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 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.     

Strong consistency of least squares estimates in multiple regression II

The strong consistency of least squares estimates in multiple regression models is established under minimal assumptions on the design and weak dependence and moment restrictions on the errors.     

Strong consistency of kernel estimators for Markov transition densities

Let P(x, dy) = t (x, y)ν(d y) be the transition kernel of a Markov chain, where t (x, y) is a density with respect to a σ-finite measure ν on (E,ℰ), with E ⊂ R ^d . In this note, we propose a general class of estimates for t (x, y) that are strongly consistent and that extend the classical results for continuous densities on R ^d . Received: 2 June 2002     

随机损失数据下回归函数核估计的强相合性

在不完全数据下，本文得到了回归函数核估计的强相合性。这里所说的不完全数据是指子样数据按一定的随机律被删除。     

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.     

Strong consistency of maximum quasi-likelihood estimates in generalized linear models

In a generalized linear model with q×1 responses, bounded and fixed p×q regressors zi and general link function, under the most general assumption on the minimum eigenvalue of ∑in=1 ZiZi', the moment condition on responses as weak as possible and other mild regular conditions, we prove that with probability one, the quasi-likelihood equation has a solution βn for all large sample size n, which converges to the true regression parameter β0. This result is an essential improvement over the relevant results in literature.     

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.     

污染数据半参数回归模型中估计的相合性

利用最小二乘估计方法和权函数法给出了半参数模型Y=βX g(T) ε在某种污染方式下,,βg和污染系数的估计,并在适当条件下证明了它们具有相合性.     

Asymptotic normality and strong consistency of maximum quasi-likelihood estimates in generalized linear models

In a generalized linear model with q x 1 responses, the bounded and fixed (or adaptive) p × q regressors Zi and the general link function, under the most general assumption on the minimum eigenvalue of ZiZ'i,the moment condition on responses as weak as possible and the other mild regular conditions, we prove that the maximum quasi-likelihood estimates for the regression parameter vector are asymptotically normal and strongly consistent.     

Universal Consistency of Local Polynomial Kernel Regression Estimates

Regression function estimation from independent and identically distributed data is considered. The L ₂ error with integration with respect to the design measure is used as an error criterion. It is shown that suitably defined local polynomial kernel estimates are weakly and strongly universally consistent, i.e., it is shown that the L ₂ errors of these estimates converge to zero almost surely and in L ₁ for all distributions.     

Convergence systems and strong consistency of least squares estimates in regression models

A recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal.9 (1979), 343–362) is extended to a more general form which unifies previous results in the literature on the strong consistency of least squares estimates in multiple regression models with nonrandom regressors. In particular the issue of strong consistency of the least squares estimate in the Gauss-Markov model, in the i.i.d. model with infinite second moment, and in general time series models is examined. In this connection, some basic properties of convergence systems are also obtained and are applied to the strong consistency problem.     

Strong consistency of M-estimates in linear models

The strong consistency of M-estimates of the regression coefficients in a linear model under some mild conditions is established, which is an essential improvement over the relevant results in the literature on the moment condition. Especially, in some important circumstances, onlyE|ψ(e_k)|^q for some q > 1 is needed, where ψ{e_k} is some score function of random error.     

Some remarks concerning the consistency of LS and LAD estimates of multiple regression

1.IntroductionConsideralinearregressionmodelwhereK,xi,doandeiaretheobservationofthetargetvariable,aknownHvector,theunknownparametervector,andtherandomerror,respectively.LetpbeaconvexfunctiondefinedonRI.TheM-estimateofpo,tobedenotedbyac,isdefinedasaminimizingpointofthefunctionH(P)=ZP(K--x:g).Denotetheleftandrightderivativesofpbyop--andi=1op .Regardingtheweakconsistencyofac,Zhao,RaoandChenll]establishedthefollowingresult:TheoremA.Lete15eZt'belid.Supposethatthereexistsfunctionop,satisfy…     

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.     

Strong uniform consistency of nonparametric regression function estimates

Let (X, Y) be a ^dx-valued random vector and let r(t)=E(Y/X=t) be the regression function of Y on X that has to be estimated from a sample (X _i, Y_i), i=1,..., n. We establish conditions ensuring that an estimate of the form

Strong Universal Pointwise Consistency of Recursive Regression Estimates

For semi-recursive and recursive kernel estimates of a regression of Y on X (d-dimensional random vector X, integrable real random variable Y), introduced by Devroye and Wagner and by Révész, respectively, strong universal pointwise consistency is shown, i.e. strong consistency P _X -almost everywhere for general distribution of (X, Y). Similar results are shown for the corresponding partitioning estimates.     

Weak and strong uniform consistency of kernel regression estimates

Summary We study the estimation of a regression function by the kernel method. Under mild conditions on the window, the bandwidth and the underlying distribution of the bivariate observations {(X _i , Y _i)}, we obtain the weak and strong uniform convergence rates on a bounded interval. These results parallel those of Silverman (1978) on density estimation and extend those of Schuster and Yakowitz (1979) and Collomb (1979) on regression estimation.This research was carried out in part while the authors were guests at the University of Heidelberg, Germany, under the sponsorship of the Sonderforschungsbereich 123 in the summer of 1980     

固定设计下半参数回归模型估计的强相合性

对于半参数模型ｙｉ（Ｔ）＝βｘｉ（Ｔ）＋ｇ（ｔｉ（Ｔ））＋εｉ（Ｔ），本文综合最小二乘和一般的非参数估计方法，定义了β，ｇ的估计量βＴ，ｇＴ，在适当条件下证明了它们的强相合性，