Statistics — Page 26
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Hamiltonian Monte Carlo with Measurement Error
Hamiltonian Monte Carlo (HMC) with measurement error is a Bayesian computational strategy for fitting models where one or more covariates are observed with noise. HMC samples jointly from the posterior over model parameters and the unobserv...
Hamiltonian Monte Carlo with Missing Data
Hamiltonian Monte Carlo with missing data extends the gradient-based HMC sampler to handle incomplete observations by treating missing values as additional unknown parameters. The posterior over model parameters and missing values is sample...
Handicap Quantification (CHART)
Handicap quantification is the approach to measuring long-term rehabilitation outcomes at the level of social roles and participation, implemented in the Craig Handicap Assessment and Reporting Technique developed by Whiteneck and colleague...
Hatemi-J Asymmetric Causality
The Hatemi-J asymmetric causality test, introduced by Abdulnasser Hatemi-J in 2012, extends the Granger causality framework to allow causal relationships between the positive and negative components of integrated time series to differ. By d...
Hatemi-J Cointegration Test
The Hatemi-J cointegration test, introduced by Abdulnasser Hatemi-J in 2008, tests for a long-run equilibrium relationship between integrated time series while allowing for up to two unknown structural breaks in the cointegrating vector. It...
Hausman Test
The Hausman test is a specification test, introduced by Jerry A. Hausman in 1978, that decides between the fixed-effects (FE) and random-effects (RE) estimators in panel data models. The null hypothesis is that the random-effects estimator...
Healthy Life Expectancy
Healthy life expectancy partitions ordinary life expectancy into the years a person can expect to live in good health and the years expected to be lived with disability or ill health. Building on the life table, the classic Sullivan method...
Heatmap and Scrollmap
Heatmaps and scrollmaps are behavioral analytics tools that visually represent user attention and interaction on web pages and screens. Click heatmaps show where users click most frequently, visualized as color-coded density overlays. Scrol...
Heckman Selection Model
The Heckman selection model, introduced by James J. Heckman in 1979, is a two-step model that corrects sample selection bias when the outcome is only observed for a non-random subset of cases. A probit selection equation models who is obser...
Hedonic Pricing
The hedonic pricing model, developed by Sherwin Rosen in 1974 and building on Kevin Lancaster's characteristics theory (1966), is an econometric method for valuing the implicit prices of product attributes by regressing market prices on obs...
Heligman-Pollard Model
The Heligman-Pollard model is an eight-parameter parametric law that describes the age pattern of mortality across the entire human lifespan in a single equation. Introduced by Larry Heligman and John Pollard in 1980, it represents the odds...
Heritage Contingent Valuation
Heritage contingent valuation applies the contingent valuation method (CVM) to cultural and heritage tourism, estimating the monetary value people place on conserving, restoring or accessing historic buildings, monuments, sites and artifact...
Heterogeneous Treatment Effect Coarsened Exact Matching
Heterogeneous treatment effect coarsened exact matching (HTE-CEM) extends the coarsened exact matching framework to estimate how treatment effects vary across subgroups or individual characteristics. After CEM creates balanced strata by coa...
Heterogeneous Treatment Effect Difference-in-Differences
HTE-DiD extends the classic Difference-in-Differences estimator to settings where treatment effects vary across units, time periods, or treatment cohorts. Developed formally by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it av...
Heterogeneous Treatment Effect Entropy Balancing
Heterogeneous Treatment Effect Entropy Balancing combines entropy balancing — a preprocessing step that reweights control units to match the treatment group on covariate moments — with methods that estimate how the treatment effect varies a...
Heterogeneous Treatment Effect Event Study Design
Heterogeneous Treatment Effect Event Study Design is a causal-inference framework that uses event study regression to estimate how treatment effects vary across groups, cohorts, or time relative to a treatment event. Unlike classical two-wa...
Heterogeneous Treatment Effect Fuzzy Regression Discontinuity
Heterogeneous Treatment Effect Fuzzy RDD extends the standard fuzzy regression discontinuity design — where treatment probability, not treatment status itself, jumps at a threshold — by examining whether the Local Average Treatment Effect (...
Heterogeneous Treatment Effect Interrupted Time Series
Heterogeneous Treatment Effect Interrupted Time Series extends the standard ITS design to detect whether an intervention's effect on a time series differs systematically across subgroups or in response to unit-level moderators. Where ordina...
Heterogeneous Treatment Effect Inverse Probability Weighting
HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseu...
Heterogeneous Treatment Effect Marginal Structural Model
The Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations...
Heterogeneous Treatment Effect Matching Estimator
The Heterogeneous Treatment Effect (HTE) Matching Estimator extends standard matching to recover how treatment impacts differ across subgroups or covariate values. Rather than reporting a single average treatment effect, it pairs treated an...
Heterogeneous Treatment Effect Propensity Score Matching
Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matche...
Heterogeneous Treatment Effect Regression Discontinuity Design
Heterogeneous Treatment Effect RDD extends the classic regression discontinuity framework to detect and estimate how the causal effect of crossing an assignment cutoff varies across subgroups or along covariates. Rather than reporting a sin...
Heterogeneous Treatment Effect Sensitivity Analysis for Causality
Heterogeneous Treatment Effect Sensitivity Analysis examines how robust subgroup-specific causal estimates are to unobserved confounding. Rather than testing a single average treatment effect, it asks whether the estimated variation in trea...