Probabilities of maximal deviations for nonparametric regression function estimates

Let (X, Y) have regression function m(x) = E(Y | X = x), and let X have a marginal density f₁(x). We consider two nonparameteric estimates of m(x): the Watson estimate when f₁ is known and the Yang estimate when f₁ is known or unknown. For both estimates the asymptotic distribution of the maximal deviation from m(x) is proved, thus extending results of Bickel and Rosenblatt for the estimation of density functions.     

Strong universal consistency of smooth kernel regression estimates

The paper deals with kernel estimates of Nadaraya-Watson type for a regression function with square integrable response variable. For usual bandwidth sequences and smooth nonnegative kernels, e.g., Gaussian and quartic kernels, strongL ₂-consistency is shown without any further condition on the underlying distribution. The proof uses a Tauberian theorem for Cesàro summability.     

Strong consistency of automatic kernel regression estimates

Regression function estimation from independent and identically distributed bounded data is considered. TheL ₂ error with integration with respect to the design measure is used as an error criterion. It is shown that the kernel regression estimate with an arbitrary random bandwidth is weakly and strongly consistent forall distributions whenever the random bandwidth is chosen from some deterministic interval whose upper and lower bounds satisfy the usual conditions used to prove consistency of the kernel estimate for deterministic bandwidths. Choosing discrete bandwidths by cross-validation allows to weaken the conditions on the bandwidths. Research supported by DAAD, NSERC and Alexander von Humboldt Foundation. The research of the second author was completed during his stay at the Technical University of Szczecin, Poland.     

On the convergence of maximal monotone operators

We study the convergence of maximal monotone operators with the help of representations by convex functions. In particular, we prove the convergence of a sequence of sums of maximal monotone operators under a general qualification condition of the Attouch-Brezis type.

     

Endpoint estimates for the circular maximal function

We consider the problem of endpoint estimates for the circular maximal function defined by

where is the normalized surface area measure on . Let be the closed triangle with vertices . We prove that for , there is a constant such that Furthermore, .

     

分数次Orlicz极大算子在齐型空间中的局部加权端点估计

利用齐型空间中的覆盖引理及其有界区域的二进方体分解得到了分数次Orlicz极大算子在齐型空间（X,d,μ）中的有界区域Ω上的局部加权端点估计．该工作为分数次积分交换子[b,Iα】在欧式空间R^n中的有界区域上的加权端点弱型估计推广到齐型空间奠定了基础．     

Exponential bounds of mean error for the kernel estimates of regression functions

Let (X, Y), (X₁, Y₁), …, (X_n, Y_n) be i.d.d. R^r × R-valued random vectors with E|Y| < ∞, and let Q_n(x) be a kernel estimate of the regression function Q(x) = E(Y|X = x). In this paper, we establish an exponential bound of the mean deviation between Q_n(x) and Q(x) given the training sample Zⁿ = (X₁, Y₁, …, X_n, Y_n), under conditions as weak as possible.     

On the rate of convergence for central limit theorems of sojourn times of Gaussian fields

The aim of this paper is to control the rate of convergence for central limit theorems of sojourn times of Gaussian fields in both cases: the fixed and the moving level. Our main tools are the Malliavin calculus and the Stein method, developed by Nualart, Peccati and Nourdin. We also extend some results of Berman to the multidimensional case.     

On the boundedness of the non-centered Gaussian Hardy-Littlewood maximal function

The purpose of this paper is to prove the boundedness, for 1$">, of the non-centered Hardy-Littlewood maximal operator associated with the Gaussian measure .

     

Weighted endpoint estimates for maximal commutators associated with the sections

We prove endpoint estimates for maximal commutators for a class of singular integral operators related to the real analysis of the Monge Ampere equation.     

A prior near-ignorance Gaussian process model for nonparametric regression

This paper proposes a prior near-ignorance model for regression based on a set of Gaussian Processes (GP). GPs are natural prior distributions for Bayesian regression. They offer a great modeling flexibility and have found widespread application in many regression problems. However, a GP requires the prior elicitation of its mean function, which represents our prior belief about the shape of the regression function, and of the covariance between any two function values.In the absence of prior information, it may be difficult to fully specify these infinite dimensional parameters. In this work, by modeling the prior mean of the GP as a linear combination of a set of basis functions and assuming as prior for the combination coefficients a set of conjugate distributions obtained as limits of truncate exponential priors, we have been able to model prior ignorance about the mean of the GP. The resulting model satisfies translation invariance, learning and, under some constraints, convergence, which are desirable properties for a prior near-ignorance model. Moreover, it is shown in this paper how this model can be extended to allow for a weaker specification of the GP covariance between function values, by letting each basis function to vary in a set of functions.Application to hypothesis testing has shown how the use of this model induces the capability of automatically detecting when a reliable decision cannot be made based on the available data.     

Optimal global rates of convergence ofM-estimates for multivariate nonparametric regression

Consider the nonparametric regression modelY=go(T)+u, whereY is real-valued,u is a random error,T is a randomd-vector of explanatory variables ranging over a nondegenerated-dimensional compact setC, andgo(·) is the unknown smooth regression function, which ism (0) times continuously differentiable and itsmth partial derivatives satisfy the Hölder condition with exponent(0,1], wherei ₁, ...,i _d are nonnegative integers satisfying _k ^=1/d i _k =m. The piecewise polynomial estimator ofgo based onM-estimates is considered. It is proved that the rate of convergence of the underlying estimator is under certain regular conditions, which is the optimal global rate of convergence of least square estimates for nonparametric regression studied in [10–11].This work is partly supported by the National Natural Science Foundation of China.     

On convergence of iterative methods for maximal correlation problems

Several iterative methods for maximal correlation problems (MCPs) have been proposed in the literature. This paper deals with the convergence of these iterations and contains three contributions. Firstly, a unified and concise proof of the monotone convergence of these iterative methods is presented. Secondly, a starting point strategy is analysed. Thirdly, some error estimates are presented to test the quality of a computed solution. Both theoretical results and numerical tests suggest that combining with this starting point strategy these methods converge rapidly and are more likely converging to a global maximizer of MCP. Copyright © 2016 John Wiley & Sons, Ltd.     

Robust errors-in-variables linear regression via Laplace distribution

Robust estimation procedures for linear and mixture linear errors-in-variables regression models are proposed based on the relationship between the least absolute deviation criterion and maximum likelihood estimation in a Laplace distribution. The finite sample performance of the proposed procedures is evaluated by simulation studies.     

Strong convergence of LAD estimates in a censored regression model

A regression model with a nonnegativity constraint on the dependent variable, known as censored median regression model, is considered. Under some mild conditions, the LAD estimate of the regression coefficient is shown to be strongly consistent. Furthermore, its convergence rate and Bahadur strong representation are also obtained.     

On regression model selection for the data with correlated errors

A class of regression model selection criteria for the data with correlated errors is proposed. The proposed class of selection criteria is an estimator of weighted prediction risk. In addition, the proposed selection criteria are the generalizations of several commonly used criteria in statistical analysis. The theoretical and asymptotic properties for the class of criteria are established. Further, in the medium-sample case, the results based on a simulation study are quite consistent with the theoretical ones. The proposed criteria perform well in the simulations. Several applications are also given for a variety of statistical models.     

Multiple weighted estimates for commutators of multilinear maximal function

Let M be the multilinear maximal function and b =(b1,..., bm) be a collection of locally integrable functions. Denote by M b and [ b, M] the maximal commutator and the commutator of M with b, respectively. In this paper, the multiple weighted strong and weak type estimates for operators M b and [ b, M] are studied. Some characterizations of the class of functions bj are given, for which these operators satisfy some strong or weak type estimates.     

The rate of convergence for sparse and low-rank quantile trace regression

Trace regression models are widely used in applications involving panel data, images, genomic microarrays, etc., where high-dimensional covariates are often involved. However, the existing research involving high-dimensional covariates focuses mainly on the condition mean model. In this paper, we extend the trace regression model to the quantile trace regression model when the parameter is a matrix of simultaneously low rank and row (column) sparsity. The convergence rate of the penalized estimator is derived under mild conditions. Simulations, as well as a real data application, are also carried out for illustration.