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Essec\Faculty\Model\Contribution {#2233 ▼
  #_index: "academ_contributions"
  #_id: "13887"
  #_source: array:26 [
    "id" => "13887"
    "slug" => "13887-high-dimensional-var-with-low-rank-transition"
    "yearMonth" => "2020-07"
    "year" => "2020"
    "title" => "High-dimensional VAR with low-rank transition"
    "description" => "ALQUIER, P., BERTIN, K., DOUKHAN, P. et GARNIER, R. (2020). High-dimensional VAR with low-rank transition. <i>Statistics and Computing</i>, 30(4), pp. 1139-1153. ALQUIER, P., BERTIN, K., DOUKHAN, P. et GARNIER, R. (2020). High-dimensional VAR with low-rank trans "
    "authors" => array:4 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:1 [
        "name" => "BERTIN Karine"
      ]
      2 => array:1 [
        "name" => "DOUKHAN Paul"
      ]
      3 => array:1 [
        "name" => "GARNIER Rémy"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2023-03-22 09:36:35"
    "publicationUrl" => "https://link.springer.com/article/10.1007/s11222-020-09929-7"
    "publicationInfo" => array:3 [
      "pages" => "1139-1153"
      "volume" => "30"
      "number" => "4"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "We propose a vector auto-regressive model with a low-rank constraint on the transition matrix. This model is well suited to predict high-dimensional series that are highly correlated, or that are driven by a small number of hidden factors. While our model has formal similarities with factor models, its structure is more a way to reduce the dimension in order to improve the predictions, rather than a way to define interpretable factors. We provide an estimator for the transition matrix in a very general setting and study its performances in terms of prediction and adaptation to the unknown rank. Our method obtains good result on simulated data, in particular when the rank of the underlying process is small. On macroeconomic data from Giannone et al. (Rev Econ Stat 97(2):436–451, 2015), our method is competitive with state-of-the-art methods in small dimension and even improves on them in high dimension. We propose a vector auto-regressive model with a low-rank constraint on the transition matrix. This  "
      "en" => "We propose a vector auto-regressive model with a low-rank constraint on the transition matrix. This model is well suited to predict high-dimensional series that are highly correlated, or that are driven by a small number of hidden factors. While our model has formal similarities with factor models, its structure is more a way to reduce the dimension in order to improve the predictions, rather than a way to define interpretable factors. We provide an estimator for the transition matrix in a very general setting and study its performances in terms of prediction and adaptation to the unknown rank. Our method obtains good result on simulated data, in particular when the rank of the underlying process is small. On macroeconomic data from Giannone et al. (Rev Econ Stat 97(2):436–451, 2015), our method is competitive with state-of-the-art methods in small dimension and even improves on them in high dimension. We propose a vector auto-regressive model with a low-rank constraint on the transition matrix. This  "
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2025-04-02T11:21:48.000Z"
    "docTitle" => "High-dimensional VAR with low-rank transition"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, BERTIN Karine, DOUKHAN Paul, GARNIER Rémy"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, BERTIN Karine, DOUKHAN Paul, GARNIER Rémy</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2020</span> <span class="document-property-authors">ALQUIER Pierre, BERTIN Karine, DOUKHAN Paul, GARNIER Rémy</s "
    "keywordList" => ""
    "docPreview" => "<b>High-dimensional VAR with low-rank transition</b><br><span>2020-07 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://link.springer.com/article/10.1007/s11222-020-09929-7" target="_blank">High-dimensional VAR with low-rank transition</a> <a href="https://link.springer.com/article/10.1007/s11222-020-09929-7" target="_blank">High-dimensio "
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.771896
  +"parent": null
}