"bias-robust" meaning in All languages combined

{
  "forms": [
    {
      "form": "more bias-robust",
      "tags": [
        "comparative"
      ]
    },
    {
      "form": "most bias-robust",
      "tags": [
        "superlative"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {},
      "expansion": "bias-robust (comparative more bias-robust, superlative most bias-robust)",
      "name": "en-adj"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "adj",
  "senses": [
    {
      "categories": [
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [
            "Entries with incorrect language header",
            "Entry maintenance"
          ],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with 1 entry",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with entries",
          "parents": [],
          "source": "w"
        }
      ],
      "derived": [
        {
          "word": "bias-robustness"
        }
      ],
      "examples": [
        {
          "ref": "2002, Usama M. Fayyad, Georges G. Grinstein, Andreas Wierse, Information Visualization in Data Mining and Knowledge Discovery, page 310:",
          "text": "The theory of bias-robust regression [ Mar89 ] provides guidelines for the selection of good robust estimators. For calculating beta, either of the following two bias-robust estimators work quite well: (a) an adaptive least trimmed squares estimate, and (b) an MM estimate of [Yoh88], computed as suggested in [Yoh91].",
          "type": "quote"
        },
        {
          "ref": "2011, Survey Methodology - Volumes 37-38, page 153:",
          "text": "Bias-robust methods for estimating the mean squared error ( MSE ) of linear predictors of finite population quantities, i.e., methods that remain approximately unbiased under failure of assumptions about second order and higher moments, have been developed.",
          "type": "quote"
        }
      ],
      "glosses": [
        "Pertaining to statistics that are relatively unbiased even when some assumptions about the data or the model do not hold true."
      ],
      "id": "en-bias-robust-en-adj-LP3FbWvo"
    }
  ],
  "word": "bias-robust"
}

{
  "derived": [
    {
      "word": "bias-robustness"
    }
  ],
  "forms": [
    {
      "form": "more bias-robust",
      "tags": [
        "comparative"
      ]
    },
    {
      "form": "most bias-robust",
      "tags": [
        "superlative"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {},
      "expansion": "bias-robust (comparative more bias-robust, superlative most bias-robust)",
      "name": "en-adj"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "adj",
  "senses": [
    {
      "categories": [
        "English adjectives",
        "English entries with incorrect language header",
        "English lemmas",
        "English multiword terms",
        "English terms with quotations",
        "Pages with 1 entry",
        "Pages with entries"
      ],
      "examples": [
        {
          "ref": "2002, Usama M. Fayyad, Georges G. Grinstein, Andreas Wierse, Information Visualization in Data Mining and Knowledge Discovery, page 310:",
          "text": "The theory of bias-robust regression [ Mar89 ] provides guidelines for the selection of good robust estimators. For calculating beta, either of the following two bias-robust estimators work quite well: (a) an adaptive least trimmed squares estimate, and (b) an MM estimate of [Yoh88], computed as suggested in [Yoh91].",
          "type": "quote"
        },
        {
          "ref": "2011, Survey Methodology - Volumes 37-38, page 153:",
          "text": "Bias-robust methods for estimating the mean squared error ( MSE ) of linear predictors of finite population quantities, i.e., methods that remain approximately unbiased under failure of assumptions about second order and higher moments, have been developed.",
          "type": "quote"
        }
      ],
      "glosses": [
        "Pertaining to statistics that are relatively unbiased even when some assumptions about the data or the model do not hold true."
      ]
    }
  ],
  "word": "bias-robust"
}