ScholarGate

Ontdekken

Bibliotheek Mijn bibliotheek Bureau Vooronderzoek Review Studio Assistent

Werkruimte

Bouw uw boekenplank

Sla methoden op, orden collecties en neem ze mee naar uw bureau.

Account aanmaken

Bibliotheek

/ Bladeren

De bibliotheek

Verken de wetenschap per methode, vakgebied en bewijs.

Eén catalogus van onderzoeksmethoden — leer hoe elke methode werkt, wanneer je haar gebruikt en wat ze niet kan.

6,480 methoden11 vakgebieden7 methodefamilies40 talen

WetenschapsatlasBreng de structuur van de wetenschap in kaart voordat je haar gebruikt.Vakgebieden · methoden · bewijsroutesVerken de kaart

VakgebiedHealth & Medicine716 Psychology570 Business & Finance410 Engineering330 Life Sciences263 Education261 Research Practice

Een inhoudsgerichte naslagbibliotheek voor onderzoeksmethoden — wat elke methode is, hoe ze werkt en waar ze vandaan komt.

Open data (CC-BY)

Ontdekken

Bibliotheek
Zoek methoden…
Bladeren per vakgebied
Vakgebieden
Reis
Vergelijken
Welke methode?

Referentie

Vakgebieden
Atlas
Begrippenlijst
Methodologie
Filosofie

Werkruimte

Mijn bibliotheek
Bureau
Chat

Bedrijf

Over ons
Prijzen
Contact
Stel een methode voor

De vermeldingen zijn ter referentie samengesteld uit gepubliceerde bronnen. Het controleren van de juistheid en geschiktheid van informatie voor uw eigen gebruik blijft uw eigen verantwoordelijkheid.

© 2026 ScholarGate · Een naslagbibliotheek voor onderzoeksmethoden

Privacy
Cookies
Voorwaarden
Account verwijderen

248

Natural Sciences236

Social Sciences185

Environment & Sustainability160

MethodeStatistiek1,836 AI & ML1,661 Besliskunde932 Onderzoeksmethoden1,354 Meten1,745 Causaliteit & evidentie532 Onderzoekspraktijk118

1,411 methoden · StatistiekWissen

Echte methoden die bij je filter passen.

SorterenPopulariteit A–Z Z–A Nieuwste

U-MIDAS

U-MIDAS (Unrestricted MIDAS) is a regression framework designed to handle mixed-frequency data—when explanatory variables arrive at different sampling frequencies (e.g., monthly GDP mixed with daily stock returns). Introduced by Ghysels and colleagues (2007), it eliminates the restrictive lag-structure polynomial const

Uncertainty Quantification

Uncertainty Quantification (UQ) is a computational framework for systematically measuring how uncertainty in the inputs of a model propagates into uncertainty in its outputs. Building on Wiener's polynomial chaos theory (1938) and formalised for general stochastic problems by Xiu and Karniadakis (2002), UQ uses two pri

Unfolding Model

The Unfolding Model is a geometric approach to preference analysis that represents both individuals and choice objects (stimuli) as points in a shared low-dimensional space. Originating with Clyde Coombs's foundational 1950 work on preferential choice and rigorously systematized by Borg and Groenen (2005), the model as

spatial analysis

Universal Kriging

Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling

Van der Waerden Test

The Van der Waerden test is a nonparametric k-sample hypothesis test that converts observations into normal scores — the quantiles of a standard normal distribution — before comparing groups. Introduced by Bartel Leendert van der Waerden in 1952, it can achieve higher statistical power than the Kruskal-Wallis test when

VaR Backtesting

VaR backtesting is a family of statistical tests that validate a risk model by comparing its Value-at-Risk forecasts against realised losses. It builds on Kupiec's (1995) unconditional coverage test, Christoffersen's (1998) conditional coverage test, and the Engle-Manganelli Dynamic Quantile (DQ) test.

VAR Model

Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-s

Variance Inflation Factor

The Variance Inflation Factor (VIF) is a scalar diagnostic statistic proposed by Donald Marquardt (1970) that quantifies how much the variance of an estimated regression coefficient increases due to linear dependence—multicollinearity—among the predictors in an ordinary least squares model. It is routinely applied in e

Variational Inference

Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the t

Variational Inference with Measurement Error

Variational inference with measurement error is a scalable Bayesian approach that simultaneously estimates model parameters and latent true covariates when observed variables are contaminated by noise. Rather than sampling the posterior via MCMC, it finds the closest tractable distribution to the true posterior by maxi

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 values of the missing entries exactly, it posits a tractable approximate distrib

VECM

The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework.

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 structural macroeconomic models, VAR has become the standard workhorse for dynamic ana

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 variable corrects back toward equilibrium after a shock, making it the standard tool fo

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 toward zero as a residual grows, it resists outliers more strongly than the Hub

Wavelet Coherence

Wavelet coherence (WTC) is a normalized measure of correlation between two time series in the time-frequency domain, eliminating the amplitude-dependence of the raw cross-wavelet transform. Introduced by Torrence and Webster (1999) and formalized by Grinsted, Moore, and Jevrejeva (2004), WTC quantifies how tightly two

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 constant over time, while covariates shift the scale of the distribution to express h

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 precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 19

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-variance assumption fails, while still requiring approximately normal data.

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 test for situations where the two groups have different spread.

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-products, so it can detect heteroskedasticity related to any of these terms. The same

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 works on continuous or ordinal data without assuming normality.

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 random sign, so that standard errors and confidence intervals stay valid when the

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 Wilcox, it keeps every observation in the sample rather than discarding any.

X-13ARIMA-SEATS

X-13ARIMA-SEATS is the standard seasonal adjustment program produced by the U.S. Census Bureau, combining RegARIMA pre-adjustment with either the classical X-11 filter or the model-based SEATS signal-extraction algorithm. It is the official tool used by national statistical agencies worldwide — including Eurostat and t

civil engineering

Yield Line Theory

Yield Line Theory is a plastic limit-analysis method used in structural civil engineering to determine the ultimate load-carrying capacity of reinforced concrete slabs. Developed by K. W. Johansen in the 1940s, it assumes that at failure the slab subdivides into rigid regions separated by lines of intense plastic rotat

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 generates structural zeros; the other is a count process that generates both zeros and p

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 with a negative binomial count process, making it one of the most widely used distr

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 mechanism with a Poisson model for the genuine counting process.

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 break occurred, making it robust to data-driven regime shifts such as policy chang

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 selecting the break point that provides the strongest evidence against the unit-

← 2324 / 24