Statistics — Page 38
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Robust Bayesian Network
A Robust Bayesian Network extends a classical Bayesian network by replacing each precise conditional probability table with a set of allowable probability distributions — called a credal set. Instead of a single probability for each query,...
Robust Canonical Correlation Analysis
Robust canonical correlation analysis extends classical CCA by replacing the standard sample covariance matrix with a robust estimator — such as the Minimum Covariance Determinant (MCD) or S-estimator — so that outlying observations do not...
Robust Cluster Analysis
Robust Cluster Analysis is a trimmed model-based clustering method, introduced by García-Escudero and colleagues in 2008, that partitions continuous multivariate data into clusters while resisting the influence of outliers and noise. By set...
Robust Co-Kriging
Robust Co-Kriging is a multivariate geostatistical interpolation method that jointly estimates values at unsampled locations using two or more spatially correlated variables, while applying robust estimators for the variogram and cross-vari...
Robust Confirmatory Factor Analysis
Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever...
Robust Conjoint Analysis
Robust conjoint analysis decomposes respondent preferences for multi-attribute products or services into part-worth utilities while guarding against the distorting influence of outlying ratings or unusual respondents. It adapts classical co...
Robust Correlation
Robust Correlation is a family of association measures that resist outliers, covering Spearman's rank correlation, Kendall's tau, and the biweight midcorrelation. Drawing on the robust-statistics tradition described by Wilcox (2012) and She...
Robust Correspondence Analysis
Robust Correspondence Analysis (RCA) extends classical correspondence analysis to contingency tables that contain outlying rows or columns. By replacing the standard singular value decomposition with a robust alternative, RCA produces biplo...
Robust Covariance (MCD)
Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (199...
Robust Cox Regression
Robust Cox regression fits the standard Cox proportional hazards model but replaces the model-based variance estimate with a sandwich (Huber-White) estimator. This yields valid standard errors and confidence intervals even when observations...
Robust Cronbach's Alpha
Robust Cronbach's alpha adapts the classical internal consistency coefficient to data that violate the assumption of multivariate normality or contain influential outliers. By replacing the conventional sample covariance matrix with a robus...
Robust DCC-GARCH
The Robust DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by replacing standard quasi-maximum likelihood estimation with outlier-resistant or composite-likelihood techniques. This preserves accurate time-va...
Robust Descriptive Statistics
Robust descriptive statistics summarize the location, spread, and shape of a dataset using measures that remain meaningful even when a fraction of the data contains outliers or severe departures from normality. Core tools include the median...
Robust Difference GMM
Robust Difference GMM applies the Arellano-Bond first-difference GMM estimator with heteroscedasticity- and autocorrelation-consistent (HAC) or Windmeijer-corrected standard errors, delivering valid inference for dynamic panel models even w...
Robust Discriminant Analysis
Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such...
Robust Dynamic Panel Data Model
The robust dynamic panel data model combines the dynamic panel GMM framework — which handles endogeneity from lagged dependent variables and unobserved heterogeneity — with robust covariance estimation that remains valid under heteroscedast...
Robust EGARCH
Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme ob...
Robust Effect Size Analysis
Robust effect size analysis quantifies the magnitude of a difference or association using estimators that are resistant to outliers and violations of normality. Rather than relying on classical statistics such as Cohen's d based on sample m...
Robust Engle-Granger Cointegration
The Robust Engle-Granger cointegration test adapts the classic two-step Engle-Granger procedure to withstand outliers, heavy-tailed error distributions, and additive noise that can severely distort standard residual-based cointegration infe...
Robust Exploratory Factor Analysis
Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard E...
Robust Factor Analysis
Robust Factor Analysis recovers the latent factor structure of multivariate continuous data while resisting the distorting pull of outliers. Introduced by Pison, Rousseeuw, Filzmoser and Croux (2003), it replaces the classical sample covari...
Robust Fisher's exact test
The robust Fisher's exact test extends Fisher's classic exact test for contingency tables by applying conservative-correcting adjustments — most commonly the mid-p correction — to reduce the extreme conservatism of the standard exact test....
Robust Fixed Effects Model
The robust fixed effects model combines the within-group estimator for panel data with variance-covariance matrices that remain valid under heteroscedasticity and within-unit error correlation. Introduced by Arellano (1987), cluster-robust...
Robust Friedman test
The robust Friedman test is a nonparametric procedure for comparing three or more related (within-subjects) conditions that replaces standard ranking or mean-based summaries with robust location estimates — typically trimmed means or Winsor...