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排序方式： 共有943条查询结果，搜索用时 15 毫秒
31.
《Journal of nonparametric statistics》2012,24(3):263-285
There are two classes of estimators for the error variance in nonparametric regression: residual-based estimators and difference-based estimators. Residual-based estimators require an estimator of the regression function and are asymptotically equivalent to the sample variance based on the actual errors. Difference-based estimators avoid estimating the regression function and are thus simpler to calculate. They also possess superior bias properties at the expense of larger variances. Müller et al. [U.U. Müller, A. Schick, and W. Wefelmeyer, Estimating the error variance in nonparametric regression by a covariate-matched U-statistics, Statistics 37 (2003), pp. 179–188.] suggested improving difference-based estimators using covariate matching. They showed that a covariate-matched version of Rice's [J. Rice, Bandwidth choice for nonparametric regression, Ann. Statist. 12 (1984), pp. 1215–1230.] difference-based estimator matches the asymptotic performance of residual-based estimators, yet still possesses the good bias properties of Rice's estimator. Here we prove a similar result for a covariate-matched version of the difference-based estimator of Gasser et al. [T. Gasser, L. Sroka, and C. Jennen-Steinmetz, Residual variance and residual pattern in nonlinear regression, Biometrika 73 (1986), pp. 625–633.] as their estimator has even better bias properties than Rice's estimator. 相似文献
32.
《Journal of nonparametric statistics》2012,24(3):365-378
We study the empirical Bayes estimation for the coefficient of variation γ=θ/(θ+μ) in a shifted exponential distribution having density p(x|θ, μ)=θ−1e −(x−μ)/θ I(x>μ), μ>0, and the parameter θ is in some finite interval [a 1, a 2], where 0<a 1<a 2<∞ are known constants. An empirical Bayes estimator ϕ˜ n is constructed and its associated asymptotic optimality investigated. It is shown that the regret of ˜ ϕ n converges to zero at a rate O((ln n)4/n). 相似文献
33.
《Journal of nonparametric statistics》2012,24(6):523-537
Assuming the structure of a mixing spatial data process {(Y i , X i ), i∈ℝ N }, the least absolute deviation (L 1) method is proposed to estimate the spatial conditional regression function with the superiority of weakening the influence of outliers and aberrant observations, which appear very often in spatial data. With appropriate choices of the bandwidth under some mild conditions imposed on the spatial process, the asymptotic distributions of the estimators are derived. Three simulation models using L 1 and L 2 methods respectively show that the L 1-estimators are superior to L 2-estimators. 相似文献
34.
《Journal of nonparametric statistics》2012,24(2):255-285
An adaptive nonparametric procedure is constructed for estimating the unknown drift coefficient in ergodic diffusion processes. A sharp non-asymptotic upper bound (an oracle inequality) is obtained for a quadratic risk. Furthermore, an asymptotic lower bound for the minimax quadratic risk is found that equals to the Pinsker constant. Asymptotic efficiency is proved, that is, the asymptotic quadratic risk of the constructed estimator coincides with this constant. 相似文献
35.
《Journal of nonparametric statistics》2012,24(1):115-130
It has been difficult to generalise Kaplan–Meier approaches to censored regression data under the minimal condition that censoring and response are conditionally independent given the explanatory variables. Portnoy [S. Portnoy, Censored regression quantiles, J. Am. Stat. Assoc. 98 (2003), pp. 1001–1012.] provided such a generalisation based on the paradigm of censored quantile regression. However, previous research has only provided consistency results for this approach. The results here provide an asymptotic distribution theory under relatively mild conditions for a gridded version of the algorithm in Portnoy [S. Portnoy, Censored regression quantiles, J. Am. Stat. Assoc. 98 (2003), pp. 1001–1012.], and show that the asymptotics for censored regression quantiles are an exact generalisation of those for the Kaplan–Meier estimator in one sample. 相似文献
36.
《Journal of nonparametric statistics》2012,24(3):341-352
Having two variables, an explanatory one ( X o ) and a response one ( Y o ), linked by the classical relation Y^{o} = g({bf X}^{o}) + varepsilon , we want to estimate the function g (.) without any parametric assumption. However, in a lot of situations, the variables are not measured directly but through their proxies {bf X} = {bf X}^{o} + bivarepsilon and Y = Y^{o} + eta where l and m are the measurements errors. We propose here a new method for estimating the function g (.) in such a context. Our estimator is based on deconvoluted kernels. Uniform convergence is established for strongly mixing stochastic processes. Some simulations show that our estimator is tractable and performs relatively well in practice. 相似文献
37.
《Journal of nonparametric statistics》2012,24(2):151-169
By generalizing the proportional hazards model, we introduce a new function β( t ), which we call the proportionality function, and which we show plays a role in studying aspects of the randomly censored model. We develop an asymptotically efficient nonparametric estimator of β( t ), establish its uniform consistency, and obtain a weak convergence result. Furthermore, a confidence band for β( t ), based on the bootstrap, is developed. The results are applied to an actual dataset. 相似文献
38.
《Journal of nonparametric statistics》2012,24(5):583-605
The fitting of heteroscedastic median regression models to right censored data has been a topic of much research in survival analysis in recent years. McKeague et al. (2001) used the missing information principle to propose an estimator for the regression parameters, and derived the asymptotic properties of their estimator assuming that the covariate takes values in a finite set. In this paper the large sample properties of their estimator are derived when the covariate is continuous. A kernel conditional Kaplan-Meier estimator is used in the missing information principle estimating function. A simulation study involving a one-dimensional covariate is presented. 相似文献
39.
Hira L. Koul 《Revue canadienne de statistique》1978,6(2):249-271
A class of tests is proposed for testing H0 F?(x) = e?λx, λ > 0, x≥0 vs. H1 F?(x + y) ≤ F?(x)F?(y), x, y≥0, with strict inequality for some x, y ≥ 0 (F = new is better than used). Efficiency comparisons of some tests within the class are made and a new test is proposed on the basis of these comparisons. Consistency and the asymptotic normality of the class of tests is proved under fairly broad conditions on the underlying entities. 相似文献
40.
Dolby's (1976) ultrastructural model with no replications is investigated within the class of the elliptical distributions. General asymptotic results are given for the sample covariance matrix S in the presence of incidental parameters. These results are used to study the asymptotic behaviour of some estimators of the slope parameter, unifying and extending existing results in the literature. In particular, under some regularity conditions they are shown to be consistent and asymptotically normal. For the special case of the structural model, some asymptotic relative efficiencies are also reported which show that generalized least squares and the method of moment estimators can be highly inefficient under nonnormality. 相似文献