Optimal quantization applied to Sliced Inverse Regression
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
| dc.contributor.author | GÉGOUT-PETIT, Anne | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
| dc.contributor.author | AZAÏS, Romain | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
| dc.contributor.author | SARACCO, Jerome | |
| dc.date.accessioned | 2024-04-04T02:27:18Z | |
| dc.date.available | 2024-04-04T02:27:18Z | |
| dc.date.issued | 2012-02-01 | |
| dc.identifier.issn | 0378-3758 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/190022 | |
| dc.description | Article dans revue internationale à comité de lecture | |
| dc.description.abstractEn | Abstract: In this paper we consider a semiparametric regression model involving a $d$-dimensional quantitative explanatory variable $X$ and including a dimension reduction of $X$ via an index $\beta'X$. In this model, the main goal is to estimate the euclidean parameter $\beta$ and to predict the real response variable $Y$ conditionally to $X$. Our approach is based on sliced inverse regression (SIR) method and optimal quantization in $\mathbf{L}^p$-norm. We obtain the convergence of the proposed estimators of $\beta$ and of the conditional distribution. Simulation studies show the good numerical behavior of the proposed estimators for finite sample size. | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1016/j.jspi.2011.08.006 | |
| dc.subject.hal | Mathématiques [math]/Statistiques [math.ST] | |
| dc.subject.hal | Statistiques [stat]/Théorie [stat.TH] | |
| dc.identifier.arxiv | 1101.2121 | |
| bordeaux.journal | Journal of Statistical Planning and Inference | |
| bordeaux.page | 481-492 | |
| bordeaux.volume | 142 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.issue | 2 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-00556420 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| dc.subject.it | Optimal quantization | |
| dc.subject.it | Semiparametric regression model | |
| dc.subject.it | Sliced Inverse Regression (SIR) | |
| dc.subject.it | Reduction dimension | |
| dc.title.it | Optimal quantization applied to Sliced Inverse Regression | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-00556420v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Statistical%20Planning%20and%20Inference&rft.date=2012-02-01&rft.volume=142&rft.issue=2&rft.spage=481-492&rft.epage=481-492&rft.eissn=0378-3758&rft.issn=0378-3758&rft.au=G%C3%89GOUT-PETIT,%20Anne&AZA%C3%8FS,%20Romain&SARACCO,%20Jerome&rft.genre=article |
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