Statistics — Page 52
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Welch t-test
Welch's t-test is a parametric hypothesis test that compares the means of two independent groups without assuming their variances are equal. It was introduced by B. L. Welch in 1947 as a more robust generalization of Student's two-sample te...
White Test
The White test, introduced by Halbert White in 1980, is a general test for heteroskedasticity that makes no assumption about its functional form. It regresses the squared OLS residuals on the regressors, their squares, and their cross-produ...
Wilcoxon signed-rank test
The Wilcoxon signed-rank test is the nonparametric alternative to the paired t-test, comparing two related measurements on the same subjects to decide whether their typical difference is zero. It was introduced by Frank Wilcoxon in 1945 and...
Wild Bootstrap
The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a r...
Winsorized Estimation
Winsorized estimation is a robust technique that reduces the influence of outliers by clamping the extreme percentiles of a distribution to a chosen threshold. Introduced by Dixon (1960) and developed in the robust-estimation tradition of W...
Within-and-Between Analysis
Within-and-Between Analysis (WABA) is a methodology for determining the level of analysis at which a relationship between variables actually operates, developed by Fred Dansereau, Joseph Alutto, and Francis Yammarino in their 1984 book on t...
Wordfish Scaling
Wordfish scaling is an unsupervised text-as-data method that estimates a single latent position for each political document — a party manifesto, a legislative speech, a press release — directly from its word frequencies, without any referen...
Zero-Inflated Negative Binomial Regression
Zero-Inflated Negative Binomial regression is a count model, introduced by Greene (1994), that handles count data showing both an excess of zeros and overdispersion. It combines a binary inflation process that generates structural zeros wit...
Zero-Inflated Poisson Regression
Zero-Inflated Poisson regression is a two-component model for count data that contains more zeros than an ordinary Poisson model can explain. Introduced by Diane Lambert in 1992, it combines a logistic model for the zero-generating mechanis...
Zero-inflated model
A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generate...
Zivot-Andrews Structural Break Test
The Zivot-Andrews (ZA) test is a unit root test that endogenously identifies the most likely location of a single structural break in a time series. Unlike the standard ADF test, it does not require the researcher to pre-specify when the br...
Zivot-Andrews Test
The Zivot-Andrews (ZA) test, introduced by Eric Zivot and Donald Andrews in 1992, is a sequential unit-root test that allows for a single structural break at an unknown date. It extends the augmented Dickey-Fuller framework by endogenously...