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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.

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Natural Sciences236
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MethodeStatistiek1,836AI & ML1,661Besliskunde932Onderzoeksmethoden1,354Meten1,745Causaliteit & evidentie532Onderzoekspraktijk118
1,411 methoden · StatistiekWissen
Echte methoden die bij je filter passen.
SorterenPopulariteitA–ZZ–ANieuwste
econometrics

Quantile VAR

Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behav

2 bronnen2006
econometrics

Quantile-on-Quantile Regression

Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the ou

2 bronnen2015
research design

Quantitative-dominant multilevel mixed methods

Quantitative-dominant multilevel mixed methods design is a mixed methods approach in which quantitative inquiry carries the primary evidential weight while qualitative data play an auxiliary, illuminating role, and both strands are applied across two or more hierarchically nested levels of analysis — for example, stude

2 bronnen2003
spatial analysis

Radiation Model

The Radiation Model, introduced by Simini et al. in 2012, is a parameter-free model for predicting human mobility and migration flows between geographic locations. Drawing an analogy from radiation physics, it predicts trip volumes based solely on population sizes at origin and destination, and the intervening populati

1 bron2012
econometrics

Ramsey RESET Test

The Ramsey RESET test, proposed by James Ramsey in 1969, is a general test for functional-form misspecification in a linear regression — for omitted nonlinear relationships between the response and the regressors. It adds powers of the fitted values to the model and checks whether they significantly improve the fit; if

1 bron1969
economics

Ramsey-Cass-Koopmans Model

The Ramsey-Cass-Koopmans model, developed initially by Frank Ramsey in 1928 and formalized by David Cass and Tjalling Koopmans in 1965, is the workhorse model of macroeconomic growth theory. It describes how rational consumers optimize consumption and savings over an infinite horizon, subject to an aggregate production

3 bronnen1928
econometrics

Random Effects Model

The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Ana

1 bron2021
econometrics

Random Effects Panel Model

The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and

2 bronnen1978
survival

Random Survival Forest

Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank splitting to handle censored observations naturally, and the ensemble aggregates c

1 bron2008
statistics

Randomization Inference

Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validit

2 bronnen1935
statistics

RANSAC Regression

RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins

2 bronnen1981
economics

Real Business Cycle Model

The Real Business Cycle (RBC) model, developed by Finn Kydland and Edward Prescott in 1982, is a dynamic stochastic general equilibrium framework that explains macroeconomic fluctuations as rational responses to exogenous technological shocks. Unlike Keynesian models that emphasize demand-side factors and nominal rigid

3 bronnen1982
finance

Realized Volatility

Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this meas

2 bronnen2009
survival

Recurrent Event Model

A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data when the same event — such as a hospital readmission, disease relapse, or equipme

2 bronnen1981
finance

Regime-Switching Model

The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-A

2 bronnen1989
econometrics

Regression Discontinuity Design

Regression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the design developed for applied practice by Imbens and Lemieux (2008) and by Lee an

3 bronnen2008
machine learning

Regularized LightGBM

Regularized LightGBM applies L1 (lasso) and L2 (ridge) penalty terms to the leaf weight objective of LightGBM — Microsoft's highly efficient gradient boosting framework — to control model complexity, reduce overfitting, and improve generalization on tabular classification and regression tasks with high-dimensional or n

2 bronnen2017
machine learning

Regularized linear regression

Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even whe

2 bronnen1970
machine learning

Regularized Logistic Regression

Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or

2 bronnen1996
machine learning

Regularized Stacking Ensemble

Regularized Stacking Ensemble is a two-level ensemble method in which predictions from multiple diverse base learners are combined by a regularized meta-learner — typically ridge regression, lasso, or elastic net — to suppress overfitting in the combination layer. Regularization ensures that the meta-learner assigns st

2 bronnen1992
research design

Relational Survey

Relational survey research is a quantitative, non-experimental design that gathers structured self-report data from a sample and examines the statistical associations among two or more variables. Unlike purely descriptive surveys, which only characterise distributions, relational surveys ask whether and how strongly va

2 bronnen1960
spatial analysis

Remote Sensing Classification

Remote sensing classification assigns discrete thematic labels — such as forest, urban, water, or cropland — to pixels in a satellite or aerial image based on their spectral, spatial, and temporal properties. It underpins land-use/land-cover mapping, change detection, environmental monitoring, and disaster response at

2 bronnen1970
statistics

Repeated-measures ANOVA

Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (199

2 bronnen1992
epidemiology

Retrospective Case Report

A retrospective case report is a detailed, structured narrative of a single patient's clinical presentation, diagnosis, management, and outcome, assembled from existing medical records after the clinical events have occurred. It is the most granular and accessible observational design in clinical medicine, serving prim

2 bronnen2013
epidemiology

Retrospective competing risks analysis

Retrospective competing risks analysis applies competing risks methodology to historical (already-collected) time-to-event data in which subjects can experience one of several mutually exclusive endpoints. It uses the cumulative incidence function and cause-specific or subdistribution hazard models to estimate the prob

2 bronnen1978
epidemiology

Retrospective Cox proportional hazards

Retrospective Cox proportional hazards regression applies Cox's (1972) semi-parametric survival model to time-to-event data extracted from existing records — medical charts, administrative databases, registries, or biobanks. It estimates covariate-adjusted hazard ratios (HRs) without specifying the underlying baseline

2 bronnen1972
epidemiology

Retrospective Ecological Study

A retrospective ecological study examines associations between exposures and outcomes using pre-existing aggregate data from defined populations or geographic units. Rather than following individual subjects, the unit of analysis is a group — a country, region, or time period — and all measurements come from historical

2 bronnen1980
epidemiology

Retrospective Kaplan-Meier Analysis

Retrospective Kaplan-Meier analysis applies the Kaplan-Meier product-limit estimator to time-to-event data drawn from existing records — medical charts, registries, or administrative databases — rather than from a prospectively followed cohort. The method estimates the probability of surviving (or remaining event-free)

2 bronnen1958
machine learning

Ridge Regression

Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when

1 bron1970
spatial analysis

Ripley K Function

The Ripley K function, introduced by Brian Ripley in 1977, is a second-order summary statistic for spatial point patterns. It measures how the number of points within a given distance d of a typical point compares to what would be expected under complete spatial randomness (CSR). Widely used in ecology, epidemiology, c

1 bron1977
finance

Risk Parity Portfolio

Risk parity is a portfolio weighting model, formalised by Maillard, Roncalli and Teïletche (2010), in which every asset contributes an equal share of the total portfolio risk. It needs only the covariance (risk) structure of the assets and no forecast of expected returns, and it underpins Bridgewater's All Weather stra

2 bronnen2010
forensics

Risk Terrain Modeling

Risk Terrain Modeling (RTM) is a geospatial crime prediction method that identifies high-risk locations by analyzing environmental and geographic features that attract or facilitate crime. Developed by Joel Caplan, Lichen Kennedy, and James Miller in 2011, RTM bridges environmental criminology theory with geographic in

3 bronnen2011
epidemiology

Risk-adjusted competing risks analysis

Risk-adjusted competing risks analysis extends classical survival analysis to settings where subjects can experience more than one type of terminal event, and where the occurrence of one event prevents the occurrence of another. By modelling cause-specific or subdistribution hazards while adjusting for measured confoun

2 bronnen1999
epidemiology

Risk-adjusted Cox Proportional Hazards

Risk-adjusted Cox proportional hazards regression extends the classical Cox (1972) survival model by simultaneously entering known confounders — age, sex, comorbidities, disease severity — into the model alongside the exposure of primary interest. This adjustment isolates the independent effect of the exposure on the h

2 bronnen1972
epidemiology

Risk-adjusted Kaplan-Meier analysis

Risk-adjusted Kaplan-Meier analysis combines the non-parametric Kaplan-Meier estimator with inverse probability of treatment weighting (IPTW) or similar risk-adjustment procedures to produce survival curves that are comparable across groups as if the groups had identical distributions of baseline confounders. It is the

2 bronnen2001
epidemiology

Risk-adjusted survival analysis

Risk-adjusted survival analysis estimates the time to an event of interest — such as death, relapse, or hospital readmission — while simultaneously accounting for baseline differences in patient characteristics (covariates). By incorporating confounders such as age, comorbidities, or disease severity, it produces hazar

2 bronnen1972
quantitative finance

Risk-Neutral Valuation

Risk-neutral valuation (1979) is the fundamental principle that derivative prices equal the expected payoff discounted at the risk-free rate, computed under a risk-neutral probability measure (Q-measure). This principle, formalized by Harrison and Kreps, eliminates the need to estimate risk premia and is the foundation

2 bronnen1979
econometrics

Robust ADF Unit Root Test

The Robust ADF unit root test extends the classical ADF procedure with improvements that correct for size distortions arising from heteroscedastic or serially correlated errors, and from poor lag-length selection. Drawing on GLS detrending (Elliott, Rothenberg, and Stock 1996) and modified information criteria (Ng and

2 bronnen1996
statistics

Robust ANCOVA

Robust ANCOVA is a covariate-adjusted group comparison that replaces classical ANCOVA's ordinary least squares estimation with resistant methods — typically trimmed means or M-estimators — so that the test retains valid Type I error control and reasonable power when data contain outliers, heavy-tailed distributions, or

2 bronnen1990
statistics

Robust ANOVA

Robust ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outl

2 bronnen1951
bayesian

Robust Approximate Bayesian Computation

Robust ABC extends standard Approximate Bayesian Computation to handle outliers, model misspecification, and sensitivity to summary statistic choice. By replacing conventional distance measures with robust alternatives — such as composite scores, trimmed statistics, or synthetic likelihoods — it protects posterior infe

2 bronnen2016
econometrics

Robust AR model

The robust AR model fits an autoregressive time series specification using estimation methods — typically M-estimators or bounded-influence estimators — that resist distortion from outliers and heavy-tailed error distributions. Unlike OLS-based AR estimation, robust variants down-weight extreme observations so that a s

2 bronnen1986
econometrics

Robust ARCH model

The Robust ARCH model extends the classical Autoregressive Conditional Heteroscedasticity framework by replacing the standard maximum-likelihood estimator with robust alternatives that downweight or eliminate the influence of outliers. This makes volatility estimates resistant to extreme observations that frequently co

2 bronnen2002
econometrics

Robust ARDL bounds test

The Robust ARDL bounds test is an augmented version of the Pesaran-Shin-Smith (2001) ARDL bounds testing approach that resolves its two key weaknesses: size distortion under mixed integration orders and the degenerate-case problem. It introduces three separate test statistics — an overall F-test and two new Wald statis

2 bronnen2019
econometrics

Robust Arellano-Bond GMM

The Robust Arellano-Bond GMM estimator applies the Arellano-Bond first-difference GMM approach to dynamic panel data while computing heteroscedasticity- and autocorrelation-consistent (robust) standard errors. This combination handles the Nickell bias from lagged dependent variables and simultaneously yields reliable i

2 bronnen1991
econometrics

Robust ARIMA model

Robust ARIMA extends the classical ARIMA framework to detect and correct the influence of outliers and structural breaks during estimation. By jointly identifying anomalous observations and re-estimating model parameters, it produces coefficient estimates and forecasts that are far less distorted by isolated shocks or

2 bronnen1986
econometrics

Robust ARMA Model

The Robust ARMA model extends the classical Autoregressive Moving Average framework by replacing the sensitive least-squares loss with outlier-resistant estimation methods — typically M-estimators or median-based approaches. This protects coefficient estimates and forecasts from being distorted by additive outliers, le

2 bronnen1986
machine learning

Robust Bagging

Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise dist

2 bronnen1996
bayesian

Robust Bayesian Inference

Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether f

2 bronnen1984
bayesian

Robust Bayesian Model Averaging

Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observation

2 bronnen1999
bayesian

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, inference returns a range of probabilities, honestly reflecting uncertainty abou

2 bronnen1991
statistics

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 distort the estimated canonical correlations and canonical variates between two

2 bronnen2003
statistics

Robust chi-square test

The robust chi-square test extends the classic Pearson chi-square framework to remain reliable when standard assumptions — especially the minimum expected-cell-count rule — are violated. Using power divergence statistics (Cressie & Read, 1984) or resampling-based corrections, it produces valid inferences for sparse con

2 bronnen1984
statistics

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 setting aside a fraction of the most discordant observations, it keeps the recovere

2 bronnen2008
spatial analysis

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-variogram to limit the distorting influence of spatial outliers or non-Gaussian meas

2 bronnen1993
statistics

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 Likert-type, skewed, or kurtotic indicators make the classical normal-theory es

2 bronnen1984
statistics

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 conjoint estimation with robust regression or robust aggregation techniques so tha

2 bronnen1990
statistics

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 Shevlyakov & Oja (2016), it measures how strongly two variables move together witho

2 bronnen2012
statistics

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 biplots and coordinate maps that accurately reflect the dominant association structur

2 bronnen2000
statistics

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 (1999), building on Rousseeuw's earlier work on robust estimation.

2 bronnen1999
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