spline series prior
A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increas...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Das, Priyam [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
23 |
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| Übergeordnetes Werk: |
Enthalten in: An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center - Phillips, Eileen ELSEVIER, 2014, Amsterdam |
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| Übergeordnetes Werk: |
volume:109 ; year:2017 ; pages:121-143 ; extent:23 |
| Links: |
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| DOI / URN: |
10.1016/j.csda.2016.11.014 |
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| 520 | |a A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. | ||
| 520 | |a A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. | ||
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10.1016/j.csda.2016.11.014 doi GBVA2017005000017.pica (DE-627)ELV035742283 (ELSEVIER)S0167-9473(16)30288-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Das, Priyam verfasserin aut Bayesian quantile regression using random B-spline series prior 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. Atlantic Hurricane data Elsevier Gaussian process Elsevier B-spline prior Elsevier US population data Elsevier Quantile regression Elsevier Ghosal, Subhashis oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:109 year:2017 pages:121-143 extent:23 https://doi.org/10.1016/j.csda.2016.11.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 109 2017 121-143 23 045F 004 |
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10.1016/j.csda.2016.11.014 doi GBVA2017005000017.pica (DE-627)ELV035742283 (ELSEVIER)S0167-9473(16)30288-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Das, Priyam verfasserin aut Bayesian quantile regression using random B-spline series prior 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. Atlantic Hurricane data Elsevier Gaussian process Elsevier B-spline prior Elsevier US population data Elsevier Quantile regression Elsevier Ghosal, Subhashis oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:109 year:2017 pages:121-143 extent:23 https://doi.org/10.1016/j.csda.2016.11.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 109 2017 121-143 23 045F 004 |
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10.1016/j.csda.2016.11.014 doi GBVA2017005000017.pica (DE-627)ELV035742283 (ELSEVIER)S0167-9473(16)30288-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Das, Priyam verfasserin aut Bayesian quantile regression using random B-spline series prior 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. Atlantic Hurricane data Elsevier Gaussian process Elsevier B-spline prior Elsevier US population data Elsevier Quantile regression Elsevier Ghosal, Subhashis oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:109 year:2017 pages:121-143 extent:23 https://doi.org/10.1016/j.csda.2016.11.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 109 2017 121-143 23 045F 004 |
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10.1016/j.csda.2016.11.014 doi GBVA2017005000017.pica (DE-627)ELV035742283 (ELSEVIER)S0167-9473(16)30288-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Das, Priyam verfasserin aut Bayesian quantile regression using random B-spline series prior 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. Atlantic Hurricane data Elsevier Gaussian process Elsevier B-spline prior Elsevier US population data Elsevier Quantile regression Elsevier Ghosal, Subhashis oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:109 year:2017 pages:121-143 extent:23 https://doi.org/10.1016/j.csda.2016.11.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 109 2017 121-143 23 045F 004 |
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10.1016/j.csda.2016.11.014 doi GBVA2017005000017.pica (DE-627)ELV035742283 (ELSEVIER)S0167-9473(16)30288-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Das, Priyam verfasserin aut Bayesian quantile regression using random B-spline series prior 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. Atlantic Hurricane data Elsevier Gaussian process Elsevier B-spline prior Elsevier US population data Elsevier Quantile regression Elsevier Ghosal, Subhashis oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:109 year:2017 pages:121-143 extent:23 https://doi.org/10.1016/j.csda.2016.11.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 109 2017 121-143 23 045F 004 |
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By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. 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A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. |
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A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. |
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A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ 1 and ξ 2 not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on ξ 1 and ξ 2 . The monotonicity constraint on the curves ξ 1 and ξ 2 are obtained through a spline basis expansion with coefficients increasing and lying in the unit interval. A Dirichlet prior distribution is put on the spacings of the coefficient vector. A finite random series based on splines obeys the shape restrictions. The proposed method is extended to multidimensional predictors such that the quantile regression depends on the predictors through an unknown linear combination only. In the simulation study, the proposed approach is compared with a Bayesian method using Gaussian process prior through an extensive simulation study and some other Bayesian approaches proposed in the literature. An application to a data on hurricane activities in the Atlantic region is given. The proposed method is also applied on region-wise population data of USA for the period 1985–2010. |
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