方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| ARFIMA：分数阶积分自回归滑动平均模型× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 计量经济学 | 研究统计学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 1980 | 1958 |
| 提出者≠ | Granger & Joyeux (1980); Hosking (1981) | David Roxbee Cox |
| 类型≠ | Long-memory time series model | Method |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model | logit model, binomial logistic regression, LR |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|