Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| ARFIMA: Daļēji integrēts ARMA modelis× | Logistiskā regresija× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Pētniecības statistika |
| Saime≠ | Regression model | Process / pipeline |
| Izcelsmes gads≠ | 1980 | 1958 |
| Autors≠ | Granger & Joyeux (1980); Hosking (1981) | David Roxbee Cox |
| Tips≠ | Long-memory time series model | Method |
| Pirmavots≠ | 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. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Citi nosaukumi≠ | fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model | logit model, binomial logistic regression, LR |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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. |
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