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    CONVERGENCE RATES OF ERROR VARIANCE ESTIMATES UNDER φ-MIXING ERROR IN LINEAR MODELS
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    Abstract:
    In this paper,we study the convergence rates of error variance estimates under (?)-mixingerror in linear models.Under the weak confinement for mixing rates,our results are consis-tent with corresponding results in the independent case.
    Keywords:
    Error Analysis
    We derive lower bounds to rates of convergence for estimators of shape and scale parameters in distributions with regularly varying tails. We exhibit simple estimators which attain these rates.
    Variation (astronomy)
    Citations (101)
    This note extends some results on homogeneous linear estimators to the general, even nonlinear case.A Sufficient condition for the difference of mean square error matrices of minimum conditional mean square error estimator and minimum average risk linear estimator to be postive definite is derived.
    Conditional expectation
    Mean square
    Citations (29)
    In this paper, we are mainly concerned with uniform convergence and mean square convergence of the error-density estimator of nonparametric regression in the i.i.d case. We have given the strong uniform convergence rate and mean square error convergence rate of the kernel density estimator fn under suitable conditions.
    Kernel density estimation
    Kernel (algebra)
    Uniform convergence
    Citations (0)
    Aim To study the strong convergence of weighted function estimator under-mixing errors.Methods Discussing the strong convergence of weighted sums of -mixing random sequences.Results Some sufficient conditions for the strong consistency of the weight function estimator in multiple regression with the fixed design case and -mixing errors was obtained.Conclusion The large sample properties of weighted function estimation was obtained.
    Weight function
    Regression function
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