Strongly consistent nonparametric forecasting and regression for stationary ergodic sequences - Mathematics > ProbabilityReportar como inadecuado




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Abstract: Let $\{X i,Y i\}$ be a stationary ergodic time series with $X,Y$ valuesin the product space $\R^d\bigotimes \R .$ This study offers what is believedto be the first strongly consistent with respect to pointwise, least-squares,and uniform distance algorithm for inferring $mx=EY 0|X 0=x$ under thepresumption that $mx$ is uniformly Lipschitz continuous. Auto-regression, orforecasting, is an important special case, and as such our work extends theliterature of nonparametric, nonlinear forecasting by circumventing customarymixing assumptions. The work is motivated by a time series model in stochasticfinance and by perspectives of its contribution to the issues of universal timeseries estimation.



Autor: S. Yakowitz, L. Gyorfi, J. Kieffer, G. Morvai

Fuente: https://arxiv.org/







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