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61.
In this article, we consider a linear model in which the covariates are measured with errors. We propose a t-type corrected-loss estimation of the covariate effect, when the measurement error follows the Laplace distribution. The proposed estimator is asymptotically normal. In practical studies, some outliers that diminish the robustness of the estimation occur. Simulation studies show that the estimators are resistant to vertical outliers and an application of 6-minute walk test is presented to show that the proposed method performs well. 相似文献
62.
Ji-Ji Xing 《统计学通讯:理论与方法》2017,46(9):4545-4555
In this paper, we adopt the Bayesian approach to expectile regression employing a likelihood function that is based on an asymmetric normal distribution. We demonstrate that improper uniform priors for the unknown model parameters yield a proper joint posterior. Three simulated data sets were generated to evaluate the proposed method which show that Bayesian expectile regression performs well and has different characteristics comparing with Bayesian quantile regression. We also apply this approach into two real data analysis. 相似文献
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65.
Tahani Coolen-Maturi 《统计学通讯:理论与方法》2017,46(19):9476-9493
Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning, and credit scoring. The receiver operating characteristic (ROC) surface is a useful tool to assess the ability of a diagnostic test to discriminate among three-ordered classes or groups. In this article, nonparametric predictive inference (NPI) for three-group ROC analysis for ordinal outcomes is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modeling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. This article also includes results on the volumes under the ROC surfaces and consideration of the choice of decision thresholds for the diagnosis. Two examples are provided to illustrate our method. 相似文献
66.
Robust parameter designs (RPDs) enable the experimenter to discover how to modify the design of the product to minimize the effect due to variation from noise sources. The aim of this article is to show how this amount of work can be reduced under modified central composite design (MCCD). We propose a measure of extended scaled prediction variance (ESPV) for evaluation of RPDs on MCCD. Using these measures, we show that we can check the error or bias associated with estimating the model parameters and suggest the values of α recommended for MCCS under minimum ESPV. 相似文献
67.
ABSTRACTThis paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison with the optimal tests for Markov-switching parameters of Carrasco et al. (2014), and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of USA output growth. 相似文献
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69.
M-quantile models with application to poverty mapping 总被引:1,自引:0,他引:1
Nikos Tzavidis Nicola Salvati Monica Pratesi Ray Chambers 《Statistical Methods and Applications》2008,17(3):393-411
Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately,
cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation,
using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models
are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random
effects models that include random area effects to account for between area variations. However, such models also depend on
strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow
for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models
was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction
of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do
not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate
for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality.
The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation
of the distribution function of the study variable within the small area of interest both under an M-quantile and a random
effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small
area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the
2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of
poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption
expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are
in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of
inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology
and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile
estimation framework through design-based simulations based on two realistic survey data sets containing small area information
and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function. 相似文献
70.
We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data. 相似文献