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Glossario IA

Il dizionario completo dell'Intelligenza Artificiale

162
categorie
2.032
sottocategorie
23.060
termini
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Sensitivity Analysis

Systematic method evaluating how variations in input parameters affect model results to identify the most influential factors.

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Robustness Test

Statistical procedure verifying the stability of analysis conclusions when underlying assumptions or data are modified.

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Leverage Points

Observations with extreme predictor variable values that exert disproportionate influence on regression model fitting.

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Cook's Distance

Quantitative measure combining leverage and residuals to identify observations having excessive impact on estimated regression coefficients.

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Influence Analysis

Set of statistical diagnostics quantitatively identifying individual observations significantly affecting model parameters and predictions.

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Collinearity Diagnostic

Procedure detecting high correlations between predictor variables that may compromise the stability and interpretability of regression coefficients.

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Structural Break Test

Statistical analysis checking whether model parameters remain constant across different periods or data subsets.

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VIF (Variance Inflation Factor)

Indicator quantifying the increase in coefficient variance due to multicollinearity, with values above 10 indicating serious problems.

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Jackknife method

Systematic resampling technique that sequentially removes each observation to assess the sensitivity of estimators to individual observations.

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Normality test of errors

Statistical verification of the distribution of residuals to ensure that the underlying inference assumptions of the model are met.

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Ramsey RESET test

Specification diagnostic testing whether the functional form of the model is appropriate by detecting nonlinearities or omitted variables.

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Coefficient stability

Evaluation of the constancy of estimated parameters when data or model specifications are changed, measuring the robustness of inferences.

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Winsorization methods

Technique for treating extreme values by replacing observations beyond certain quantiles with the values of those quantiles to reduce their influence.

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