Vertaile menetelmiä

	ARFIMA: Murskattu integroitu ARMA-malli	Logistinen regressio	Kvanttiiliregressio
Tieteenala	Ekonometria	Tutkimuksen tilastomenetelmät	Ekonometria
Menetelmäperhe	Regression model	Process / pipeline	Regression model
Syntyvuosi	1980	1958	1978
Kehittäjä	Granger & Joyeux (1980); Hosking (1981)	David Roxbee Cox	Koenker & Bassett
Tyyppi	Long-memory time series model	Method	Conditional quantile regression
Alkuperäislähde	Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Rinnakkaisnimet	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
Liittyvät	5	3	5
Tiivistelmä	ARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.