"sparsistency" meaning in All languages combined

{
  "etymology_templates": [
    {
      "args": {
        "1": "en",
        "2": "sparse",
        "3": "consistency"
      },
      "expansion": "Blend of sparse + consistency",
      "name": "blend"
    }
  ],
  "etymology_text": "Blend of sparse + consistency",
  "head_templates": [
    {
      "args": {
        "1": "-"
      },
      "expansion": "sparsistency (uncountable)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        {
          "kind": "other",
          "name": "English blends",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [
            "Entries with incorrect language header",
            "Entry maintenance"
          ],
          "source": "w"
        },
        {
          "kind": "topical",
          "langcode": "en",
          "name": "Statistics",
          "orig": "en:Statistics",
          "parents": [
            "Formal sciences",
            "Mathematics",
            "Sciences",
            "All topics",
            "Fundamental"
          ],
          "source": "w"
        }
      ],
      "examples": [
        {
          "ref": "2015, Edouard Ollier, Vivian Viallon, “Regression modeling on stratified data: automatic and covariate-specific selection of the reference stratum with simple L_1-norm penalties”, in arXiv",
          "text": "Its implementation can be done with available packages under a variety of models and, in the linear regression model, we show it is sparsistent under conditions similar to those ensuring sparsistency for an oracular version of the reference stratum strategy.",
          "type": "quotation"
        }
      ],
      "glosses": [
        "Let mathbf b be a vector and define the support operatorname supp( mathbf b)=i: mathbf bᵢ≠0 where mathbf bᵢ is the ith element of mathbf b. Let ̂mathbf b be an estimator for mathbf b. Then sparsistency is the property that the support of the estimator converges to the true support as the number of samples grows to infinity."
      ],
      "id": "en-sparsistency-en-noun-~9sSQJ--",
      "links": [
        [
          "statistics",
          "statistics"
        ]
      ],
      "raw_glosses": [
        "(statistics) Let mathbf b be a vector and define the support operatorname supp( mathbf b)=i: mathbf bᵢ≠0 where mathbf bᵢ is the ith element of mathbf b. Let ̂mathbf b be an estimator for mathbf b. Then sparsistency is the property that the support of the estimator converges to the true support as the number of samples grows to infinity."
      ],
      "related": [
        {
          "word": "sparsistent"
        }
      ],
      "tags": [
        "uncountable"
      ],
      "topics": [
        "mathematics",
        "sciences",
        "statistics"
      ],
      "wikipedia": [
        "Consistency_(statistics)#Sparsistency"
      ]
    }
  ],
  "word": "sparsistency"
}

{
  "etymology_templates": [
    {
      "args": {
        "1": "en",
        "2": "sparse",
        "3": "consistency"
      },
      "expansion": "Blend of sparse + consistency",
      "name": "blend"
    }
  ],
  "etymology_text": "Blend of sparse + consistency",
  "head_templates": [
    {
      "args": {
        "1": "-"
      },
      "expansion": "sparsistency (uncountable)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "related": [
    {
      "word": "sparsistent"
    }
  ],
  "senses": [
    {
      "categories": [
        "English blends",
        "English entries with incorrect language header",
        "English lemmas",
        "English nouns",
        "English terms with quotations",
        "English uncountable nouns",
        "en:Statistics"
      ],
      "examples": [
        {
          "ref": "2015, Edouard Ollier, Vivian Viallon, “Regression modeling on stratified data: automatic and covariate-specific selection of the reference stratum with simple L_1-norm penalties”, in arXiv",
          "text": "Its implementation can be done with available packages under a variety of models and, in the linear regression model, we show it is sparsistent under conditions similar to those ensuring sparsistency for an oracular version of the reference stratum strategy.",
          "type": "quotation"
        }
      ],
      "glosses": [
        "Let mathbf b be a vector and define the support operatorname supp( mathbf b)=i: mathbf bᵢ≠0 where mathbf bᵢ is the ith element of mathbf b. Let ̂mathbf b be an estimator for mathbf b. Then sparsistency is the property that the support of the estimator converges to the true support as the number of samples grows to infinity."
      ],
      "links": [
        [
          "statistics",
          "statistics"
        ]
      ],
      "raw_glosses": [
        "(statistics) Let mathbf b be a vector and define the support operatorname supp( mathbf b)=i: mathbf bᵢ≠0 where mathbf bᵢ is the ith element of mathbf b. Let ̂mathbf b be an estimator for mathbf b. Then sparsistency is the property that the support of the estimator converges to the true support as the number of samples grows to infinity."
      ],
      "tags": [
        "uncountable"
      ],
      "topics": [
        "mathematics",
        "sciences",
        "statistics"
      ],
      "wikipedia": [
        "Consistency_(statistics)#Sparsistency"
      ]
    }
  ],
  "word": "sparsistency"
}