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11.
Representation theorem and local asymptotic minimax theorem are derived for nonparametric estimators of the distribution function on the basis of randomly truncated data. The convolution-type representation theorem asserts that the limiting process of any regular estimator of the distribution function is at least as dispersed as the limiting process of the product-limit estimator. The theorems are similar to those results for the complete data case due to Beran (1977, Ann. Statist., 5, 400–404) and for the censored data case due to Wellner (1982, Ann. Statist., 10, 595–602). Both likelihood and functional approaches are considered and the proofs rely on the method of Begun et al. (1983, Ann. Statist., 11, 432–452) with slight modifications.Division of Biostatistics, School of Public Health, Columbia Univ. 相似文献
12.
Liu Huimei 《Annals of the Institute of Statistical Mathematics》2000,52(1):15-27
Let Xhave a multivariate, p-dimensional normal distribution (p 2) with unknown mean and known, nonsingular covariance . Consider testing H
0 : b
i 0, for some i = 1,..., k, and b
i 0, for some i = 1,..., k, versus H
1 : b
i < 0, for all i = 1,..., k, or b
i
< 0, for all i = 1,..., k, where b
1,..., b
k
, k 2, are known vectors that define the hypotheses and suppose that for each i = 1,..., k there is an j {1,..., k} (j will depend on i) such that b
i b
j 0. For any 0 < < 1/2. We construct a test that has the same size as the likelihood ratio test (LRT) and is uniformly more powerful than the LRT. The proposed test is an intersection-union test. We apply the result to compare linear regression functions. 相似文献
13.
In this paper we investigate the performance of a linear wavelet-type deconvolution estimator for weakly dependent data. We
show that the rates of convergence which are optimal in the case of i.i.d. data are also (almost) attained for strongly mixing
observations, provided the mixing coefficients decay fast enough. The results are applied to a discretely observed continuous-time
stochastic volatility model. 相似文献
14.
Roelof Helmers I. Wayan Mangku 《Annals of the Institute of Statistical Mathematics》2009,61(3):599-628
We construct and investigate a consistent kernel-type nonparametric estimator of the intensity function of a cyclic Poisson
process in the presence of linear trend. It is assumed that only a single realization of the Poisson process is observed in
a bounded window. We prove that the proposed estimator is consistent when the size of the window indefinitely expands. The
asymptotic bias, variance, and the mean-squared error of the proposed estimator are also computed. A simulation study shows
that the first order asymptotic approximations to the bias and variance of the estimator are not accurate enough. Second order
terms for bias and variance were derived in order to be able to predict the numerical results in the simulation. Bias reduction
of our estimator is also proposed. 相似文献
15.
This article deals with adaptive nonparametric estimation for Lévy processes observed at low frequency. For general linear functionals of the Lévy measure, we construct kernel estimators, provide upper risk bounds and derive rates of convergence under regularity assumptions. 相似文献
16.
We reveal the boundary bias problem of Birnbaum–Saunders, inverse Gaussian, and reciprocal inverse Gaussian kernel estimators (Jin and Kawczak, 2003, Scaillet, 2004) and re-formulate these estimators to solve the problem. We investigate asymptotic properties of a new class of asymmetric kernel estimators. 相似文献
17.
We show that copulae and kernel estimation can be mixed to estimate the risk of an economic loss. We analyze the properties of the Sarmanov copula. We find that the maximum pseudo-likelihood estimation of the dependence parameter associated with the copula with double transformed kernel estimation to estimate marginal cumulative distribution functions is a useful method for approximating the risk of extreme dependent losses when we have large data sets. We use a bivariate sample of losses from a real database of auto insurance claims. 相似文献
18.
Mood’s median test for testing the equality of medians is a nonparametric approach, which has been widely used for uncensored data in practice. For survival data, many nonparametric methods have been proposed to test for the equality of survival curves. However, if the survival medians, rather than the curves, are compared, those methods are not applicable. Some approaches have been developed to fill this gap. Unfortunately, in general those tests have inflated type I error rates, which make them inapplicable to survival data with small sample sizes. In this paper, Mood’s median test for uncensored data is extended for survival data. The results from a comprehensive simulation study show that the proposed test outperforms existing methods in terms of controlling type I error rate and detecting power. 相似文献
19.
In continuous time, rates of convergence of density estimators fluctuate with the nature of observed sample paths. In this paper, we give a family of rates reached by the kernel estimator and we show that these rates are minimax. Finally, we study applications of these results for specific classes of processes including the Gaussian ones 相似文献
20.
To achieve robustness against the outliers or heavy-tailed sampling distribution, we consider an Ivanov regularized empirical risk minimization scheme associated with a modified Huber's loss for nonparametric regression in reproducing kernel Hilbert space. By tuning the scaling and regularization parameters in accordance with the sample size, we develop nonasymptotic concentration results for such an adaptive estimator. Specifically, we establish the best convergence rates for prediction error when the conditional distribution satisfies a weak moment condition. 相似文献