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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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以内容为本的研究方法参考文库——每种方法是什么、如何运作、源自何处。

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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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psychometrics

Longitudinal EFA

Longitudinal EFA applies exploratory factor analysis separately at each measurement occasion — or jointly across occasions — to discover whether the same latent factor structure emerges over time and whether factor loadings remain stable across waves. It is the foundational data-driven approach for examining structural

2 个来源1970
research design

Longitudinal relational survey

A longitudinal relational survey follows the same sample at two or more time points, collecting structured questionnaire data each wave and examining how the relationships among variables change, strengthen, weaken, or emerge across time. Unlike a cross-sectional relational survey that offers a single snapshot, this de

2 个来源1960
actuarial science

Loss Distribution Model

A Loss Distribution Model is a parametric statistical framework used in actuarial science to characterise the probabilistic behaviour of insurance claim amounts and frequencies. Developed comprehensively by Klugman, Panjer, and Willmot in their foundational text Loss Models: From Data to Decisions (first edition 1998,

1 个来源2012
econometrics

LSDVC

LSDVC is a bias-corrected panel data estimator introduced by Kiviet (1995) to address the well-known Nickell bias that afflicts the standard Least Squares Dummy Variable (LSDV) estimator in dynamic panel models with a lagged dependent variable. It is particularly suited for researchers working with datasets where the n

1 个来源1995
econometrics

Lumsdaine-Papell Test

The Lumsdaine-Papell test, introduced by Robin Lumsdaine and David Papell in 1997, extends the Zivot-Andrews single-break unit-root test to allow for two simultaneous structural breaks in the intercept and/or linear trend of a time series. It is widely used in macroeconomics and finance when data are suspected to have

1 个来源1997
statistics

M-Estimator

M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.

2 个来源2009
statistics

MAD Estimation

Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme va

2 个来源1974
econometrics

Maki Cointegration Test

The Maki cointegration test extends cointegration testing to allow for an unknown number of endogenously-determined structural breaks in the cointegrating relationship. Introduced by Maki (2012), it builds on Gregory and Hansen (1996), enabling detection of cointegration even when relationships shift due to policy chan

2 个来源2012
statistics

Mann-Whitney U test

The Mann-Whitney U test is the nonparametric alternative to the independent samples t-test, comparing two independent groups by ranking all observations together rather than relying on their means. It was introduced by H. B. Mann and D. R. Whitney in 1947 and does not require the data to be normally distributed.

2 个来源1947
statistics

MANOVA

MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Gr

2 个来源1932
spatial analysis

Map Algebra

Map Algebra is a rule-based language and computational framework for deriving new raster layers from existing ones by applying arithmetic, logical, or statistical operations cell by cell or across neighborhoods. Formalized by Dana Tomlin in 1990, it is the foundational algebraic system underlying raster GIS analysis an

1 个来源1990
finance

Market Microstructure Analysis

Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014).

2 个来源2007
econometrics

Markov-Switching Model

The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.

2 个来源1989
time series

Markov-Switching Multifractal

The Markov-Switching Multifractal (MSM) model is a flexible framework for capturing time-varying volatility and long-memory effects in financial time series. Developed by Calvet and Fisher (2004), it combines Markov chain theory with multifractal scaling principles to generate volatility that exhibits multiple frequenc

3 个来源2004
epidemiology

Matched Competing Risks Analysis

Matched competing risks analysis combines subject-level matching (e.g., propensity-score matching) with competing risks survival methods to estimate the cause-specific or subdistribution hazard of an event of interest while accounting for competing events that preclude the occurrence of that event. It is widely used in

2 个来源1999
epidemiology

Matched Cox Proportional Hazards

Matched Cox proportional hazards is a survival analysis method that extends the Cox regression model to appropriately handle data arising from matched study designs — matched cohorts or matched case-control studies with time-to-event outcomes. By stratifying the partial likelihood by matched set, the method eliminates

2 个来源1972
signal processing

Matched Filter

The matched filter is an optimal signal detector that maximizes the signal-to-noise ratio (SNR) for detecting a known signal in additive Gaussian noise. Developed by D. O. North during World War II for radar applications, the matched filter represents the optimal linear filter for signal detection and remains the found

2 个来源1943
epidemiology

Matched Kaplan-Meier Analysis

Matched Kaplan-Meier analysis estimates and compares survival functions in groups that have been pre-balanced through individual or propensity-score matching. By applying the Kaplan-Meier product-limit estimator to matched cohorts or matched pairs, investigators can visualize time-to-event outcomes while controlling fo

2 个来源1958
epidemiology

Matched Survival Analysis

Matched survival analysis combines a matching design — typically propensity score matching or exact matching on key covariates — with time-to-event methods such as Kaplan-Meier estimation and the Cox proportional hazards model. By pairing treated and control subjects who are similar on observed confounders before estim

2 个来源1983
model evaluation

Matthews Correlation Coefficient

The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets.

2 个来源1975
statistics

Maximum Likelihood Estimation

Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society,

2 个来源1922
bayesian

MCMC

Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Marko

2 个来源
bayesian

MCMC for Model Comparison

MCMC for model comparison uses Markov chain Monte Carlo algorithms to estimate the marginal likelihoods and Bayes factors needed to formally compare competing statistical models. Techniques such as reversible-jump MCMC and bridge sampling allow exploration across model spaces of different dimensionality, enabling fully

2 个来源1995
bayesian

MCMC with Measurement Error

MCMC with measurement error applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for the fact that covariates or outcomes are observed with error. By treating the true, unobserved values as latent variables and sampling their joint posterior alongside all other parameters, the method cor

2 个来源1993
bayesian

MCMC with missing data

MCMC with missing data is a Bayesian computational strategy that treats unobserved values as additional unknown parameters. By alternating between sampling the missing values from their predictive distribution and sampling the model parameters from their posterior, the algorithm produces a valid joint posterior that fu

2 个来源1987
statistics

McNemar's test

McNemar's test is a nonparametric hypothesis test that compares two paired (correlated) binary proportions, such as a yes/no measurement taken on the same subjects before and after an intervention. It was introduced by Quinn McNemar in 1947 and works on the 2×2 table of matched outcomes.

1 个来源1947
statistics

MCUSUM Chart

The Multivariate CUSUM (MCUSUM) Chart is a sequential monitoring scheme designed to detect small, persistent mean shifts in a process characterized by multiple correlated quality variables simultaneously. Introduced by Robert Crosier in 1988, it extends the classical univariate CUSUM principle to the multivariate setti

1 个来源1988
finance

Mean-Variance Portfolio Optimization

Mean-variance portfolio optimization is the foundational model of modern portfolio theory, introduced by Harry Markowitz in 1952. It describes portfolios in an expected-return versus risk (variance) plane and traces the efficient frontier of allocations that offer the highest expected return for each level of risk, cov

2 个来源1952
statistics

Mediation Analysis

Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path

3 个来源1986
quantitative finance

Merton Default Model

The Merton model (1974) is a structural approach to credit risk in which a firm defaults when its asset value falls below liabilities at maturity. Equity is viewed as a call option on firm value, and debt is an implicit short put position. The model links company fundamentals (asset volatility) to default probability a

2 个来源1974
epidemiology

Meta-analytic competing risks analysis

Meta-analytic competing risks analysis pools results from multiple primary studies that each used a competing risks framework, allowing summary estimates of cause-specific or subdistribution hazard ratios and cumulative incidence functions. Because standard meta-analytic methods may misrepresent competing events, speci

2 个来源2000
epidemiology

Meta-analytic Cox proportional hazards

Meta-analytic Cox proportional hazards is a quantitative synthesis technique that pools log hazard ratios from multiple Cox regression survival analyses into a single, more precise estimate of the association between an exposure or treatment and a time-to-event outcome. It combines the inferential power of survival ana

2 个来源1998
epidemiology

Meta-analytic Kaplan-Meier analysis

Meta-analytic Kaplan-Meier analysis synthesizes time-to-event data across multiple studies by pooling Kaplan-Meier survival estimates, either from reconstructed individual patient data or from summary statistics extracted from published curves. It produces a pooled survival function with confidence bands and enables fo

2 个来源2007
epidemiology

Meta-analytic Phase I clinical trial

A meta-analytic Phase I clinical trial formally pools evidence from prior Phase I studies — using Bayesian or frequentist meta-analysis — to construct an informative prior (or summary estimate) for dose-toxicity relationships before or during a new first-in-human or early-phase study. The approach increases statistical

2 个来源2000
econometrics

Method of Moments Quantile Regression

Method of Moments Quantile Regression combines moment-based estimation (GMM) with quantile regression to estimate distribution parameters while handling endogeneity, panel structure, and dynamic relationships. Introduced by Koenker (2004) and developed by Machado and Mata (2005), it enables distributional analysis (not

2 个来源2004
bayesian

Metropolis-Hastings Algorithm

The Metropolis-Hastings (MH) algorithm is a general-purpose Markov chain Monte Carlo (MCMC) method for drawing samples from any probability distribution whose density can be evaluated up to a normalising constant. Introduced by Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) in computational physics and g

4 个来源1953
bayesian

Metropolis-Hastings for model comparison

Metropolis-Hastings for model comparison uses the Metropolis-Hastings MCMC algorithm to explore both parameter and model space simultaneously, producing posterior probabilities for competing models and enabling Bayes factor estimation without requiring closed-form marginal likelihoods. The canonical extension — reversi

2 个来源1970
bayesian

Metropolis-Hastings with measurement error

Metropolis-Hastings with measurement error is a Bayesian MCMC approach that jointly estimates model parameters and the true (unobserved) covariate values when predictors or outcomes are recorded with noise. By treating the latent true values as unknown parameters, it propagates measurement uncertainty fully into poster

2 个来源1953
bayesian

Metropolis-Hastings with Missing Data

Metropolis-Hastings with missing data treats unobserved values as latent variables and samples them jointly with model parameters inside a single MCMC chain. By augmenting the target distribution to include both parameters and missing values, the algorithm yields properly calibrated posterior inference without discardi

2 个来源1953
statistics

MEWMA Chart

The Multivariate EWMA (MEWMA) control chart is a statistical process monitoring method designed to detect small and sustained shifts in the mean vector of a multivariate process. Introduced by Lowry, Woodall, Champ, and Rigdon in 1992, it extends the univariate EWMA chart to p-dimensional observation vectors by computi

1 个来源1992
spatial analysis

MGWR

Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can

2 个来源2017
statistics

MICE

Multivariate Imputation by Chained Equations (MICE) is an iterative procedure for handling missing data in multivariate datasets. Introduced by Stef van Buuren and Karin Groothuis-Oudshoorn through the R package mice (2011), the algorithm fills each missing variable using a separate regression model conditioned on all

1 个来源2011
econometrics

MIDAS Regression

MIDAS (Mixed Data Sampling) Regression is an econometric framework that directly incorporates high-frequency predictors into models for lower-frequency outcome variables without requiring temporal aggregation of the regressors. Introduced by Eric Ghysels, Arthur Sinko, and Rossen Valkanov in 2007, MIDAS uses parsimonio

1 个来源2007
demography

Migration Models

Migration models are quantitative frameworks for explaining and forecasting population movement between geographic units. Lee's (1966) push-pull theory classifies factors at origin and destination into positive and negative forces, modulated by intervening obstacles. Widely used by demographers, regional planners, and

1 个来源1966
rehabilitation

Mini-BESTest Balance Evaluation

The Mini-Balance Evaluation Systems Test (Mini-BESTest) is a brief performance-based measure of balance impairment designed to identify the underlying sensory, motor, and cognitive contributions to balance deficits. Developed by Franchignoni and colleagues in 2010 as a shortened version of the comprehensive BESTest, Mi

2 个来源2009
statistics

Missing Data Mechanisms

Missing data mechanisms, introduced by Donald Rubin in 1976, provide a formal taxonomy for classifying why observations are absent from a dataset. The three categories — Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) — describe the relationship between the probability of

1 个来源1976
statistics

Mixed ANOVA

Mixed ANOVA is a parametric factorial analysis of variance that simultaneously examines at least one between-subjects factor and at least one within-subjects (repeated-measures) factor. Rooted in R. A. Fisher's ANOVA framework formalised in 1925, it is the standard method for experimental and longitudinal designs in wh

1 个来源1925
statistics

Mixed Effects Model

A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel

2 个来源1982
econometrics

Mixed Logit

The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes

2 个来源2000
survival

Mixture Cure Model

The mixture cure model, first proposed by Boag in 1949 for cancer survival data, is a parametric survival model that explicitly accounts for a fraction of subjects who will never experience the event of interest — the so-called cured or immune fraction. It is the appropriate tool whenever the Kaplan-Meier curve levels

1 个来源1949
statistics

Mixture Modeling

Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and suppo

2 个来源1894
statistics

MM-Estimator

The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.

2 个来源1987
econometrics

Model Confidence Set

The Model Confidence Set (MCS) is a sequential hypothesis-testing procedure introduced by Hansen, Lunde, and Nason (2011) that identifies the smallest collection of forecasting or predictive models statistically indistinguishable from the best-performing model at a given confidence level. Instead of selecting a single

1 个来源2011
research design

Model Testing Research

Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (C

2 个来源1970
statistics

Moderated Mediation

Moderated mediation tests whether the indirect effect of an independent variable on an outcome — transmitted through a mediator — differs in strength depending on the level of a moderator variable. It answers the question: for whom, or under what conditions, does the mediated pathway operate most strongly?

2 个来源2007
bayesian

Monte Carlo Simulation with Missing Data

Monte Carlo simulation with missing data combines stochastic simulation — drawing random values from probability distributions — with principled missing-data strategies such as multiple imputation. Instead of discarding incomplete records or substituting a single fill-in value, the method generates many simulated compl

2 个来源1987
rehabilitation

Montreal Cognitive Assessment

The Montreal Cognitive Assessment (MoCA) is a brief 10-minute cognitive screening test designed to detect mild cognitive impairment (MCI) in older adults. Developed by Nasreddine and colleagues in 2005 at McGill University, MoCA is more sensitive to cognitive impairment than the Mini-Cog or MMSE, particularly for detec

2 个来源2005
statistics

Mood's Median Test

Mood's median test is a nonparametric procedure that compares the medians of k independent groups by counting how many observations in each group fall above and below the pooled (grand) median, then applying a chi-square test to the resulting 2×k contingency table. It traces to A. M. Mood's 1954 work on nonparametric t

2 个来源1954
spatial analysis

Moran's I

Moran's I is the standard global statistic for detecting spatial autocorrelation: whether nearby locations tend to share similar values. The index ranges from approximately −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering), allowing researchers to test whether a geographic pattern differ

2 个来源1950
spatial analysis

Moran's I

Moran's I is a global statistic, introduced by Patrick Moran in 1950, that measures whether and how a continuous variable is spatially autocorrelated across mapped units. A positive value signals clustering of similar values, a negative value signals a dispersed (checkerboard) pattern, and it is most often used as a di

2 个来源1950
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