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Showing 6 results for Measurement Error
Hashem Mahmoudnejad, Mousa Golalizadeh, Volume 7, Issue 2 (3-2014)
Abstract
Although the measurement error exists in the most scientific experiments, in order to simplify the modeling, its presence is usually ignored in statistical studying. In this paper, various approaches on estimating the parameters of multilevel models in presence of measurement error are studied. In addition, to improve the parameter estimates in this case, a new method is proposed which has high precision and reasonable convergence rate in compare with previous common approaches. Also, the performance of the proposed method as well as usual approaches are evaluated and compared using simulation study and analyzing real data of the income-expenditure of some households in Tehran city in 2008.
Roshanak Aliakbari Saba, Alireza Zahedian, Marzieh Arbabi, Volume 9, Issue 1 (9-2015)
Abstract
Annual estimation of average household incomes is one of the main goals of the household income and expenditure survey in Iran. So, regarding importance of accuracy of gathered data and reasons that lead to error in measuring household income, in this paper, model-based methods are used for estimating income measurement error and adjusting sample households declared income for 2011 household income and expenditure survey.
Hadi Emami, Parvaneh Mansoori, Volume 11, Issue 2 (3-2018)
Abstract
Semiparametric linear mixed measurement error models are extensions of linear mixed measurement error models to include a nonparametric function of some covariate. They have been found to be useful in both cross-sectional and longitudinal studies. In this paper first we propose a penalized corrected likelihood approach to estimate the parametric component in semiparametric linear mixed measurement error model and then using the case deletion and subject deletion analysis we survey the influence diagnostics in such models. Finally, the performance of our influence diagnostics methods are illustrated through a simulated example and a real data set.
Naghi Hemmati, Mousa Golalizadeh, Volume 12, Issue 1 (9-2018)
Abstract
According to multiple sources of errors, shape data are often prone to measurement error. Ignoring such error, if does exists, causes many problems including the biasedness of the estimators. The estimators coming from variables without including the measurement errors are called naive estimators. These for rotation and scale parameters are biased, while using the Procrustes matching for two dimensional shape data. To correct this and to improve the naive estimators, regression calibration methods that can be obtained through the complex regression models and invoking the complex normal distribution, as well as the conditional score are proposed in this paper. Moreover, their performance are studied in simulation studies. Also, the statistical shape analysis of the sand hills in Ardestan in Iran is undertaken in presence of measurement errors.
Vahid Tadayon, Abdolrahman Rasekh, Volume 13, Issue 1 (9-2019)
Abstract
Uncertainty is an inherent characteristic of biological and geospatial data which is almost made by measurement error in the observed values of the quantity of interest. Ignoring measurement error can lead to biased estimates and inflated variances and so an inappropriate inference. In this paper, the Gaussian spatial model is fitted based on covariate measurement error. For this purpose, we adopt the Bayesian approach and utilize the Markov chain Monte Carlo algorithms and data augmentations to carry out calculations. The methodology is illustrated using simulated data.
Mozhgan Moradi, Shaho Zarei, Volume 18, Issue 1 (8-2024)
Abstract
Model-based clustering is the most widely used statistical clustering method, in which heterogeneous data are divided into homogeneous groups using inference based on mixture models. The presence of measurement error in the data can reduce the quality of clustering and, for example, cause overfitting and produce spurious clusters. To solve this problem, model-based clustering assuming a normal distribution for measurement errors has been introduced. However, too large or too small (outlier) values of measurement errors cause poor performance of existing clustering methods. To tackle this problem {and build a stable model against the presence of outlier measurement errors in the data}, in this article, a symmetric $alpha$-stable distribution is proposed as a replacement for the normal distribution for measurement errors, and the model parameters are estimated using the EM algorithm and numerical methods. Through simulation and real data analysis, the new model is compared with the MCLUST-based model, considering cases with and without measurement errors, and the performance of the proposed model for data clustering in the presence of various outlier measurement errors is shown.
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