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311.
Real lifetime data are never precise numbers but more or less non-precise, also called fuzzy. This kind of imprecision is connected with all measurement results of continuous variables, therefore also with time observations. Imprecision is different from errors and variability. Therefore estimation methods for reliability characteristics have to be adapted to the situation of fuzzy lifetimes in order to obtain realistic results. 相似文献
312.
Goele Massonnet Paul Janssen Luc Duchateau 《Journal of statistical planning and inference》2009,139(11):3865
We study copula models for correlated infection times in the four udder quarters of dairy cows. Both a semi-parametric and a nonparametric approach are considered to estimate the marginal survival functions, taking into account the effect of a binary udder quarter level covariate. We use a two-stage estimation approach and we briefly discuss the asymptotic behaviour of the estimators obtained in the first and the second stage of the estimation. A pseudo-likelihood ratio test is used to select an appropriate copula from the power variance copula family that describes the association between the outcomes in a cluster. We propose a new bootstrap algorithm to obtain the p-value for this test. This bootstrap algorithm also provides estimates for the standard errors of the estimated parameters in the copula. The proposed methods are applied to the udder infection data. A small simulation study for a setting similar to the setting of the udder infection data gives evidence that the proposed method provides a valid approach to select an appropriate copula within the power variance copula family. 相似文献
313.
We consider survival data that are both interval censored and truncated. Under appropriate assumptions on the involved distributions, the censoring, truncation and survival, we prove the consistency of the NPMLE of the density of the survival, and give the rate of convergence. Finally, we give an example where the joint law of the censoring and truncation can be explicitly computed. 相似文献
314.
Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator
seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a
weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that
perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data
driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate
from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement
error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized
subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type
penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over
the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of negative estimation
in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate
deconvolution density estimation in a very straightforward manner. 相似文献
315.
We consider in this paper the regularization by projection of a linear inverse problem Y=Af+εξ where ξ denotes a Gaussian white noise, A a compact operator and ε>0 a noise level. Compared to the standard unbiased risk estimation (URE) method, the risk hull minimization (RHM) procedure presents a very interesting numerical behavior. However, the regularization in the singular value decomposition setting requires the knowledge of the eigenvalues of A. Here, we deal with noisy eigenvalues: only observations on this sequence are available. We study the efficiency of the RHM method in this situation. More generally, we shed light on some properties usually related to the regularization with a noisy operator. 相似文献
316.
In this paper, we study the MDPDE (minimizing a density power divergence estimator), proposed by Basu et al. (Biometrika 85:549–559,
1998), for mixing distributions whose component densities are members of some known parametric family. As with the ordinary
MDPDE, we also consider a penalized version of the estimator, and show that they are consistent in the sense of weak convergence.
A simulation result is provided to illustrate the robustness. Finally, we apply the penalized method to analyzing the red
blood cell SLC data presented in Roeder (J Am Stat Assoc 89:487–495, 1994).
This research was supported (in part) by KOSEF through Statistical Research Center for Complex Systems at Seoul National University. 相似文献
317.
In this note we consider the equality of the ordinary least squares estimator (OLSE) and the best linear unbiased estimator
(BLUE) of the estimable parametric function in the general Gauss–Markov model. Especially we consider the structures of the
covariance matrix V for which the OLSE equals the BLUE. Our results are based on the properties of a particular reparametrized version of the
original Gauss–Markov model.
相似文献
318.
Two-step estimation for inhomogeneous spatial point processes 总被引:1,自引:0,他引:1
Rasmus Waagepetersen Yongtao Guan 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2009,71(3):685-702
Summary. The paper is concerned with parameter estimation for inhomogeneous spatial point processes with a regression model for the intensity function and tractable second-order properties ( K -function). Regression parameters are estimated by using a Poisson likelihood score estimating function and in the second step minimum contrast estimation is applied for the residual clustering parameters. Asymptotic normality of parameter estimates is established under certain mixing conditions and we exemplify how the results may be applied in ecological studies of rainforests. 相似文献
319.
In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ1 ’ or ‘θ < θ1 ’, where θ1 is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ1 is to replace an estimator, , of θ by the maximum of and θ1. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ1 , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ1 , strictly exceed θ1 except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ1, and more generally produces estimators that respect the constraint in a smooth, realistic fashion. 相似文献
320.
Howard D. Bondell Lexin Li 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2009,71(1):287-299
Summary. The family of inverse regression estimators that was recently proposed by Cook and Ni has proven effective in dimension reduction by transforming the high dimensional predictor vector to its low dimensional projections. We propose a general shrinkage estimation strategy for the entire inverse regression estimation family that is capable of simultaneous dimension reduction and variable selection. We demonstrate that the new estimators achieve consistency in variable selection without requiring any traditional model, meanwhile retaining the root n estimation consistency of the dimension reduction basis. We also show the effectiveness of the new estimators through both simulation and real data analysis. 相似文献