共查询到8条相似文献,搜索用时 0 毫秒
1.
We propose two distance-based methods and two likelihood-based methods of inversely regressing a linear predictor on a circular variable, and of inversely regressing a circular predictor on a linear variable. An asymptotic result on least circular distance estimators is provided in the Appendix. We present likelihood-based methods for symmetrical and asymmetrical errors in each situation. The utility of our methodology in each situation is illustrated by applying it to real data sets in engineering and environmental science. We then compare their performances using a cross validation method. 相似文献
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Muhammad Hanif 《统计学通讯:理论与方法》2013,42(22):4142-4163
In this article, we provide a nonparametric estimation of first and second infinitesimal moments of the underlying jump diffusion model. We show that under certain regularity conditions the nonparametric estimations of first and second infinitesimal moments based on the local linear estimator are consistent and asymptotically follow normal distributions. 相似文献
3.
William M. Bolstad 《The American statistician》2013,67(2):129-135
This is an expository article. The Harrison–Stevens forecasting algorithm using the multiprocess dynamic linear model is a robust method for forecasting in a nonstationary time series. The purpose of this article is to help statisticians become familiar with the method. 相似文献
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Jarkko Isotalo 《统计学通讯:理论与方法》2013,42(6):1011-1023
We consider the prediction of new observations in a general Gauss–Markov model. We state the fundamental equations of the best linear unbiased prediction, BLUP, and consider some properties of the BLUP. Particularly, we focus on such linear statistics, which preserve enough information for obtaining the BLUP of new observations as a linear function of them. We call such statistics linearly prediction sufficient for new observations, and introduce some equivalent characterizations for this new concept. 相似文献
5.
In regression analysis, to overcome the problem of multicollinearity, the r ? k class estimator is proposed as an alternative to the ordinary least squares estimator which is a general estimator including the ordinary ridge regression estimator, the principal components regression estimator and the ordinary least squares estimator. In this article, we derive the necessary and sufficient conditions for the superiority of the r ? k class estimator over each of these estimators under the Mahalanobis loss function by the average loss criterion. Then, we compare these estimators with each other using the same criterion. Also, we suggest to test to verify if these conditions are indeed satisfied. Finally, a numerical example and a Monte Carlo simulation are done to illustrate the theoretical results. 相似文献
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In this paper we consider linear sufficiency and linear completeness in the context of estimating the estimable parametric function K′β under the general Gauss–Markov model {y,Xβ,σ2V}. We give new characterizations for linear sufficiency, and define and characterize linear completeness in a case of estimation of K′β. Also, we consider a predictive approach for obtaining the best linear unbiased estimator of K′β, and subsequently, we give the linear analogues of the Rao–Blackwell and Lehmann–Scheffé Theorems in the context of estimating K′β. 相似文献
8.
Rainer Schlittgen 《Statistical Papers》2009,50(2):451-452