Search Results - "Lima, Maria da Glória A."

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  1. 1

    Sequences of bias-adjusted covariance matrix estimators under heteroskedasticity of unknown form by Cribari-Neto, Francisco, Lima, Maria da Glória A.

    “…The linear regression model is commonly used by practitioners to model the relationship between the variable of interest and a set of explanatory variables…”
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    Journal Article
  2. 2

    Approximate inference in heteroskedastic regressions: A numerical evaluation by Cribari-Neto, Francisco, Lima, Maria da Glória A.

    Published in Journal of applied statistics (01-04-2010)
    “…The commonly made assumption that all stochastic error terms in the linear regression model share the same variance (homoskedasticity) is oftentimes violated…”
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    Journal Article
  3. 3

    A sequence of improved standard errors under heteroskedasticity of unknown form by Cribari-Neto, Francisco, Lima, Maria da Glória A.

    “…The linear regression model is commonly used by practitioners to model the relationship between the variable of interest and a set of explanatory variables…”
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    Journal Article
  4. 4

    Heteroskedasticity-consistent interval estimators by Cribari-Neto, Francisco, Lima, Maria da Glória A.

    “…The linear regression model is commonly used in applications. One of the assumptions made is that the error variances are constant across all observations…”
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    Journal Article
  5. 5

    New heteroskedasticity-robust standard errors for the linear regression model by Cribari-Neto, Francisco, da Gloria A. Lima, Maria

    “…Linear regressions fitted to cross-sectional data of tentimes display heteroskedasticity, that is, error variances that are not constant. A common modeling…”
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    Journal Article