Fixed-design regression for linear time series
نویسندگان
چکیده
منابع مشابه
Minimax Fixed-Design Linear Regression
We consider a linear regression game in which the covariates are known in advance: at each round, the learner predicts a real-value, the adversary reveals a label, and the learner incurs a squared error loss. The aim is to minimize the regret with respect to linear predictions. For a variety of constraints on the adversary’s labels, we show that the minimax optimal strategy is linear, with a pa...
متن کاملRegression Quantiles for Time Series
In this article we study nonparametric estimation of regression quantiles by inverting a weighted Nadaraya-Watson estimator (WNW) of conditional distribution function, which was rst used by Hall, Woll and Yao (1999). First, under some regularity conditions, we establish the asymptotic normality and weak consistency of the WNW conditional distribution estimator for-mixing time series at both bou...
متن کاملTime Series Forecasting Using Distribution Enhanced Linear Regression
Amongst the wealth of available machine learning algorithms for forecasting time series, linear regression has remained one of the most important and widely used methods, due to its simplicity and interpretability. A disadvantage, however, is that a linear regression model may often have higher error than models that are produced by more sophisticated techniques. In this paper, we investigate t...
متن کاملSemiparametric Regression Smoothing of Non-linear Time Series
In this paper, we consider using a semiparametric regression approach to modelling non-linear autoregressive time series. Based on a ®nite series approximation to non-parametric components, an adaptive selection procedure for the number of summands in the series approximation is proposed. Meanwhile, a large sample study is detailed and a small sample simulation for the Mackey±Glass system is pr...
متن کاملWhich Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?
Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1996
ISSN: 0090-5364
DOI: 10.1214/aos/1032526952