[PDF] Top 20 Uniform convergence of estimator for nonparametric regression with dependent data

... internal estimator was first proposed by Mack and Müller ...kernel-type regression estimators, including introduced the internal estimator ...the estimator similar to estimator ...kernel ... See full document

... the nonparametric regression model based on these ...the estimator of g(x) are presented, including the mean convergence, uniform convergence, almost sure convergence and ... See full document

... In this paper, we aim to study the asymptotic properties of internal estimator of nonparametric regression with independent and dependent data. Under some weak conditions, we present ... See full document

... Although nonparametric series estimation gives a systematic way of doing specification search by restricting the domain of search as K ∈ [K, K], little justification has been done, ¯ especially for the inference ... See full document

... for nonparametric multiple regression mea- surement error models when both independent validation data on covariables and primary data on the response variable and surrogate covariables are ... See full document

... This article is organized as follows. In Section 2 we propose a regularization- based method. Under general regularity conditions, we give the convergence rate of our estimator in Section 3. Section 4 ... See full document

... of convergence in the central limit ...the convergence rate result in the central limit theorem and precise asymptotics in the law of the iterated logarithm for NA and linearly negative quadrant ... See full document

... complicated data structures that often call for a specific algorithm tailored to the problem at ...of nonparametric curve estimation ...proposed estimator in section 3 and study its asymptotic ... See full document

... (j = , . . . , m), and in the meantime to exhibit weakly dependence among the units. For a systematic discussion of wavelets and their applications in statistics, see the recent monographs by Härdle et al. [] and ... See full document

... uniformly strong consistency with negatively associated (NA) errors under some weaker assumptions, which improve and extend the corresponding results of Benedetti [2]; Yang [4] provided the Berry–Esseen bound of the ... See full document

... following section gives a review of nonparametric regression, states a nonparametric regression based estimator of the total, gives expressions for its bias and variance, and suggests ho[r] ... See full document

... Quantile regression estimates conditional quantiles and has wide applications in the real ...quantile regression (QR) method often designs a linear or non-linear model, then estimates the coefficients to ... See full document

... or data could be confined to a small arc of the circle (reproducing an euclidean-like scenario), but more generally (as in our example) we could choose the origin according to a minimum width ...same data ... See full document

... strong convergence rates and strong representation of the Kaplan-Meier estimator and the hazard estimator based on censored data when the survival and the censoring times form the NA (see the ... See full document

... A nonparametric Nadaraya-Watson kernel estimator was proposed by Yin et ...kernel estimator that may not be locally ...Bayesian nonparametric approach to simultaneously modeling µ(x) and ... See full document

... Nonparametric regression is an alternative method in regression ...analysis. Nonparametric regression can be used to investigate the relationship between predictor variables and ... See full document

... resultant estimator cannot be reduced further than that given in ...ratio estimator, product estimator and power transformation estimator are the special cases of the class of estimators ... See full document

... Bayesian nonparametric models, inference for the posterior distribution of the parameters in my models is achieved using Monte Carlo Markov Chain ...simulated data, I observed very slow mixing of the ... See full document

... of uniform convergence and learnability was formally established only in the supervised classification and regression ...dimensions, uniform convergence, as well as ERM stability, are ... See full document

... c on more th a n mo inputs chosen random ly according to the distribution //, then h has actual error less th a n e. As emphasised earlier, th e value of mo m ust depend on neither the target concept c nor the ... See full document