Statistics — Page 74
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Variational Inference with Missing Data
Variational inference with missing data is a scalable Bayesian approach that simultaneously approximates the posterior over latent variables and model parameters while imputing missing observations. Instead of integrating over all possible...
Vector Autoregression
Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large struct...
Vector Error Correction Model
The Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variabl...
Vertical Scaling
Vertical scaling places tests written for different grade levels onto a single continuous score scale so that growth from one grade to the next can be measured in common units. Unlike horizontal equating, which links alternate forms intende...
Von Thünen Land-Use Model
The von Thünen model is the founding theory of agricultural land use, explaining how the pattern of farming around a market emerges from transport costs and land rent. Set out by Johann Heinrich von Thünen in his 1826 work Der isolierte Sta...
Vote Buying Analysis
Vote buying analysis is the measurement-and-modeling toolkit for studying the distribution of private goods or cash in exchange for electoral support. Two problems define the field. First, who is being paid: Simeon Nichter's 2008 article sh...
Vulnerable Elders Survey-13
The Vulnerable Elders Survey-13 (VES-13) is a brief, function-based screening tool that identifies community-dwelling older adults at increased risk of health deterioration, functional decline, and death. Developed by Debra Saliba, Neil Wen...
W-Estimator
The W-estimator is a family of robust M-estimator variants for linear regression that use the Tukey bisquare and Welsch weight functions, introduced in the line of work going back to Beaton and Tukey (1974). Because its weights fall rapidly...
WHOQOL-OLD Module
The WHOQOL-OLD is a 24-item add-on module developed by the World Health Organization Quality of Life group to capture aspects of quality of life that matter especially to older adults but are not adequately covered by the generic WHOQOL-BRE...
Wavelet Financial Analysis
Wavelet financial analysis decomposes a financial time series into different frequency bands (time scales) so short- and long-term relationships can be studied at the same time. Drawing on the treatments of Gençay, Selçuk and Whitcher (2001...
Weber Industrial Location Model
Weber's industrial location model is the classic least-cost theory of where a manufacturing plant should locate. Developed by Alfred Weber in 1909, it finds the site that minimizes total transport cost between the sources of raw materials a...
Weibull Regression
Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains const...
Weighted Least Squares
Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting pre...
Welch ANOVA
Welch ANOVA is a parametric hypothesis test that compares the means of three or more independent groups when their variances are not equal. Introduced by B. L. Welch in 1951, it replaces classic one-way ANOVA whenever the homogeneity-of-var...
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...
Window DEA
Window Data Envelopment Analysis (Window DEA) is a non-parametric panel efficiency method that evaluates decision-making units (DMUs) over time by embedding each DMU's observations across a rolling temporal window into a single cross-sectio...
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...
Wizard of Oz
The Wizard of Oz method is a prototyping and evaluation technique where users interact with what appears to be an automated system, but behind the scenes, a human operator (the wizard) controls the system's behavior. Developed by John Kelle...
Wordfish
Wordfish is a statistical model for scaling documents on latent dimensions, developed by Slapin and Proksch (2008). Unlike reference-based methods like Wordscores, Wordfish uses a Poisson generative model to jointly estimate word frequencie...
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...