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一个汇集研究方法的目录——了解每种方法如何运作、何时使用以及它做不到什么。

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方法统计学1,836人工智能与机器学习1,661决策科学932研究方法1,354测量1,745因果与证据532研究实践118
1,411 种方法 · 统计学清除
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machine learning

Semi-supervised Bagging

Semi-supervised Bagging extends the classical bagging ensemble to settings where labeled training examples are scarce but large amounts of unlabeled data are available. Base learners trained on labeled data assign pseudo-labels to unlabeled examples; the expanded dataset is then used to grow a diverse ensemble whose ag

2 个来源2000
machine learning

Semi-supervised Logistic Regression

Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when

2 个来源1995
research statistics

Sensitivity and Specificity

Sensitivity and specificity are fundamental metrics of diagnostic test accuracy. Sensitivity is the probability that a test correctly identifies a person with the disease (true positive rate: TP / (TP + FN)). Specificity is the probability that a test correctly identifies a person without the disease (true negative rat

3 个来源1978
statistics

Sequential Analysis

Sequential analysis is a framework for conducting hypothesis tests with pre-planned interim looks at accumulating data, allowing a study to stop early for efficacy or futility while controlling the overall Type I error rate. The group sequential approach was formalised by Pocock (1977) and O'Brien and Fleming (1979), a

2 个来源1977
bayesian

Sequential Monte Carlo

Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-t

2 个来源1993
bayesian

Sequential Monte Carlo with Measurement Error

Sequential Monte Carlo (SMC) with measurement error is a particle-based Bayesian filtering method for tracking hidden states in dynamical systems when observations are corrupted by noise. It propagates a weighted cloud of particles through time, updating weights at each step to reflect how well each particle explains t

2 个来源1993
bayesian

Sequential Monte Carlo with Missing Data

Sequential Monte Carlo (SMC) with missing data extends the standard particle filter to state-space models in which some observations are absent. When an observation is missing at a given time step the update step is simply skipped: particles are propagated forward through the transition model without reweighting, prese

2 个来源1993
spatial analysis

Service Area Analysis

Service Area Analysis delineates the geographic region reachable from one or more origin facilities within a specified travel cost — typically time, distance, or generalized impedance — by traversing a real road or transit network. It is widely used by urban planners, public health officials, logistics managers, and em

1 个来源2001
statistics

Shapiro-Wilk test

The Shapiro-Wilk test is a hypothesis test that checks whether a continuous variable was drawn from a normal distribution. It was introduced by Samuel Shapiro and Martin Wilk in 1965 and is regarded as one of the most powerful normality tests, recommended for sample sizes below 5000.

1 个来源1965
statistics

Shewhart Control Chart

The Shewhart control chart, invented by Walter Shewhart at Bell Labs in the 1920s and set out in his 1931 book, is the foundational tool of statistical process control. It plots a process statistic — typically the subgroup mean (X-bar) and range (R) — over time against a center line and three-sigma control limits, dist

2 个来源1931
psychometrics

Short-Form CFA

Short-form confirmatory factor analysis applies CFA to a reduced subset of items drawn from a longer validated scale, testing whether the abbreviated version preserves the original factor structure with acceptable model fit and reliability. It is a standard step in short-form scale development and validation.

2 个来源1990
psychometrics

Short-form Cronbach's alpha

Short-form Cronbach's alpha quantifies the internal consistency reliability of an abbreviated version of a psychological scale. It applies Cronbach's alpha formula to a reduced item set, verifying that the shortened instrument retains sufficient reliability to support valid score interpretation in research and applied

2 个来源1951
ecology

SIAR Mixing Model

The Stable Isotope Analysis in R (SIAR) mixing model is a Bayesian framework for estimating the proportional contributions of dietary sources to a consumer, using stable isotope ratios. Developed by Parnell and colleagues (2010) and implemented in the R package siar (and its successor MixSIAR), this method integrates i

3 个来源2010
statistics

Siegel-Tukey test

The Siegel-Tukey test is a nonparametric hypothesis test that detects differences in variability (spread) between two independent groups whose central tendencies are equal or have been equalised. Introduced by Sidney Siegel and John W. Tukey in 1960, it is the nonparametric counterpart of Levene's test and requires no

1 个来源1960
statistics

Sign Test

The sign test is the simplest nonparametric hypothesis test for deciding whether the median of paired differences — or of a single sample — differs significantly from a hypothesised value. Formalised by W. J. Dixon and A. M. Mood in 1946, it imposes virtually no distributional assumptions and can be applied to any data

1 个来源1946
statistics

Simple Linear Regression

Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and

3 个来源introductory1805
statistics

Simulation-Based Power Analysis

Simulation-based power analysis estimates the statistical power and required sample size of a study by repeating a full analysis pipeline thousands of times on artificially generated data. Because it relies on Monte Carlo simulation rather than closed-form equations, it is applicable to designs — mixed models, complex

2 个来源2011
experimental design

Single-blind A/B test

A single-blind A/B test is a controlled two-condition experiment in which participants are randomised to condition A (control) or condition B (treatment) but are kept unaware of which condition they have received, while researchers and analysts remain aware. The blind prevents participants from changing their behaviour

2 个来源1990
time series

Singular Spectrum Analysis

Singular Spectrum Analysis (SSA) is a nonparametric method for time-series decomposition and forecasting based on singular value decomposition (SVD) of a time-lagged embedding matrix. Introduced by Broomhead and King (1986) and developed further by Vautard, Yiou, and Ghil (1992), SSA decomposes time series into trend,

3 个来源1986
bayesian

Slice Sampling

Slice sampling is a Markov chain Monte Carlo (MCMC) algorithm introduced by Radford M. Neal in his 2003 Annals of Statistics paper. It generates samples from a target distribution by drawing uniformly from the region under the density curve — called the 'slice' — without requiring the user to specify a step-size or pro

3 个来源2003
economics

Slutsky Equation

The Slutsky equation, derived by Russian economist Eugen Slutsky in 1915, is a fundamental identity in microeconomics that decomposes the total change in demand for a good into two effects: the substitution effect and the income effect. Formalizing John Hicks' later interpretation, it provides the mathematical foundati

3 个来源1915
statistics

Sn and Qn Scale Estimators

Sn and Qn are robust estimators of scale (spread) proposed by Rousseeuw and Croux (1993) as alternatives to the median absolute deviation (MAD). Both attain a 50% breakdown point while delivering higher statistical efficiency than MAD, so they measure dispersion accurately even when the data contain outliers.

1 个来源1993
statistics

Somers' D

Somers' D is an asymmetric ordinal association coefficient, introduced by Robert H. Somers in 1962, that quantifies how well one ordinal variable predicts another by measuring the excess of concordant over discordant pairs relative to all pairs that are not tied on the designated independent variable. It is the standar

3 个来源1962
spatial analysis

Space-Time Geary's C

Space-Time Geary's C extends the classical Geary contiguity ratio to panel or longitudinal spatial data, measuring autocorrelation across both geographic neighbors and adjacent time periods simultaneously. Values below 1 indicate positive space-time clustering; values above 1 indicate dispersion, and a value near 1 sug

2 个来源1954
spatial analysis

Space-Time Getis-Ord Gi*

The Space-Time Getis-Ord Gi* statistic extends the classic Gi* local hot spot measure into three dimensions — two spatial and one temporal — revealing not only where concentrations of high or low values cluster, but how those clusters evolve, intensify, or diminish over time. It is widely used in crime analysis, epidem

2 个来源1992
spatial analysis

Space-Time Hot Spot Analysis

Space-Time Hot Spot Analysis extends the classic Getis-Ord Gi* statistic across repeated time slices organised in a space-time cube. By testing each location-time bin for statistically significant clustering of high or low values, then examining the sequence of results over time, it identifies whether clusters are new,

2 个来源1997
spatial analysis

Space-Time Kernel Density Estimation

Space-Time Kernel Density Estimation extends classical KDE into three dimensions — two spatial and one temporal — to reveal how the intensity of point events (crimes, accidents, disease cases) varies continuously across both geographic space and time. It produces a smooth probabilistic surface that highlights where and

2 个来源2010
spatial analysis

Space-Time Kriging

Space-Time Kriging is a geostatistical interpolation method that predicts an unknown variable at any location and time by borrowing strength from nearby observations in both space and time simultaneously. It models the joint spatial-temporal covariance structure through a space-time variogram, then uses optimal linear

2 个来源1999
spatial analysis

Space-Time Local Indicators of Spatial Association

Space-Time Local Indicators of Spatial Association (ST-LISA) extend the classic LISA framework of Anselin (1995) into the temporal dimension, identifying locations that exhibit statistically significant spatial clustering or spatial outlier behavior consistently or intermittently across multiple time periods. They deco

2 个来源1995
spatial analysis

Space-Time Moran's I

Space-Time Moran's I extends the classic Moran's I statistic into the spatiotemporal domain, measuring whether observations that are close in both space and time tend to be more similar than those that are distant. It detects clustering, dispersion, or randomness across a combined space-time weight matrix, making it a

2 个来源1981
spatial analysis

Space-Time Network-Based Spatial Analysis

Space-Time Network-Based Spatial Analysis integrates network topology with temporal constraints to model how people, goods, or phenomena move through geographic networks over time. Rooted in Hägerstrand's time-geography, it evaluates accessibility, interaction potential, and movement patterns along real-world infrastru

2 个来源1970
spatial analysis

Space-Time Ordinary Kriging

Space-Time Ordinary Kriging (STOK) is a geostatistical interpolation method that predicts a spatially and temporally varying phenomenon at unsampled space-time locations by combining the ordinary kriging assumption of an unknown, locally constant mean with a joint space-time covariance (or variogram) structure. It prod

2 个来源1999
spatial analysis

Space-Time Remote Sensing Classification

Space-Time Remote Sensing Classification extends standard image classification to multi-temporal satellite or aerial imagery, enabling analysts to track land cover change, phenological cycles, and environmental dynamics across both space and time. By incorporating the temporal dimension, classifiers achieve higher accu

2 个来源1980
spatial analysis

Space-Time Spatial Autocorrelation

Space-Time Spatial Autocorrelation extends classic spatial autocorrelation measures — most notably Moran's I — to data that vary across both geographic units and time periods. It detects whether nearby locations that are also temporally close tend to share similar attribute values, revealing clusters, trends, or anomal

2 个来源1981
spatial analysis

Space-Time Spatial Durbin Model

The Space-Time Spatial Durbin Model extends the cross-sectional Spatial Durbin Model to panel data, simultaneously capturing spatial spillovers in both the dependent variable and the explanatory variables across space and over time. It is the most general and flexible specification in the spatial panel family, nesting

2 个来源2009
spatial analysis

Space-Time Spatial Error Model

The Space-Time Spatial Error Model (space-time SEM) is a spatial panel regression technique that accounts for spatial dependence confined to the error term across geographic units and time periods. It corrects biased inference caused by spatially correlated disturbances while estimating covariate effects on a panel of

2 个来源1988
spatial analysis

Space-Time Spatial Lag Model

The Space-Time Spatial Lag Model extends the classic spatial autoregressive (SAR) lag model to panel data, capturing how the outcome in each location at each time point is influenced by the contemporaneous outcomes of neighboring locations, while also controlling for unit-specific and time-specific fixed effects.

2 个来源2003
spatial analysis

Space-Time Spatial Panel Model

The Space-Time Spatial Panel Model extends standard spatial panel econometrics to jointly account for cross-sectional spatial dependence, temporal autocorrelation, and unit-level heterogeneity. It allows outcomes in one location and time period to be influenced by outcomes in neighboring locations and by the location's

2 个来源2003
spatial analysis

Space-Time Spatial Regression

Space-Time Spatial Regression extends classical spatial regression to panel settings where georeferenced units are observed across multiple time periods. By embedding a spatial weights matrix into a panel regression framework, it simultaneously controls for spatial dependence among cross-sectional units and temporal dy

2 个来源1990
spatial analysis

Space-Time Universal Kriging

Space-Time Universal Kriging (STUK) is a geostatistical method that interpolates a continuously varying phenomenon across both space and time while explicitly modelling a deterministic trend component. It generalises Universal Kriging to the joint space-time domain, producing unbiased optimal predictions and associated

2 个来源1999
bayesian

Spatial Approximate Bayesian Computation

Spatial Approximate Bayesian Computation (Spatial ABC) is a likelihood-free Bayesian inference framework for spatial data models whose likelihood function is intractable or too expensive to evaluate. It draws candidate parameters from a prior, simulates spatially structured datasets under those parameters, and accepts

2 个来源2002
spatial analysis

Spatial Autocorrelation

Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal

2 个来源1950
bayesian

Spatial Bayesian Inference

Spatial Bayesian inference applies Bayesian hierarchical modeling to data indexed by geographic location. By placing structured spatial priors on location-specific random effects, the model borrows information from neighboring regions or nearby points, producing smooth, uncertainty-quantified maps of any spatially vary

2 个来源1991
bayesian

Spatial Bayesian Model Averaging

Spatial Bayesian model averaging (spatial BMA) extends classical BMA to settings where observations are georeferenced and spatial dependence must be modelled. Rather than selecting a single spatial regression model — which spatial weight matrix to use, which regressors to include, which spatial lag or error structure t

2 个来源2008
bayesian

Spatial Bootstrap Simulation

Spatial bootstrap simulation is a resampling technique designed for spatially dependent data. By resampling contiguous spatial blocks rather than independent observations, it preserves the local autocorrelation structure of the data and yields valid estimates of sampling variability for statistics computed on geographi

2 个来源1990
causal inference

Spatial Difference-in-Differences

Spatial Difference-in-Differences (Spatial DiD) extends the classical DiD estimator to settings where observations are geo-referenced and outcomes may be spatially autocorrelated or subject to spillover effects. Introduced by Delgado and Florax (2015), the method augments the standard two-way fixed-effects DiD regressi

1 个来源2015
spatial analysis

Spatial Durbin Model

The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special

2 个来源2009
spatial analysis

Spatial Error Model

The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move tog

1 个来源1988
bayesian

Spatial Gibbs Sampling

Spatial Gibbs sampling applies the Gibbs sampler — a coordinate-wise Markov chain Monte Carlo algorithm — to models where observations are arranged in space and nearby locations are statistically dependent. By exploiting the conditional independence implied by a spatial neighbourhood structure, each site is updated one

2 个来源1984
spatial analysis

Spatial Interaction Model

Spatial interaction models predict the volume of flows — migrants, commuters, shoppers, trade, trips — between origins and destinations as a function of the size of each place and the distance or cost separating them. By analogy to Newton's gravity, interaction rises with the 'mass' of origin and destination and falls

2 个来源1971
bayesian

Spatial Kalman Filter

The spatial Kalman filter applies classical Kalman filtering to spatio-temporal state-space models, treating a spatially distributed latent field as the hidden state that evolves over time. At each time step, the filter recursively predicts the spatial field forward and then updates the prediction with new spatial obse

2 个来源1960
spatial analysis

Spatial Lag Model

The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and i

2 个来源1988
bayesian

Spatial MCMC

Spatial MCMC applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for spatial dependence among observations. It draws posterior samples from models such as conditional autoregressive (CAR), simultaneous autoregressive (SAR), or geostatistical (Gaussian process) models, yielding full unce

2 个来源1990
bayesian

Spatial Monte Carlo Simulation

Spatial Monte Carlo simulation applies random sampling methods to spatial problems, generating many stochastic realisations of a spatial process — such as a random field, point pattern, or network — to estimate distributional properties, propagate uncertainty, or test spatial hypotheses. It is a cornerstone technique i

2 个来源1970
spatial analysis

Spatial Panel Model

The spatial panel model is a family of econometric models that adds spatial dependence to panel data (units observed over time). It combines fixed- or random-effects panel structure with spatial lag, spatial error, or spatial Durbin components, and is developed in the modern spatial-econometrics literature by Elhorst (

2 个来源2014
causal inference

Spatial Propensity Score Weighting

Spatial propensity score weighting extends inverse probability of treatment weighting (IPTW) to settings where units are geographically located and treatment assignment may depend on spatial factors such as location, neighborhood characteristics, or spatial clustering. By incorporating spatial covariates into the prope

2 个来源2000
econometrics

Spatial Regression

Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model,

2 个来源1988
spatial analysis

Spatial SAC Model

The Spatial Autoregressive Combined (SAC) model, also known as the SARAR model, simultaneously accounts for spatial dependence in both the dependent variable and the error term. Formalized by LeSage and Pace (2009), the SAC model combines the spatial lag model and the spatial error model into a single framework, estima

1 个来源2009
bayesian

Spatial Variational Inference

Spatial variational inference is a scalable approximate Bayesian method that fits latent Gaussian or Gaussian-process models to georeferenced data by optimising a lower bound on the marginal likelihood. It replaces expensive MCMC sampling with a deterministic optimisation step, making full-posterior uncertainty quantif

2 个来源2009
statistics

Spearman Correlation

The Spearman rank correlation coefficient (ρ) is a nonparametric measure of the monotonic association between two variables. Introduced by Charles Spearman in 1904, it converts raw observations to ranks and measures how consistently one variable increases as the other increases, without assuming a normal distribution o

1 个来源1904
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