Bootstrap approximation of nearest neighbor regression function estimates

Let (X, Y) be a random vector in the plane and denote by m(x) = (Y|X = x) the corresponding regression function. We show that the bootstrap approximation for the distribution of a smoothed nearest neighbor estimate of m(x) is valid. Also we compare, by Monte Carlo, confidence intervals which are obtained from both the normal and the bootstrap approximation.     

Asymptotic properties for median cross-validated nearest neighbor median estimate in nonparametric regression

Consider the nonparametric regression model , whereg is an unknown function to be estimated on [0, 1], are the fixed design points in the interval [0, 1] and is a triangular array of row iid random variables having median zero. The nearest neighbor median estimator is taken as the estimator of the unknown functiong(x). Median cross validation (mev) criterion is employed to select the smoothing parameterh. Leth _π ^* be the smoothing parameter chosen by mev criterion. Under mild regularity conditions, the upper and lower bounds ofh _π ^* , the rate of convergence and the weak consistency of the median cross-validated estimate are obtained. Project supported by the National Natural Science Foundation of China and the Doctoral Foundation of Education of China.     

Strong consistency of nearest neighbor regression function estimators

For a well-known class of nonparametric regression function estimators of nearest neighbor type the uniform measure of deviation from the estimators to the true regression function is studied. Under weak regularity conditions it is shown that the estimators are uniformly consistent with probability one and the corresponding rate of convergence is near-optimal.     

Strong consistency of nearest neighbor kernel regression estimation for stationary dependent samples

Under quite mild conditions onk _n . the strong consistency is proved for the nearest neighbor density, the nearest neighbor kernel regression and the modified nearest neighbor kernel regression of an a-mixing stationary sequence in time series context. The condition imposed on the mixing coefficients is , . which is simple and weak.     

Asymptotic consistency of fixed-width sequential confidence intervals for a multiple regression function

Summary Letm _n (x) be the recursive kernel estimator of the multiple regression functionm(x)=E[Y|X=x]. For given α (0<α<1) andd>0 we define a certain class of stopping timesN=N(α,d, x) and takeI _N,d (x)=[m _N (x)−d, m _N (x)+d] as a 2d-width confidence interval form(x) at a given pointx. In this paper it is shown that the probability P{m(x)∈I _N,d (x)} converges to α asd tends to zero.     

L1-optimal estimates for a regression type function in R

Let X₁, X₂, …, X_n be random vectors that take values in a compact set in R^d, d ≥ 1. Let Y₁, Y₂, …, Y_n be random variables (“the responses”) which conditionally on X₁ = x₁, …, X_n = x_n are independent with densities f(y | x_i, θ(x_i)), i = 1, …, n. Assuming that θ lives in a sup-norm compact space Θ_q,d of real valued functions, an optimal L₁-consistent estimator of θ is constructed via empirical measures. The rate of convergence of the estimator to the true parameter θ depends on Kolmogorov's entropy of Θ_q,d.     

Robust nonparametric regression estimation

In this paper we define a robust conditional location functional without requiring any moment condition. We apply the nonparametric proposals considered by C. Stone (Ann. Statist. 5 (1977), 595–645) to this functional equation in order to obtain strongly consistent, robust nonparametric estimates of the regression function. We give some examples by using nearest neighbor weights or weights based on kernel methods under no assumptions whatsoever on the probability measure of the vector (X,Y). We also derive strong convergence rates and the asymptotic distribution of the proposed estimates.     

一种包含递归的核回归估计的回归预测模型

本文首先构造了包含递归的核回归的回归模型 ,然后将其应用于湖南省城镇居民消费性支出 ,提高了拟合度 ,减少了预测误差     

Abnormal behavior of the least squares estimate of multiple regression

An example is given to reveal the abnormal behavior of the least squares estimate of multiple regression. It is shown that the least squares estimate of the multiple linear regression may be “improved” in the sense of weak consistency when nuisance parameters are introduced into the model. A discussion on the implications of this finding is given.     

Consistent nonparametric regression from recursive partitioning schemes

We here extend our results on asymptotically Bayes risk efficient classification to the general regression scenario. More precisely, we find L^p consistent estimators for an arbitrary regression function provided only that the dependent variable has a finite absolute pth moment. The estimators are truncated and untruncated local means derived from recursive partitioning schemes.     

Asymptotic normality of the nearest neighbor hazard estimates

1.IntroductionSupposethatXI)')Xu,'beani.i.d.sequenceofrandomvariableswithdistributionfunctionF(x)anddensityfunctionf(x).TOestimatethedensityfunctionatxbasedonthefirstnobservations,thekerneltypeestimategiveswhereK')isakernelfunction,R.(x)isabandwidthsequenceandFi')isanestimateofF,usuallytakentobetheempiricaldistributionfunction.Formoredetails,see[21.Inmedicalapplications,itisoftenmoreimportanttoestimatethehazardfunctiondefinedbyA(x)~f(x)/(1--F(x)).IfXrepresentsalife-time,thenA(x)repre…     

The homotopical reduction of a nearest neighbor random walk*

Consider a nearest neighbor random walk on a graph G and discard all the segments of its trajectory that are homotopically equivalent to a single point. We prove that if the lift of the random walk to the covering tree of G is transient, then the resulting reduced trajectories induce a Markov chain on the set of oriented edges of G. We study this chain in relation with the original random walk. As an intermediate result, we give a simple proof of the Markovian structure of the harmonic measure on trees.* Supported by Nucleus Millennium Information and Randomness ICM P01-005.     

α混合下非参数回归函数改良分割估计的强相合性及收敛速度

In this paper, we study the strong consistency and convergence rate of modified partitioning estimate of nonparametric regression function under the sample {(Xi, Yi),i ≥ 1} that is α sequence taking values in Rd × R1 with identical distribution.     

NA样本固定设计下半参数回归模型中估计的强相合性

对于半参数回归模型yi=xiβ g(ti) ei，i=1，2,…，n(xi，ti)为已知的固定设计点列．本在误差{e．1≤n≤n)为NA序列时，对g(t)和σ的估计量gn~(t)和σn^2的逐点强相合。以及gn~(t)的一致强相合作了研究，得到比较理想的结果。     

On strong consistency of the fuzzy generalized nearest neighbor rule

The k nearest neighbor rule (k-NNR) is a well-known nonparametric decision rule in pattern classification. Fuzzy set theory has been widely used to represent the uncertainty of class membership. Several researchers extended conventional k-NNR to fuzzy k-NNR, such as Bezdek et al. [Fuzzy Sets and Systems 18 (1986) 237–256], Keller et al. [IEEE Trans. Syst. Man, and Cybern. 15(4) (1985) 580–585], Béreau and Dubuisson [Fuzzy Sets and Systems 44 (1991) 17–32]. In this paper, we describe a fuzzy generalized k-NN algorithm. This algorithm is a unified approach to a variety of fuzzy k-NNR's. Then we create the strong consistency of posterior risk of the fuzzy generalized NNR.     

Nonlinear wavelet estimation of regression function with random desigm

The nonlinear wavelet estimator of regression function with random design is constructed. The optimal uniform convergence rate of the estimator in a ball of Besov spaceB ³ _p,q is proved under quite general assumpations. The adaptive nonlinear wavelet estimator with near-optimal convergence rate in a wide range of smoothness function classes is also constructed. The properties of the nonlinear wavelet estimator given for random design regression and only with bounded third order moment of the error can be compared with those of nonlinear wavelet estimator given in literature for equal-spaced fixed design regression with i.i.d. Gauss error. Project supported by Doctoral Programme Foundation, the National Natural Science Foundation of China (Grant No. 19871003) and Natural Science Fundation of Heilongjiang Province, China.     

Asymptotic theory of least squares estimator in a nonregular nonlinear regression model

The asymptotic properties of the least squares estimator are derived for a nonregular nonlinear model via the study of weak convergence of the least squares process. This approach was adapted earlier by the author in the smooth case. The model discussed here is not amenable to analysis via the normal equations and Taylor expansions used by earlier authors.     

Asymptotic theory of nonparametric regression estimates with censored data

For regression analysis, some useful information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literature, but the optimal rates of global convergence have not been obtained yet. Because of the possible information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression function based on right_censored response data, and proves, under some regularity conditions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtain the asymptotic optimality of AIC, AICC, GCV, Cp and FPE criteria in the process of selecting the parameters.     

Testing for independence by the empirical characteristic function

Tests of total independence of d (≥2) random variables are proposed using the empirical characteristic function. The approach is parallel to that of Hoeffding, Blum, Kiefer, and Rosenblatt.     

Asymptotically optimal selection of a piecewise polynomial estimator of a regression function

Let (X, Y) be a pair of random variables such that X = (X₁,…, X_d) ranges over a nondegenerate compact d-dimensional interval C and Y is real-valued. Let the conditional distribution of Y given X have mean θ(X) and satisfy an appropriate moment condition. It is assumed that the distribution of X is absolutely continuous and its density is bounded away from zero and infinity on C. Without loss of generality let C be the unit cube. Consider an estimator of θ having the form of a piecewise polynomial of degree k_n based on m_n^d cubes of length 1/m_n, where the m_n^d(_d^k_n+d) coefficients are chosen by the method of least squares based on a random sample of size n from the distribution of (X, Y). Let (k_n, m_n) be chosen by the FPE procedure. It is shown that the indicated estimator has an asymptotically minimal squared error of prediction if θ is not of the form of piecewise polynomial.