ScholarGate

探索

文库我的文库桌面预检 Review Studio 助手

工作区

打造您的书架

保存方法、整理合集，并将它们带到您的桌面。

文库 / 浏览

方法库

按方法、领域与证据探索科学。

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。

6,575 方法11 领域7 方法家族40 语言

科学图谱在使用之前，先绘制科学的结构图。领域 · 方法 · 证据路径探索图谱

领域Health & Medicine716 Psychology570 Business & Finance410 Engineering330 Life Sciences263 Education261 Research Practice248 Natural Sciences236

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。

开放数据（CC-BY）

探索

文库
搜索方法…
按领域浏览
学科领域
历程
对比
该用哪种方法？

参考

学科
图集
术语表
方法论
哲学

工作区

我的文库
桌面
聊天

公司

关于
价格
联系我们
建议新方法

本词条系根据已发表文献整理，仅供参考。核实任何信息的准确性及其是否适用于您的具体用途，仍由您自行负责。

© 2026 ScholarGate · 研究方法参考文库

隐私
Cookie
条款
删除账户

Social Sciences185

Environment & Sustainability160

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

与筛选条件匹配的真实方法。

排序热门程度 A–Z Z–A 最新

experimental design

Split-Plot Design

The split-plot design is a parametric experimental design that applies one factor to large whole plots and a second factor to subdivisions (sub-plots) within each whole plot. It was introduced by Frank Yates in 1935 to handle agricultural experiments where one factor — such as irrigation or tillage method — is difficul

2 个来源1935

Stable Population Theory

Stable Population Theory is a mathematical framework in demography that describes the age structure and growth dynamics of a closed population subject to constant age-specific fertility and mortality schedules over a long period. Foundational work by Alfred J. Lotka established the core integral equation in the early t

1 个来源1972

STAR Model

The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purch

2 个来源1994

State Space Model

A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman

2 个来源1990

research statistics

Statistical Power and Sample Size

Statistical power is the probability of detecting a true effect if it exists (1 − β). Power analysis determines the sample size required to detect a hypothesized effect size with specified Type I error (α) and Type II error (β) rates. Introduced by Jacob Cohen (1988), power analysis is foundational to research design:

3 个来源1988

Stepwise Regression

Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and th

3 个来源1960

STL Decomposition

STL Decomposition, introduced by Cleveland, Cleveland, McRae, and Terpenning (1990), is a nonparametric procedure that separates a time series into three additive components — trend, seasonal, and remainder — using iterative locally weighted regression (loess). Widely used in economics, meteorology, and data science, i

1 个来源1990

Stochastic Volatility Model

The stochastic volatility model is a continuous-time option-pricing and risk framework in which volatility follows its own random process rather than staying constant. The Heston model, introduced by Steven Heston in 1993, gives the variance a mean-reverting square-root (CIR) dynamic and yields a closed-form option pri

2 个来源1993

Stratigraphic Correlation

Stratigraphic correlation is the practice of identifying equivalent rock layers or chronostratigraphic units across space by tracing physical or chemical signatures. Rooted in 19th-century work on Alpine glacial sequences, this method was formalized in the 20th century by geologists like Vail who unified global sea-lev

3 个来源1901

Structural Break ADF Unit Root Test

The structural break ADF unit root test extends the standard Augmented Dickey-Fuller test to allow for one or more discrete shifts in the level or trend of a time series. Because ignoring a structural break inflates the apparent persistence of a series, this test prevents false acceptance of the unit root null when the

2 个来源1989

Structural Break AR Model

The structural break AR model extends the standard autoregressive framework by allowing the intercept and autoregressive coefficients to shift at one or more unknown break dates. Each regime between consecutive break points is governed by its own AR parameters, capturing abrupt changes in the dynamics of a time series

2 个来源1989

Structural Break ARCH Model

The Structural Break ARCH model extends Engle's (1982) Autoregressive Conditional Heteroscedasticity framework by explicitly accounting for abrupt, permanent shifts in the conditional variance process. Ignoring structural breaks in variance causes ARCH parameters to appear spuriously persistent, so incorporating break

2 个来源1982

Structural Break ARDL Bounds Test

The structural break ARDL bounds test extends the Pesaran, Shin and Smith (2001) bounds testing framework to accommodate one or more structural breaks in the long-run relationship between time-series variables. By incorporating break dummies or smooth Fourier terms into the ARDL error-correction equation, it allows res

2 个来源2001

Structural Break ARIMA Model

A structural break ARIMA model extends the standard ARIMA framework by explicitly identifying and accommodating one or more abrupt shifts in the level, trend, or dynamics of a time series. Rather than forcing a single set of ARIMA parameters across the entire sample, it fits separate ARIMA specifications for each regim

2 个来源1989

Structural break DCC-GARCH

Structural break DCC-GARCH extends Engle's Dynamic Conditional Correlation GARCH framework by explicitly allowing the correlation and volatility structure to shift at one or more structural break points in the sample. It models time-varying co-volatility between multiple financial series while accounting for sudden reg

2 个来源2002

Structural Break Difference GMM

Structural Break Difference GMM extends the Arellano-Bond first-difference GMM estimator to dynamic panel settings where the data-generating process shifts at one or more unknown breakpoints. By explicitly incorporating break indicators or allowing regime-specific parameters, the estimator avoids the biased coefficient

2 个来源1991

Structural Break Dynamic Panel Data Model

The structural break dynamic panel data model extends the standard dynamic panel framework by allowing regression coefficients or the autoregressive parameter to shift at one or more unknown break dates. It combines GMM-based dynamic panel estimation with formal structural change tests, enabling researchers to study ho

2 个来源1991

Structural Break EGARCH

Structural Break EGARCH combines Nelson's Exponential GARCH framework with explicit allowance for one or more structural breaks in the volatility process. By letting the intercept and persistence parameters of the log-variance equation shift at detected break dates, the model avoids the spurious long-memory and inflate

2 个来源1990

Structural break Engle-Granger cointegration

The structural break Engle-Granger cointegration test, most commonly implemented via the Gregory-Hansen (1996) procedure, extends the classical Engle-Granger two-step test to allow for a single unknown structural break in the long-run cointegrating relationship. It tests whether two or more integrated series share a co

2 个来源1996

Structural Break Fixed Effects Model

The structural break fixed effects model extends the standard within-group (FE) panel estimator by allowing the slope coefficients to shift at one or more detected break dates. Each unit's unobserved time-invariant heterogeneity is still removed by demeaning, but separate coefficient regimes are estimated for each sub-

2 个来源1998

Structural Break GLS

Structural Break GLS combines Generalized Least Squares estimation with explicit allowance for regime shifts in the data-generating process. The method estimates separate coefficient vectors for each segment defined by detected break dates while correcting for non-spherical errors — heteroscedasticity or autocorrelatio

2 个来源1998

Structural Break Granger Causality

Structural break Granger causality extends the classic Granger causality framework to accommodate regime shifts and parameter instability in time series. By detecting break points and testing causality within sub-samples or via rolling/recursive windows, it reveals whether a predictive relationship between variables sw

2 个来源1995

Structural Break Hausman Test

The Structural Break Hausman Test extends the classical Hausman (1978) specification test to panel or time-series settings where the data-generating process shifts at one or more break points. By detecting structural breaks first and then running the Hausman comparison within each regime, researchers can reliably choos

2 个来源1978

Structural break Johansen cointegration

The structural break Johansen cointegration test extends the standard maximum-likelihood Johansen procedure to settings where the multivariate time series exhibits level shifts or trend breaks. By incorporating dummy variables or shift regressors into the VECM, the test determines the cointegrating rank without confoun

2 个来源2000

Structural Break KPSS Test

The structural break KPSS test extends the standard Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationarity test to allow for one or more known or unknown structural breaks in the level or trend of a time series. Under the null hypothesis the series is stationary around a broken deterministic component, enabling research

2 个来源2002

Structural Break MA Model

A Moving Average (MA) time series model augmented to accommodate one or more structural breaks — abrupt shifts in the mean, variance, or MA coefficients occurring at known or unknown break dates. Ignoring structural breaks in an MA process inflates forecast errors and distorts inference on the error dynamics.

2 个来源1989

Structural Break NARDL

Structural Break NARDL extends the Nonlinear Autoregressive Distributed Lag (NARDL) bounds-testing framework by explicitly accommodating one or more structural breaks in the long-run relationship. It separates positive and negative changes in the regressor, tests for cointegration, and allows regime shifts, providing a

2 个来源2014

Structural Break OLS

Structural Break OLS extends ordinary least squares to allow regression coefficients to shift at one or more breakpoints in time or across regimes. Rather than forcing a single coefficient vector across the entire sample, the model partitions the data and estimates a separate OLS regression within each segment, making

2 个来源1960

Structural Break Panel Data Analysis

Structural break panel data analysis detects and estimates points in time — break dates — where the underlying regression coefficients shift permanently across a panel of cross-sectional units observed over multiple periods. By jointly exploiting cross-sectional and time-series variation, it offers sharper identificati

2 个来源1998

Structural break PP unit root test

The structural break Phillips-Perron (PP) unit root test extends the classical PP framework to allow for one or more discrete shifts in the level or trend of a time series. By endogenously or exogenously identifying break dates and controlling for them, it tests the null of a unit root against a trend-stationary altern

2 个来源1988

Structural Break Quantile-on-Quantile Regression

Structural Break Quantile-on-Quantile Regression (SB-QQR) extends the quantile-on-quantile framework of Sim and Zhou (2015) by allowing regression slopes to differ across regimes separated by structural breaks. It maps how the effect of a predictor's quantile on an outcome's quantile changes not only across the full di

2 个来源2015

Structural Break Random Effects Model

The structural break random effects model extends standard panel RE estimation by allowing one or more breakpoints at which slope coefficients or error variances shift across time. It combines structural change detection (e.g., Bai-Perron) with the GLS-based random effects estimator, producing regime-specific parameter

2 个来源1998

Structural Break SARIMA Model

The Structural Break SARIMA model extends the classical Seasonal ARIMA framework by explicitly detecting and accommodating abrupt, permanent shifts in the level, trend, or seasonal pattern of a time series. Rather than forcing a single SARIMA specification across the entire sample, the model partitions the series at es

2 个来源1970

Structural break SVAR model

The structural break SVAR model extends the standard Structural Vector Autoregression by allowing one or more discrete shifts in the system's parameters across time. It simultaneously identifies causal (structural) shocks and accounts for regime changes — such as policy shifts, crises, or institutional reforms — that a

2 个来源1980

Structural Break System GMM

Structural Break System GMM extends the Blundell-Bond System GMM estimator for dynamic panel data by explicitly accounting for structural breaks — abrupt regime changes in slopes, intercepts, or dynamics — that, if ignored, bias the coefficient estimates and invalidate the moment conditions that underpin standard GMM i

2 个来源1998

Structural Break TGARCH

Structural Break TGARCH extends the Threshold GARCH (GJR-GARCH) model to accommodate discrete, permanent shifts in the volatility process. By detecting structural breaks and incorporating them — either as regime-specific intercepts or dummy variables — the model separates genuine volatility persistence from spurious pe

2 个来源1990

Structural Break Toda-Yamamoto Causality

The structural break Toda-Yamamoto causality test extends the standard Toda-Yamamoto modified Wald (MWALD) procedure to accommodate one or more structural breaks in the time series. By identifying break dates first and then including dummy variables in the augmented VAR, the test maintains its valid asymptotic chi-squa

2 个来源1995

Structural Break VAR Model

The Structural Break VAR model extends the standard Vector Autoregression (VAR) framework by allowing coefficient matrices and error covariance to shift at one or more unknown break dates. It is designed for multivariate time series where economic relationships change abruptly due to policy shifts, financial crises, or

2 个来源1980

Structural break VECM

The Structural Break VECM extends the standard Vector Error Correction Model to allow the cointegrating relationships, adjustment speeds, or short-run dynamics to shift at one or more known or estimated break dates. It preserves the long-run equilibrium framework of the VECM while explicitly modelling regime changes ca

2 个来源1996

Structural Break WLS

Structural Break WLS combines Weighted Least Squares estimation with explicit detection and correction for structural breaks — abrupt regime shifts — in the data. By identifying break points and assigning observation-level weights that account for heteroscedasticity within and across regimes, the estimator delivers con

2 个来源1998

Structural break Zivot-Andrews test

The Zivot-Andrews test is an endogenous structural break unit root test that determines the break point from the data rather than imposing it externally. It tests for a unit root against the alternative of stationarity around a single structural break — in the mean, the trend, or both — choosing the break date that pro

2 个来源1992

research statistics

Structural Equation Modeling

Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measu

3 个来源1921

Structural Time Series Model

The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition w

2 个来源1990

Structural VAR

Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system

2 个来源1980

research statistics

Survival Analysis

Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censor

2 个来源1958

Survival Analysis Power Analysis

Power analysis for survival studies determines how many participants — and how many observed events — are required so that a log-rank test or Cox regression has a sufficient probability of detecting a clinically meaningful difference in survival between groups. The foundational formulas were derived by Schoenfeld (1981

2 个来源1981

Survival Regression

Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the su

2 个来源1980

SVAR

Structural Vector Autoregression (SVAR) is a multivariate time-series model, developed by Christopher Sims (1980), that extends the reduced-form VAR by imposing economically motivated identifying restrictions on contemporaneous relationships among variables. SVAR enables researchers to isolate orthogonal structural sho

1 个来源1980

Synthetic Difference-in-Differences

Synthetic Difference-in-Differences (SDID) combines synthetic control and difference-in-differences approaches to estimate treatment effects when a policy or intervention affects one unit (country, firm) at a point in time. Introduced by Arkhangelsky et al. (2021), it improves upon both methods alone by using weighted

2 个来源2021

System GMM

System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver con

3 个来源1998

Tail Risk Measures

Tail risk measures quantify the loss distribution beyond Value-at-Risk (VaR). Expected Shortfall — the expected loss given that VaR is exceeded — is the leading coherent risk measure, formalised by Artzner, Delbaen, Eber and Heath (1999) and shown to be coherent by Acerbi and Tasche (2002). Spectral and expectile-based

2 个来源1999

TAR / SETAR

TAR and SETAR are nonlinear autoregressive models introduced by Howell Tong (1990) that allow a time series to follow different linear dynamics in distinct regimes, separated by one or more threshold values. SETAR is the self-exciting variant, in which the threshold variable is a lagged value of the series itself, maki

1 个来源1990

Tau Estimator

The Tau estimator is a robust linear regression method introduced by Yohai and Zamar in 1988 that fits the model by minimising an efficient τ-scale of the residuals. It builds on the scale estimate of the S-estimator to combine a high breakdown point with high statistical efficiency, and is often used as an alternative

2 个来源1988

TBATS

TBATS is an innovations state space forecasting model, introduced by De Livera, Hyndman and Snyder (2011), that combines a Box-Cox transformation, ARMA errors and trigonometric (Fourier) seasonal terms. It is built to handle continuous time series with several nested seasonal cycles at once — for example hourly data th

2 个来源2011

computer vision

Template Matching

Template matching is a straightforward technique for locating a known pattern (template) within a larger image. By sliding a template image across the target image and computing a similarity measure at each position, template matching identifies locations where the template appears. It is effective for simple object de

2 个来源1980

Test-Retest Reliability

Test-retest reliability quantifies the temporal consistency of a measure by correlating scores obtained from the same participants on two separate occasions. It is a cornerstone of psychometric validation, directly indicating whether a scale or instrument yields stable scores when the underlying construct has not chang

2 个来源1904

TGARCH model

The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage eff

2 个来源1993

Theil-Sen Estimator

The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%.

2 个来源1968

pharmacometrics

Therapeutic Drug Monitoring

Therapeutic Drug Monitoring (TDM) is a clinical pharmacokinetic practice in which drug concentrations are measured in a patient's blood to guide individualized dosing. It applies principally to drugs with narrow therapeutic windows—where the margin between efficacy and toxicity is small—such as aminoglycosides, vancomy

1 个来源1988

Theta Method

The Theta Method is a univariate time-series forecasting model introduced by Assimakopoulos and Nikolopoulos in 2000. It decomposes a series into two theta lines that capture its long-run trend and its short-run dynamics, forecasts each line separately, and combines them by a weighted average. Its simplicity and accura

2 个来源2000

← 2122 / 2423 →