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121.
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
122.
M. P. Wand 《Australian & New Zealand Journal of Statistics》2009,51(1):9-41
Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology. 相似文献
123.
当事人在离婚分割夫妻共同财产时,如何处理知识产权,已成为不可忽视的问题。因其独有的特性,知识产权不属于夫妻共同财产,由知识产权取得的收益,则属于夫妻共同财产。知识产权的获得与收益的取得具有不同步性,必须根据具体情形认定婚姻关系存续期间知识产权收益的归属。在离婚时对知识产权收益进行分割可以采取平均分割、估价分割或保留分割请求权的方式。 相似文献
124.
Shonosuke Sugasawa Tatsuya Kubokawa Kota Ogasawara 《Scandinavian Journal of Statistics》2017,44(3):684-706
Random effects model can account for the lack of fitting a regression model and increase precision of estimating area‐level means. However, in case that the synthetic mean provides accurate estimates, the prior distribution may inflate an estimation error. Thus, it is desirable to consider the uncertain prior distribution, which is expressed as the mixture of a one‐point distribution and a proper prior distribution. In this paper, we develop an empirical Bayes approach for estimating area‐level means, using the uncertain prior distribution in the context of a natural exponential family, which we call the empirical uncertain Bayes (EUB) method. The regression model considered in this paper includes the Poisson‐gamma and the binomial‐beta, and the normal‐normal (Fay–Herriot) model, which are typically used in small area estimation. We obtain the estimators of hyperparameters based on the marginal likelihood by using a well‐known expectation‐maximization algorithm and propose the EUB estimators of area means. For risk evaluation of the EUB estimator, we derive a second‐order unbiased estimator of a conditional mean squared error by using some techniques of numerical calculation. Through simulation studies and real data applications, we evaluate a performance of the EUB estimator and compare it with the usual empirical Bayes estimator. 相似文献
125.
?iray et al. proposed a restricted Liu estimator to overcome multicollinearity in the logistic regression model. They also used a Monte Carlo simulation to study the properties of the restricted Liu estimator. However, they did not present the theoretical result about the mean squared error properties of the restricted estimator compared to MLE, restricted maximum likelihood estimator (RMLE) and Liu estimator. In this article, we compare the restricted Liu estimator with MLE, RMLE and Liu estimator in the mean squared error sense and we also present a method to choose a biasing parameter. Finally, a real data example and a Monte Carlo simulation are conducted to illustrate the benefits of the restricted Liu estimator. 相似文献
126.
Kung-Jong Lui 《统计学通讯:模拟与计算》2017,46(7):5229-5239
We consider hypothesis testing and estimation of carry-over effects in continuous data under an incomplete block crossover design when comparing two experimental treatments with a placebo. We develop procedures for testing differential carry-over effects based on the weighted-least-squares (WLS) method. We apply Monte Carlo simulations to evaluate the performance of these test procedures in a variety of situations. We use the data regarding the forced expiratory volume in one second (FEV1) readings taken from a double-blind crossover trial comparing two different doses of formoterol with a placebo to illustrate the use of test procedures proposed here. 相似文献
127.
Abouzar Bazyari 《统计学通讯:模拟与计算》2017,46(9):7194-7209
Testing homogeneity of multivariate normal mean vectors under an order restriction when the covariance matrices are unknown, arbitrary positive definite and unequal are considered. This problem of testing has been studied to some extent, for example, by Kulatunga and Sasabuchi (1984) when the covariance matrices are known and also Sasabuchi et al. (2003) and Sasabuchi (2007) when the covariance matrices are unknown but common. In this paper, a test statistic is proposed and because of the main advantage of the bootstrap test is that it avoids the derivation of the complex null distribution analytically, a bootstrap test statistic is derived and since the proposed test statistic is location invariance the bootstrap p-value defined logical and some steps are presented to estimate it. Our numerical studies via Monte Carlo simulation show that the proposed bootstrap test can correctly control the type I error rates. The power of the test for some of the p-dimensional normal distributions is computed by Monte Carlo simulation. Also, the null distribution of test statistic is estimated using kernel density. Finally, the bootstrap test is illustrated using a real data. 相似文献
128.
Yu-Ye Zou 《统计学通讯:理论与方法》2017,46(2):1007-1023
In this article, we study global L2 error of non linear wavelet estimator of density in the Besov space Bspq for missing data model when covariables are present and prove that the estimator can achieve the optimal rate of convergence, which is similar to the result studied by Donoho et al. (1996) in complete independent data case with term-by-term thresholding of the empirical wavelet coefficients. Finite-sample behavior of the proposed estimator is explored via simulations. 相似文献
129.
This paper applies stratified random sampling using Neyman allocation to Mangat et al. (1992) unrelated question randomized response (RR) strategy for both completely truthful reporting and less than completely truthful reporting. It is shown that, for the prior information given, our new model is more efficient in terms of variance (in the case of completely truthful reporting) and mean square error (in terms of less than completely truthful reporting) than Kim and Elam's (2007) model. Numerical illustrations and graphs are also given in support of the present study. 相似文献
130.
Ro Jin Pak 《统计学通讯:理论与方法》2017,46(2):602-608
Theories about the bandwidth of kernel density estimation have been well established by many statisticians. However, the influence function of the bandwidth has not been well investigated. The influence function of the optimal bandwidth that minimizes the mean integrated square error is derived and the asymptotic property of the bandwidth selectors based on the influence function is provided. 相似文献