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时间依赖下高维回归模型的变点推断AI 解读

This paper concerns the limiting distributions of change-point estimators, in a high-dimensional linear regression time-series context, where a regression object (yt,Xt)∈R×Rp is observed at every time point t∈{1,…,n}. At unknown time points, called change points, the regression coefficients change, with the jump sizes measured in ℓ2-norm. We provide limiting distributions of the change-point estimators in the regimes where the minimal jump size vanishes and where it remains…

时间序列模型的稳健两步小波推断AI 解读

) models with additional stochastic processes are increasingly used for data analysis in biology, ecology, engineering, and economics. Inference on and/or prediction from these models can be highly challenging: (i) the data may contain outliers that can adversely affect the estimation procedure; (ii) the computational complexity can become prohibitive when the time series are extremely large; (iii) model selection adds another layer of (computational) complexity; and (iv)…