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971.
In this work, we propose a technique of estimating the location parameter μ and scale parameter σ of Type-I generalized logistic distribution by U-statistics constructed by using best linear functions of order statistics as kernels. The efficiency comparison of the proposed estimators with respect to maximum likelihood estimators is also made. 相似文献
972.
973.
In this article, we consider a nonparametric estimator of the Lorenz curve under censored dependent model. We show that this estimator is uniformly strongly consistent for the associated Lorenz curve. Also, a strong Gaussian approximation for the associated Lorenz process are established under appropriate assumptions. A law of the iterated logarithm for the Lorenz process is also derived. 相似文献
974.
Liciana V. A. Silveira Enrico A. Colosimo José Raimundo de S. Passos 《统计学通讯:理论与方法》2013,42(15):2659-2666
It is common to have experiments in which it is not possible to observe the exact lifetimes but only the interval where they occur. This sort of data presents a high number of ties and it is called grouped or interval-censored survival data. Regression methods for grouped data are available in the statistical literature. The regression structure considers modeling the probability of a subject's survival past a visit time conditional on his survival at the previous visit. Two approaches are presented: assuming that lifetimes come from (1) a continuous proportional hazards model and (2) a logistic model. However, there may be situations in which none of the models are adequate for a particular data set. This article proposes the generalized log-normal model as an alternative model for discrete survival data. This model was introduced by Chen (1995) and it is extended in this article for grouped survival data. A real example related to a Chagas disease illustrates the proposed model. 相似文献
975.
This article focuses on the minimum distance estimators under two newly introduced modifications of Cramér–von Mises distance. The generalized power form of Cramér–von Mises distance is defined together with the so-called Kolmogorov–Cramér distance which includes both standard Kolmogorov and Cramér–von Mises distances as limiting special cases. We prove the consistency of Kolmogorov-Cramér estimators in the (expected) L1-norm by direct technique employing domination relations between statistical distances. In our numerical simulation we illustrate the quality of consistency property for sample sizes of the most practical range from n = 10 to n = 500. We study dependence of consistency in L1-norm on ?-contamination neighborhood of the true model and further the robustness of these two newly defined estimators for normal families and contaminated samples. Numerical simulations are used to compare statistical properties of the minimum Kolmogorov–Cramér, generalized Cramér–von Mises, standard Kolmogorov, and Cramér–von Mises distance estimators of the normal family scale parameter. We deal with the corresponding order of consistency and robustness. The resulting graphs are presented and discussed for the cases of the contaminated and uncontaminated pseudo-random samples. 相似文献
976.
Skew-symmetric distributions of various types have been the center of attraction by many researchers in the literature. In this article, we will introduce a uni/bimodal generalization of the Azzalini's skew-normal distribution which is indeed an extension of the skew-generalized normal distribution obtained by Arellano-Valle et al. (2004). Our new distribution contains more parameters and thus it is more flexible in data modeling. Indeed, certain univariate case of the so called flexible skew-symmetric distribution of Ma and Genton (2004) is also a particular case of our proposed model. We will first study some basic distributional properties of the new extension, such as its distribution function, limiting behavior and moments. Then, we will investigate some useful results regarding its relation with other known distributions, such as student's t and skew-Cauchy distributions. In addition, we will present certain methods to generate the new distribution and, finally, we shall apply the model to a real data set to illustrate its behavior comparing to some rival models. 相似文献
977.
Consider the estimation of the regression parameters in the usual linear model. For design densities with infinite support, it has been shown by Faraldo Roca and González Manteiga [1] that it is possible to modify the classical least squares procedure and to obtain estimators for the regression parameters whose MSE's (mean squared errors) are smaller than those of the usual least squares estimators. The modification consists of presmoothing the response variables by a kernel estimator of the regression function. These authors also show that the gain in efficiency is not possible for a design density with compact support. We show that in this case local linear presmoothing does not fix this inefficiency problem, in spite of the well known fact that local linear fitting automatically corrects the bias in the endpoints of the (design density) support. We demonstrate on a theoretical basis how this inefficiency problem can be rectified in the compact design case: we prove that presmoothing with boundary kernels studied in Müller [2] and Müller and Wang [3] leads to regression estimators which are superior over the least squares estimators. A very careful analytic treatment is needed to arrive at these asymptotic results. 相似文献
978.
A regression estimator using two prior values of population mean (μx) of an auxiliary variable (x) is proposed after a preliminary test of closeness of these prior values to the true valueμx. The proposed preliminary test regression estimator has been found to be more efficient in general than the usual regression estimator when prior values are used in place of μxwithout preliminary test of significance. The efficiency of the proposed estimator over the usual regression estimator has also been computed for different values of Δ0, Δ1, n, and ρ, which showed considerable gain in precision. 相似文献
979.
980.
K.V. Mardia 《统计学通讯:理论与方法》2013,42(13):1233-1241
The paper gives a new optimal property of the classical method of multi-dimensional scaling when the distance matrix is non-Euclidean. We also examine robustness of the method under a linear model. A technique to estimate missing values is also given. 相似文献